sctransform/0000755000176200001440000000000015130402306012605 5ustar liggesuserssctransform/tests/0000755000176200001440000000000014731054755013770 5ustar liggesuserssctransform/tests/testthat/0000755000176200001440000000000015130402306015607 5ustar liggesuserssctransform/tests/testthat/test_vst.R0000644000176200001440000000637614731054755017642 0ustar liggesuserscontext("vst function") test_that('vst runs and returns expected output', { skip_on_cran() suppressWarnings(RNGversion(vstr = "3.5.0")) set.seed(42) vst_out <- vst(pbmc, return_gene_attr = TRUE, return_cell_attr = TRUE) expect_equal(c(910, 283), dim(vst_out$y)) ga <- vst_out$gene_attr[order(-vst_out$gene_attr$residual_variance), ] expect_equal(c("GNLY", "NKG7", "GZMB", "LYZ", "S100A9"), rownames(ga)[1:5]) expect_equal(c(27.8, 26.9, 18.7, 18.2, 16.8), ga$residual_variance[1:5], tolerance = 1e-01) }) test_that('vst runs with multicore futures', { skip_on_cran() suppressWarnings(RNGversion(vstr = "3.5.0")) set.seed(42) options(future.fork.enable = TRUE) options(future.globals.maxSize = 10 * 1024 ^ 3) future::plan(strategy = 'multicore', workers = 2) vst_out <- vst(pbmc, return_gene_attr = TRUE, return_cell_attr = TRUE) expect_equal(c(910, 283), dim(vst_out$y)) ga <- vst_out$gene_attr[order(-vst_out$gene_attr$residual_variance), ] expect_equal(c("GNLY", "NKG7", "GZMB", "LYZ", "S100A9"), rownames(ga)[1:5]) expect_equal(c(27.8, 26.9, 18.7, 18.2, 16.8), ga$residual_variance[1:5], tolerance = 1e-01) }) test_that('vst with batch variable works', { skip_on_cran() suppressWarnings(RNGversion(vstr = "3.5.0")) set.seed(42) ca <- data.frame(batch = sample(x = c('A', 'B'), size = ncol(pbmc), replace = TRUE)) rownames(ca) <- colnames(pbmc) vst_out <- vst(pbmc, batch_var = 'batch', cell_attr = ca, return_gene_attr = TRUE, return_cell_attr = TRUE) expect_equal(c(910, 283), dim(vst_out$y)) ga <- vst_out$gene_attr[order(-vst_out$gene_attr$residual_variance), ] expect_equal(c("GNLY", "NKG7", "GZMB", "LYZ", "S100A9"), rownames(ga)[1:5]) expect_equal(c(27.11, 26.11, 17.89, 17.66, 16), ga$residual_variance[1:5], tolerance = 1e-01) }) test_that('vst with pre-calculated cell attributes works', { skip_on_cran() suppressWarnings(RNGversion(vstr = "3.5.0")) ca <- data.frame(umi = colSums(pbmc)) set.seed(42) vst_out <- vst(pbmc, cell_attr = ca, latent_var = 'log_umi', return_gene_attr = TRUE, return_cell_attr = TRUE) expect_equal(c(910, 283), dim(vst_out$y)) ga <- vst_out$gene_attr[order(-vst_out$gene_attr$residual_variance), ] expect_equal(c("GNLY", "NKG7", "GZMB", "LYZ", "S100A9"), rownames(ga)[1:5]) expect_equal(c(27.8, 26.9, 18.7, 18.2, 16.8), ga$residual_variance[1:5], tolerance = 1e-01) ca <- data.frame(log_umi = log10(colSums(pbmc))) set.seed(42) vst_out <- vst(pbmc, cell_attr = ca, latent_var = 'umi', return_gene_attr = TRUE, return_cell_attr = TRUE) expect_equal(c(910, 283), dim(vst_out$y)) ga <- vst_out$gene_attr[order(-vst_out$gene_attr$residual_variance), ] expect_equal(c("GNLY", "NKG7", "LYZ", "S100A9", "GZMB"), rownames(ga)[1:5]) expect_equal(c(26.5, 20.5, 19.6, 16.6, 16.5), ga$residual_variance[1:5], tolerance = 1e-01) ca <- data.frame(log_umi = log10(colSums(pbmc))) set.seed(42) vst_out <- vst(pbmc, cell_attr = ca, latent_var = 'log_umi', return_gene_attr = TRUE, return_cell_attr = TRUE) expect_equal(c(910, 283), dim(vst_out$y)) ga <- vst_out$gene_attr[order(-vst_out$gene_attr$residual_variance), ] expect_equal(c("GNLY", "NKG7", "GZMB", "LYZ", "S100A9"), rownames(ga)[1:5]) expect_equal(c(27.8, 26.9, 18.7, 18.2, 16.8), ga$residual_variance[1:5], tolerance = 1e-01) }) sctransform/tests/testthat/test_differential_expression.R0000644000176200001440000000160414731054755023726 0ustar liggesuserscontext("differential expression") # test_that('compare expression runs and returns expected output', { # skip_on_cran() # options(mc.cores = 2) # set.seed(42) # vst_out <- vst(pbmc, return_cell_attr = TRUE) # # create fake clusters # clustering <- 1:ncol(pbmc) %/% 100 # res <- compare_expression(x = vst_out, umi = pbmc, group = clustering, val1 = 0, val2 = 3) # expect_equal(c("AKAP17A", "LRBA", "SEC23A", "RRP8", "TRNT1"), rownames(res)[1:5]) # expect_equal(c(-27.35713, -27.05464, -26.62938, -26.41430, -26.25116), res$log_fc[1:5], tolerance = 1e-05) # res <- compare_expression(x = vst_out, umi = pbmc, group = clustering, val1 = 0, val2 = 3, method = 't_test') # expect_equal(c("TMSB4X", "AKAP17A", "CALM3", "TOMM40", "HSPB11"), rownames(res)[1:5]) # expect_equal(c(-0.6481318, -0.5870122, -0.7482577, -0.5022045, -0.5954648), res$log_fc[1:5], tolerance = 1e-05) # }) sctransform/tests/testthat/test_utils.R0000644000176200001440000000566314731054755020164 0ustar liggesuserscontext("Rcpp utility functions") test_that('row_mean_grouped runs and returns expected output', { skip_on_cran() suppressWarnings(RNGversion(vstr = "3.5.0")) set.seed(42) grouping <- as.factor(sample(c('a','b','c'), size = ncol(pbmc), replace = TRUE)) means <- sctransform:::row_mean_grouped_dgcmatrix(matrix = pbmc, group = grouping, shuffle = FALSE) means_agg <- t(apply(pbmc, 1, function(x) { aggregate(x = x, by = list(group = grouping), FUN = mean)$x })) colnames(means_agg) <- levels(grouping) expect_equal(means, means_agg) gmeans <- sctransform:::row_gmean_grouped_dgcmatrix(matrix = pbmc, group = grouping, eps = 1, shuffle = FALSE) gmeans_agg <- sapply(levels(grouping), function(g) { sctransform:::row_gmean(pbmc[, grouping == g]) }) expect_equal(gmeans, gmeans_agg) # very sparse input matrix mat <- Matrix::rsparsematrix(100, 1000, density = 0.01) grouping <- as.factor(sample(c('a','b','c'), size = ncol(mat), replace = TRUE)) means <- sctransform:::row_mean_grouped_dgcmatrix(matrix = mat, group = grouping, shuffle = FALSE) means_agg <- t(apply(mat, 1, function(x) { aggregate(x = x, by = list(group = grouping), FUN = mean)$x })) colnames(means_agg) <- levels(grouping) expect_equal(means, means_agg) mat[mat < 0] <- 0 means <- sctransform:::row_gmean_grouped_dgcmatrix(matrix = mat, group = grouping, eps = 1, shuffle = FALSE) means_agg <- t(apply(mat, 1, function(x) { aggregate(x = x, by = list(group = grouping), FUN = function(y) { expm1(mean(log1p(y))) })$x })) colnames(means_agg) <- levels(grouping) expect_equal(means, means_agg) }) test_that('row_nonzero_count runs and returns expected output', { skip_on_cran() suppressWarnings(RNGversion(vstr = "3.5.0")) set.seed(42) nzc <- sctransform:::row_nonzero_count_dgcmatrix(pbmc) nzc2 <- Matrix::rowSums(pbmc > 0) expect_equal(nzc, nzc2) grouping <- as.factor(sample(c('a','b','c'), size = ncol(pbmc), replace = TRUE)) nzc <- sctransform:::row_nonzero_count_grouped_dgcmatrix(pbmc, grouping) f2 <- function(mat, grp) { ret <- sapply(levels(grp), function(g) { rowSums(mat[, grp == g, drop = FALSE] > 0) }) colnames(ret) <- levels(grp) ret } nzc2 <- f2(pbmc, grouping) expect_equal(nzc, nzc2) }) test_that('get_nz_median2 runs and returns expected output', { skip_on_cran() m <- Matrix(nrow = 3, ncol = 3, data = 0, sparse = TRUE) m[1,1] <- 1 m[2,2] <- 2 m[3,3] <- 3 mo <- sctransform:::get_nz_median2(m) expect_equal(mo, 2) }) test_that('get_nz_median2 runs as expected with genes', { skip_on_cran() m <- Matrix(nrow = 4, ncol = 4, data = 0, sparse = TRUE) m[1,1] <- 1 m[2,2] <- 2 m[3,3] <- 3 m[4,3] <- 2 m[4,4] <- 4 rownames(m) <- c("gene1", "gene2", "gene3", "gene4") mo <- sctransform:::get_nz_median2(m, c("gene4")) expect_equal(mo, 3) mo2 <- sctransform:::get_nz_median2(m, c("gene1","gene3")) expect_equal(mo2, 2) }) sctransform/tests/testthat/test_denoising.R0000644000176200001440000000141314731054755020770 0ustar liggesuserscontext("correcting") test_that('correcting runs and returns expected output', { skip_on_cran() suppressWarnings(RNGversion(vstr = "3.5.0")) set.seed(42) vst_out <- vst(pbmc, return_cell_attr = TRUE, res_clip_range = c(-Inf, Inf)) y_smooth <- smooth_via_pca(vst_out$y, do_plot = FALSE) expect_equal(c(910, 283), dim(y_smooth)) expect_equal(c(0.0868, 0.0380, 0.6062, 0.3123, 0.0101, 0.8751, -0.0557, -0.2222, -0.9911), as.numeric(y_smooth[1:3, 1:3]), tolerance = 1e-3) umi_corrected <- correct(vst_out) expect_equal(c(0, 1, 28, 1, 1, 37, 0, 0, 7), as.numeric(umi_corrected[1:3, 1:3])) umi_corrected <- correct(vst_out, data = y_smooth) expect_equal(c(0, 0, 31, 0, 0, 34, 0, 0, 8), as.numeric(umi_corrected[1:3, 1:3])) }) sctransform/tests/testthat/test_generate.R0000644000176200001440000000104414731060137020572 0ustar liggesuserscontext("generate function") test_that('generate runs and returns expected output', { skip_on_cran() suppressWarnings(RNGversion(vstr = "3.5.0")) set.seed(42) vst_out <- vst(pbmc, return_cell_attr = TRUE) generated_data <- generate(vst_out) expect_equal(c(1, 0, 0, 4, 1), generated_data['ERP29', 1:5]) genes <- sample(x = rownames(vst_out$model_pars_fit), size = 100) generated_data <- generate(vst_out = vst_out, genes = genes) expect_equal(c(100, 283), dim(generated_data)) expect_equal(genes, 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47a51cede5d9eb999d1040c9da1301c4 *tests/testthat.R a0c57f845f02156dafaec4e7f066aef1 *tests/testthat/test_denoising.R 5d16b85eb6ce0be774151373382ab900 *tests/testthat/test_differential_expression.R c06aca0c9172127c9d18436235659f25 *tests/testthat/test_generate.R 0f36b768ee3e24ca469b479f11febc2b *tests/testthat/test_utils.R c443815912b3d9b81d9b157610f56076 *tests/testthat/test_vst.R sctransform/R/0000755000176200001440000000000015123223603013011 5ustar liggesuserssctransform/R/RcppExports.R0000644000176200001440000000351115123223603015425 0ustar liggesusers# Generated by using Rcpp::compileAttributes() -> do not edit by hand # Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 row_mean_dgcmatrix <- function(matrix) { .Call('_sctransform_row_mean_dgcmatrix', PACKAGE = 'sctransform', matrix) } row_mean_grouped_dgcmatrix <- function(matrix, group, shuffle) { .Call('_sctransform_row_mean_grouped_dgcmatrix', PACKAGE = 'sctransform', matrix, group, shuffle) } row_gmean_dgcmatrix <- function(matrix, eps) { .Call('_sctransform_row_gmean_dgcmatrix', PACKAGE = 'sctransform', matrix, eps) } row_gmean_grouped_dgcmatrix <- function(matrix, group, eps, shuffle) { .Call('_sctransform_row_gmean_grouped_dgcmatrix', PACKAGE = 'sctransform', matrix, group, eps, shuffle) } row_nonzero_count_dgcmatrix <- function(matrix) { .Call('_sctransform_row_nonzero_count_dgcmatrix', PACKAGE = 'sctransform', matrix) } row_nonzero_count_grouped_dgcmatrix <- function(matrix, group) { .Call('_sctransform_row_nonzero_count_grouped_dgcmatrix', PACKAGE = 'sctransform', matrix, group) } row_var_dgcmatrix <- function(x, i, rows, cols) { .Call('_sctransform_row_var_dgcmatrix', PACKAGE = 'sctransform', x, i, rows, cols) } grouped_mean_diff_per_row <- function(x, group, shuffle) { .Call('_sctransform_grouped_mean_diff_per_row', PACKAGE = 'sctransform', x, group, shuffle) } mean_boot <- function(x, N, S) { .Call('_sctransform_mean_boot', PACKAGE = 'sctransform', x, N, S) } mean_boot_grouped <- function(x, group, N, S) { .Call('_sctransform_mean_boot_grouped', PACKAGE = 'sctransform', x, group, N, S) } distribution_shift <- function(x) { .Call('_sctransform_distribution_shift', PACKAGE = 'sctransform', x) } qpois_reg <- function(X, Y, tol, maxiters, minphi, returnfit) { .Call('_sctransform_qpois_reg', PACKAGE = 'sctransform', X, Y, tol, maxiters, minphi, returnfit) } sctransform/R/generate.R0000644000176200001440000000340614731054755014747 0ustar liggesusers#' Generate data from regularized models. #' #' Generate data from regularized models. This generates data from the background, #' i.e. no residuals are added to the simulated data. The cell attributes for the #' generated cells are sampled from the input with replacement. #' #' @param vst_out A list that provides model parameters and optionally meta data; use output of vst function #' @param genes The gene names for which to generate data; default is rownames(vst_out$model_pars_fit) #' @param cell_attr Provide cell meta data holding latent data info; default is vst_out$cell_attr #' @param n_cells Number of cells to generate; default is nrow(cell_attr) #' #' @return Generated data as dgCMatrix #' #' @importFrom methods as #' #' @export #' #' @examples #' \donttest{ #' vst_out <- vst(pbmc, return_cell_attr = TRUE) #' generated_data <- generate(vst_out) #' } #' generate <- function(vst_out, genes = rownames(vst_out$model_pars_fit), cell_attr = vst_out$cell_attr, n_cells = nrow(cell_attr)) { genes <- genes[genes %in% rownames(vst_out$model_pars_fit)] # get model parameters mp <- vst_out$model_pars_fit[genes, , drop = FALSE] coefs <- mp[, -1, drop=FALSE] theta <- mp[, 1] # we sample from the original list of cell attributes when we generate data # choose cells here idx <- sample(x = nrow(cell_attr), size = n_cells, replace = TRUE) regressor_data <- cbind(rep(1, length(idx)), cell_attr[idx, colnames(coefs)[-1]]) # calculate expected values mu <- exp(tcrossprod(coefs, regressor_data)) x.sim <- t(sapply(rownames(mu), function(gene) { gene.mu <- mu[gene, ] x <- MASS::rnegbin(n = length(gene.mu), mu = gene.mu, theta = theta[gene]) return(x) })) x.sim <- make.sparse(mat = x.sim) return(x.sim) } sctransform/R/vst.R0000644000176200001440000012741515123222545013766 0ustar liggesusers#' @useDynLib sctransform NULL #' Variance stabilizing transformation for UMI count data #' #' Apply variance stabilizing transformation to UMI count data using a regularized Negative Binomial regression model. #' This will remove unwanted effects from UMI data and return Pearson residuals. #' Uses future_lapply; you can set the number of cores it will use to n with plan(strategy = "multicore", workers = n). #' If n_genes is set, only a (somewhat-random) subset of genes is used for estimating the #' initial model parameters. For details see \doi{10.1186/s13059-019-1874-1}. #' #' @param umi A matrix of UMI counts with genes as rows and cells as columns #' @param cell_attr A data frame containing the dependent variables; if omitted a data frame with umi and gene will be generated #' @param latent_var The independent variables to regress out as a character vector; must match column names in cell_attr; default is c("log_umi") #' @param batch_var The dependent variables indicating which batch a cell belongs to; no batch interaction terms used if omiited #' @param latent_var_nonreg The non-regularized dependent variables to regress out as a character vector; must match column names in cell_attr; default is NULL #' @param n_genes Number of genes to use when estimating parameters (default uses 2000 genes, set to NULL to use all genes) #' @param n_cells Number of cells to use when estimating parameters (default uses all cells) #' @param method Method to use for initial parameter estimation; one of 'poisson', 'qpoisson', 'nb_fast', 'nb', 'nb_theta_given', 'glmGamPoi', 'offset', 'offset_shared_theta_estimate', 'glmGamPoi_offset'; default is 'poisson' #' @param do_regularize Boolean that, if set to FALSE, will bypass parameter regularization and use all genes in first step (ignoring n_genes); default is FALSE #' @param theta_regularization Method to use to regularize theta; use 'log_theta' for the behavior prior to version 0.3; default is 'od_factor' #' @param res_clip_range Numeric of length two specifying the min and max values the results will be clipped to; default is c(-sqrt(ncol(umi)), sqrt(ncol(umi))) #' @param bin_size Number of genes to process simultaneously; this will determine how often the progress bars are updated and how much memory is being used; default is 500 #' @param min_cells Only use genes that have been detected in at least this many cells; default is 5 #' @param residual_type What type of residuals to return; can be 'pearson', 'deviance', or 'none'; default is 'pearson' #' @param return_cell_attr Make cell attributes part of the output; default is FALSE #' @param return_gene_attr Calculate gene attributes and make part of output; default is TRUE #' @param return_corrected_umi If set to TRUE output will contain corrected UMI matrix; see \code{correct} function #' @param min_variance Lower bound for the estimated variance for any gene in any cell when calculating pearson residual; one of 'umi_median', 'model_median', 'model_mean' or a #' numeric. default is -Inf. When set to 'umi_median' uses (median of non-zero UMIs / 5)^2 as the minimum variance so that a median UMI (often 1) #' results in a maximum pearson residual of 5. When set to 'model_median' or 'model_mean' uses the mean/median of the model estimated mu per gene as the minimum_variance.#' #' @param bw_adjust Kernel bandwidth adjustment factor used during regurlarization; factor will be applied to output of bw.SJ; default is 3 #' @param gmean_eps Small value added when calculating geometric mean of a gene to avoid log(0); default is 1 #' @param theta_estimation_fun Character string indicating which method to use to estimate theta (when method = poisson); default is 'theta.ml', but 'theta.mm' seems to be a good and fast alternative #' @param theta_given If method is set to nb_theta_given, this should be a named numeric vector of fixed theta values for the genes; if method is offset, this should be a single value; default is NULL #' @param exclude_poisson Exclude poisson genes (i.e. mu < 0.001 or mu > variance) from regularization; default is FALSE #' @param use_geometric_mean Use geometric mean instead of arithmetic mean for all calculations ; default is TRUE #' @param use_geometric_mean_offset Use geometric mean instead of arithmetic mean in the offset model; default is FALSE #' @param fix_intercept Fix intercept as defined in the offset model; default is FALSE #' @param fix_slope Fix slope to log(10) (equivalent to using library size as an offset); default is FALSE #' @param scale_factor Replace all values of UMI in the regression model by this value instead of the median UMI; default is NA #' @param vst.flavor When set to `v2` sets method = glmGamPoi_offset, n_cells=2000, and exclude_poisson = TRUE which causes the model to learn theta and intercept only besides excluding poisson genes from learning and regularization; default is NULL which uses the original sctransform model #' @param verbosity An integer specifying the verbosity level: 0 (silent, no messages), 1 (show messages only), or 2 (show messages and progress bars); default is 2 #' #' @return A list with components #' \item{y}{Matrix of transformed data, i.e. Pearson residuals, or deviance residuals; empty if \code{residual_type = 'none'}} #' \item{umi_corrected}{Matrix of corrected UMI counts (optional)} #' \item{model_str}{Character representation of the model formula} #' \item{model_pars}{Matrix of estimated model parameters per gene (theta and regression coefficients)} #' \item{model_pars_outliers}{Vector indicating whether a gene was considered to be an outlier} #' \item{model_pars_fit}{Matrix of fitted / regularized model parameters} #' \item{model_str_nonreg}{Character representation of model for non-regularized variables} #' \item{model_pars_nonreg}{Model parameters for non-regularized variables} #' \item{genes_log_gmean_step1}{log-geometric mean of genes used in initial step of parameter estimation} #' \item{cells_step1}{Cells used in initial step of parameter estimation} #' \item{arguments}{List of function call arguments} #' \item{cell_attr}{Data frame of cell meta data (optional)} #' \item{gene_attr}{Data frame with gene attributes such as mean, detection rate, etc. (optional)} #' \item{times}{Time stamps at various points in the function} #' #' @section Details: #' In the first step of the algorithm, per-gene glm model parameters are learned. This step can be done #' on a subset of genes and/or cells to speed things up. #' If \code{method} is set to 'poisson', a poisson regression is done and #' the negative binomial theta parameter is estimated using the response residuals in #' \code{theta_estimation_fun}. #' If \code{method} is set to 'qpoisson', coefficients and overdispersion (phi) are estimated by quasi #' poisson regression and theta is estimated based on phi and the mean fitted value - this is currently #' the fastest method with results very similar to 'glmGamPoi' #' If \code{method} is set to 'nb_fast', coefficients and theta are estimated as in the #' 'poisson' method, but coefficients are then re-estimated using a proper negative binomial #' model in a second call to glm with \code{family = MASS::negative.binomial(theta = theta)}. #' If \code{method} is set to 'nb', coefficients and theta are estimated by a single call to #' \code{MASS::glm.nb}. #' If \code{method} is set to 'glmGamPoi', coefficients and theta are estimated by a single call to #' \code{glmGamPoi::glm_gp}. #' #' A special case is \code{method = 'offset'}. Here no regression parameters are learned, but #' instead an offset model is assumed. The latent variable is set to log_umi and a fixed #' slope of log(10) is used (offset). The intercept is given by log(gene_mean) - log(avg_cell_umi). #' See Lause et al. \doi{10.1186/s13059-021-02451-7} for details. #' Theta is set #' to 100 by default, but can be changed using the \code{theta_given} parameter (single numeric value). #' If the offset method is used, the following parameters are overwritten: #' \code{cell_attr <- NULL, latent_var <- c('log_umi'), batch_var <- NULL, latent_var_nonreg <- NULL, #' n_genes <- NULL, n_cells <- NULL, do_regularize <- FALSE}. Further, \code{method = 'offset_shared_theta_estimate'} #' exists where the 250 most highly expressed genes with detection rate of at least 0.5 are used #' to estimate a theta that is then shared across all genes. Thetas are estimated per individual gene #' using 5000 randomly selected cells. The final theta used for all genes is then the average. #' #' #' @import Matrix #' @importFrom future.apply future_lapply #' @importFrom future nbrOfWorkers #' @importFrom MASS theta.ml theta.mm glm.nb negative.binomial #' @importFrom stats glm glm.fit df.residual ksmooth model.matrix as.formula approx density poisson var bw.SJ #' @importFrom utils txtProgressBar setTxtProgressBar capture.output #' @importFrom methods as #' @importFrom utils packageVersion #' #' @export #' #' @examples #' \donttest{ #' vst_out <- vst(pbmc) #' } #' vst <- function(umi, cell_attr = NULL, latent_var = c('log_umi'), batch_var = NULL, latent_var_nonreg = NULL, n_genes = 2000, n_cells = NULL, method = 'poisson', do_regularize = TRUE, theta_regularization = 'od_factor', res_clip_range = c(-sqrt(ncol(umi)), sqrt(ncol(umi))), bin_size = 500, min_cells = 5, residual_type = 'pearson', return_cell_attr = FALSE, return_gene_attr = TRUE, return_corrected_umi = FALSE, min_variance = -Inf, bw_adjust = 3, gmean_eps = 1, theta_estimation_fun = 'theta.ml', theta_given = NULL, exclude_poisson = FALSE, use_geometric_mean = TRUE, use_geometric_mean_offset = FALSE, fix_intercept = FALSE, fix_slope = FALSE, scale_factor = NA, vst.flavor = NULL, verbosity = 2) { if (!is.null(vst.flavor)){ if (vst.flavor == "v2"){ if (verbosity > 0){ message("vst.flavor='v2' set. Using model with fixed slope and excluding poisson genes.") } glmGamPoi_check <- requireNamespace("glmGamPoi", quietly = TRUE) method <- "glmGamPoi_offset" if (!glmGamPoi_check){ message("`vst.flavor` is set to 'v2' but could not find glmGamPoi installed.\n", "Please install the glmGamPoi package for much faster estimation.\n", "--------------------------------------------\n", "install.packages('BiocManager')\n", "BiocManager::install('glmGamPoi')\n", "--------------------------------------------\n", "Falling back to native (slower) implementation.\n") method <- "nb_offset" } exclude_poisson <- TRUE if (min_variance == -Inf) min_variance <- 'umi_median' if (is.null(n_cells)) n_cells <- 2000 } } arguments <- as.list(environment()) arguments <- arguments[!names(arguments) %in% c("umi", "cell_attr")] # Check for suggested package if (method %in% c("glmGamPoi", "glmGamPoi_offset")) { glmGamPoi_check <- requireNamespace("glmGamPoi", quietly = TRUE) if (!glmGamPoi_check){ stop('Please install the glmGamPoi package. See https://github.com/const-ae/glmGamPoi for details.') } } # Special case offset model - override most parameters if (startsWith(x = method, prefix = 'offset')) { cell_attr <- NULL latent_var <- c('log_umi') batch_var <- NULL latent_var_nonreg <- NULL n_genes <- NULL n_cells <- NULL do_regularize <- FALSE if (is.null(theta_given)) { theta_given <- 100 } else { theta_given <- theta_given[1] } } times <- list(start_time = Sys.time()) cell_attr <- make_cell_attr(umi, cell_attr, latent_var, batch_var, latent_var_nonreg, verbosity) if (!is.null(batch_var)) { cell_attr[, batch_var] <- as.factor(cell_attr[, batch_var]) batch_levels <- levels(cell_attr[, batch_var]) } # we will generate output for all genes detected in at least min_cells cells # but for the first step of parameter estimation we might use only a subset of genes genes_cell_count <- rowSums(umi >= 0.01) genes <- rownames(umi)[genes_cell_count >= min_cells] umi <- umi[genes, ] if (use_geometric_mean){ genes_log_gmean <- log10(row_gmean(umi, eps = gmean_eps)) } else { genes_log_gmean <- log10(rowMeans(umi)) } if (!do_regularize && !is.null(n_genes)) { if (verbosity > 0) { message('do_regularize is set to FALSE, will use all genes') } n_genes <- NULL } if (!is.null(n_cells) && n_cells < ncol(umi)) { # downsample cells to speed up the first step cells_step1 <- sample(x = colnames(umi), size = n_cells) if (!is.null(batch_var)) { dropped_batch_levels <- setdiff(batch_levels, levels(droplevels(cell_attr[cells_step1, batch_var]))) if (length(dropped_batch_levels) > 0) { stop('Dropped batch levels ', dropped_batch_levels, ', set n_cells higher') } } genes_cell_count_step1 <- rowSums(umi[, cells_step1] > 0) genes_step1 <- rownames(umi)[genes_cell_count_step1 >= min_cells] if (use_geometric_mean){ genes_log_gmean_step1 <- log10(row_gmean(umi[genes_step1, ], eps = gmean_eps)) } else { genes_log_gmean_step1 <- log10(rowMeans(umi[genes_step1, ])) } } else { cells_step1 <- colnames(umi) genes_step1 <- genes genes_log_gmean_step1 <- genes_log_gmean } genes_amean <- NULL genes_var <- NULL # Exclude known poisson genes from the learning step if (do_regularize && exclude_poisson){ genes_amean <- rowMeans(umi) genes_var <- row_var(umi) overdispersion_factor <- genes_var - genes_amean overdispersion_factor_step1 <- overdispersion_factor[genes_step1] is_overdispersed <- (overdispersion_factor_step1 > 0) if (verbosity > 1) { message(paste("Total Step 1 genes:", length(genes_step1))) message(paste("Total overdispersed genes:", sum(is_overdispersed))) message(paste("Excluding", length(genes_step1) - sum(is_overdispersed), "genes from Step 1 because they are not overdispersed.")) } genes_step1 <- genes_step1[is_overdispersed] genes_log_gmean_step1 <- genes_log_gmean[genes_step1] } data_step1 <- cell_attr[cells_step1, , drop = FALSE] if (!is.null(n_genes) && n_genes < length(genes_step1)) { # density-sample genes to speed up the first step log_gmean_dens <- density(x = genes_log_gmean_step1, bw = 'nrd', adjust = 1) sampling_prob <- 1 / (approx(x = log_gmean_dens$x, y = log_gmean_dens$y, xout = genes_log_gmean_step1)$y + .Machine$double.eps) genes_step1 <- sample(x = genes_step1, size = n_genes, prob = sampling_prob) if (use_geometric_mean){ genes_log_gmean_step1 <- log10(row_gmean(umi[genes_step1, ], eps = gmean_eps)) } else { genes_log_gmean_step1 <- log10(rowMeans(umi[genes_step1, ])) } } if (!is.null(batch_var)) { model_str <- paste0('y ~ (', paste(latent_var, collapse = ' + '), ') : ', batch_var, ' + ', batch_var, ' + 0') } else { model_str <- paste0('y ~ ', paste(latent_var, collapse = ' + ')) } bin_ind <- ceiling(x = 1:length(x = genes_step1) / bin_size) max_bin <- max(bin_ind) if (verbosity > 0) { message('Variance stabilizing transformation of count matrix of size ', nrow(umi), ' by ', ncol(umi)) message('Model formula is ', model_str) } times$get_model_pars = Sys.time() model_pars <- get_model_pars(genes_step1, bin_size, umi, model_str, cells_step1, method, data_step1, theta_given, theta_estimation_fun, exclude_poisson, fix_intercept, fix_slope, use_geometric_mean, use_geometric_mean_offset, verbosity) # make sure theta is not too small min_theta <- 1e-7 if (any(model_pars[, 'theta'] < min_theta)) { if (verbosity > 0) { msg <- sprintf('There are %d estimated thetas smaller than %g - will be set to %g', sum(model_pars[, 'theta'] < min_theta), min_theta, min_theta) message(msg) } model_pars[, 'theta'] <- pmax(model_pars[, 'theta'], min_theta) } times$reg_model_pars = Sys.time() if (do_regularize) { model_pars_fit <- reg_model_pars(model_pars, genes_log_gmean_step1, genes_log_gmean, cell_attr, batch_var, cells_step1, genes_step1, umi, bw_adjust, gmean_eps, theta_regularization, genes_amean, genes_var, exclude_poisson, fix_intercept, fix_slope, use_geometric_mean, use_geometric_mean_offset, verbosity) model_pars_outliers <- attr(model_pars_fit, 'outliers') } else { model_pars_fit <- model_pars model_pars_outliers <- rep(FALSE, nrow(model_pars)) } # use all fitted values in NB model regressor_data <- model.matrix(get_model_formula(model_str), cell_attr) if (!is.null(latent_var_nonreg)) { if (verbosity > 0) { message('Estimating parameters for following non-regularized variables: ', latent_var_nonreg) } if (!is.null(batch_var)) { model_str_nonreg <- paste0('y ~ (', paste(latent_var_nonreg, collapse = ' + '), ') : ', batch_var, ' + ', batch_var, ' + 0') } else { model_str_nonreg <- paste0('y ~ ', paste(latent_var_nonreg, collapse = ' + ')) } times$get_model_pars_nonreg = Sys.time() model_pars_nonreg <- get_model_pars_nonreg(genes, bin_size, model_pars_fit, regressor_data, umi, model_str_nonreg, cell_attr, verbosity) regressor_data_nonreg <- model.matrix(get_model_formula(model_str_nonreg), cell_attr) model_pars_final <- cbind(model_pars_fit, model_pars_nonreg) regressor_data_final <- cbind(regressor_data, regressor_data_nonreg) #model_pars_final[, '(Intercept)'] <- model_pars_final[, '(Intercept)'] + model_pars_nonreg[, '(Intercept)'] #model_pars_final <- cbind(model_pars_final, model_pars_nonreg[, -1, drop=FALSE]) # model_str <- paste0(model_str, gsub('^y ~ 1', '', model_str2)) } else { model_str_nonreg <- '' model_pars_nonreg <- c() model_pars_final <- model_pars_fit regressor_data_final <- regressor_data } times$get_residuals = Sys.time() if (!residual_type == 'none') { # min_variance estimated using median umi if (min_variance == "umi_median"){ # Maximum pearson residual for non-zero median UMI is 5 min_var <- (get_nz_median2(umi) / 5)^2 if (verbosity > 1) { message(paste("Setting min_variance based on median UMI: ", min_var)) } arguments$set_min_var <- min_var } else { min_var <- min_variance } if (verbosity > 0) { message('Second step: Get residuals using fitted parameters for ', length(x = genes), ' genes') } bin_ind <- ceiling(x = 1:length(x = genes) / bin_size) max_bin <- max(bin_ind) if (verbosity > 1) { pb <- txtProgressBar(min = 0, max = max_bin, style = 3) } # browser() res <- matrix(NA_real_, length(genes), nrow(regressor_data_final), dimnames = list(genes, rownames(regressor_data_final))) for (i in 1:max_bin) { genes_bin <- genes[bin_ind == i] mu <- exp(tcrossprod(model_pars_final[genes_bin, -1, drop=FALSE], regressor_data_final)) y <- as.matrix(umi[genes_bin, , drop=FALSE]) if (min_variance == "model_mean") { mu_mean_var <- matrixStats::rowMeans2(mu) res[genes_bin, ] <- switch(residual_type, 'pearson' = pearson_residual2(y, mu, model_pars_final[genes_bin, 'theta'], min_vars = mu_mean_var), 'deviance' = deviance_residual(y, mu, model_pars_final[genes_bin, 'theta']), stop('residual_type ', residual_type, ' unknown - only pearson and deviance supported at the moment') ) } else if (min_variance == "model_median") { mu_median_var <- matrixStats::rowMedians(mu) res[genes_bin, ] <- switch(residual_type, 'pearson' = pearson_residual2(y, mu, model_pars_final[genes_bin, 'theta'], min_vars = mu_median_var), 'deviance' = deviance_residual(y, mu, model_pars_final[genes_bin, 'theta']), stop('residual_type ', residual_type, ' unknown - only pearson and deviance supported at the moment')) } else { res[genes_bin, ] <- switch(residual_type, 'pearson' = pearson_residual(y, mu, model_pars_final[genes_bin, 'theta'], min_var = min_var), 'deviance' = deviance_residual(y, mu, model_pars_final[genes_bin, 'theta']), stop('residual_type ', residual_type, ' unknown - only pearson and deviance supported at the moment') ) } if (verbosity > 1) { setTxtProgressBar(pb, i) } } if (verbosity > 1) { close(pb) } } else { if (verbosity > 0) { message('Skip calculation of full residual matrix') } res <- matrix(data = NA, nrow = 0, ncol = 0) } rv <- list(y = res, model_str = model_str, model_pars = model_pars, model_pars_outliers = model_pars_outliers, model_pars_fit = model_pars_fit, model_str_nonreg = model_str_nonreg, model_pars_nonreg = model_pars_nonreg, arguments = arguments, genes_log_gmean_step1 = genes_log_gmean_step1, cells_step1 = cells_step1, cell_attr = cell_attr) rm(res) gc(verbose = FALSE) times$correct_umi = Sys.time() if (return_corrected_umi) { if (residual_type != 'pearson') { message("Will not return corrected UMI because residual type is not set to 'pearson'") } else { rv$umi_corrected <- sctransform::correct(rv, do_round = TRUE, do_pos = TRUE, scale_factor = scale_factor, verbosity = verbosity) rv$umi_corrected <- make.sparse(mat = rv$umi_corrected) } } rv$y <- clip_matrix_values(rv$y, res_clip_range) if (!return_cell_attr) { rv[['cell_attr']] <- NULL } times$get_gene_attr = Sys.time() if (return_gene_attr) { if (verbosity > 0) { message('Calculating gene attributes') } gene_attr <- data.frame( detection_rate = genes_cell_count[genes] / ncol(umi), gmean = 10 ^ genes_log_gmean, amean = rowMeans(umi), variance = row_var(umi)) if (ncol(rv$y) > 0) { gene_attr$residual_mean = rowMeans(rv$y) gene_attr$residual_variance = row_var(rv$y) } rv[['gene_attr']] <- gene_attr } if (verbosity > 0) { message('Wall clock passed: ', capture.output(print(Sys.time() - times$start_time))) } times$done = Sys.time() rv$times <- times return(rv) } get_model_pars <- function(genes_step1, bin_size, umi, model_str, cells_step1, method, data_step1, theta_given, theta_estimation_fun, exclude_poisson = FALSE, fix_intercept = FALSE, fix_slope = FALSE, use_geometric_mean = TRUE, use_geometric_mean_offset = FALSE, verbosity = 0) { if (fix_slope | fix_intercept) { gene_mean <- rowMeans(umi) mean_cell_sum <- mean(colSums(umi)) model_pars_fixed <- cbind(rep(NA, length(genes_step1)), log(gene_mean)[genes_step1] - log(mean_cell_sum), rep(log(10), length(genes_step1))) if (use_geometric_mean_offset){ gene_gmean <- row_gmean(umi) model_pars_fixed <- cbind(rep(NA, length(genes_step1)), log(gene_gmean)[genes_step1] - log(mean_cell_sum), rep(log(10), length(genes_step1))) } dimnames(model_pars_fixed) <- list(genes_step1, c('theta', '(Intercept)', 'log_umi')) regressor_data <- model.matrix(get_model_formula(model_str), data_step1[cells_step1, ]) y_fixed <- as.matrix(umi[genes_step1, cells_step1]) mu_fixed <- exp(tcrossprod(model_pars_fixed[genes_step1, -1, drop=FALSE], regressor_data)) } # Special case offset model with one theta for all genes if (startsWith(x = method, prefix = 'offset')) { gene_mean <- rowMeans(umi) mean_cell_sum <- mean(colSums(umi)) model_pars <- cbind(rep(theta_given, nrow(umi)), log(gene_mean) - log(mean_cell_sum), rep(log(10), nrow(umi))) dimnames(model_pars) <- list(rownames(umi), c('theta', '(Intercept)', 'log_umi')) if (method == 'offset_shared_theta_estimate') { # use all genes with detection rate > 0.5 to estimate theta # if there are more, use the 250 most highly expressed ones # use at most 5000 cells (random sample) use_genes <- rowMeans(umi > 0) > 0.5 if (sum(use_genes) > 250) { o <- order(-row_gmean(umi[use_genes, ])) use_genes <- which(use_genes)[o[1:250]] } use_cells <- sample(x = ncol(umi), size = min(ncol(umi), 5000), replace = FALSE) if (verbosity > 0) { message(sprintf('Estimate shared theta for offset model using %d genes, %d cells', length(x = use_genes), length(x = use_cells))) } y <- as.matrix(umi[use_genes, use_cells]) regressor_data <- model.matrix(get_model_formula(model_str), data_step1[use_cells, ]) mu <- exp(tcrossprod(model_pars[use_genes, -1, drop=FALSE], regressor_data)) if (requireNamespace("glmGamPoi", quietly = TRUE) && getNamespaceVersion('glmGamPoi') >= '1.2') { theta <- 1 / glmGamPoi::overdispersion_mle(y = y, mean = mu)$estimate theta <- theta[is.finite(theta)] } else { theta <- sapply(1:nrow(y), function(i) { as.numeric(MASS::theta.ml(y = y[i, ], mu = mu[i, ], limit = 100)) }) } model_pars[, 'theta'] <- mean(theta) } else if (method == 'offset_allshared_theta_estimate') { # use all genes with detection rate > 0.5 to estimate theta use_genes <- rowMeans(umi > 0) > 0.5 use_cells <- sample(x = ncol(umi), size = min(ncol(umi), 5000), replace = FALSE) if (verbosity > 0) { message(sprintf('Estimate shared theta for offset model using %d genes, %d cells', length(x = use_genes), length(x = use_cells))) } y <- as.matrix(umi[use_genes, use_cells]) regressor_data <- model.matrix(get_model_formula(model_str), data_step1[use_cells, ]) mu <- exp(tcrossprod(model_pars[use_genes, -1, drop=FALSE], regressor_data)) if (requireNamespace("glmGamPoi", quietly = TRUE) && getNamespaceVersion('glmGamPoi') >= '1.2') { theta <- 1 / glmGamPoi::overdispersion_mle(y = y, mean = mu)$estimate theta <- theta[is.finite(theta)] } else { theta <- sapply(1:nrow(y), function(i) { as.numeric(suppressWarnings(MASS::theta.ml(y = y[i, ], mu = mu[i, ], limit = 100))) }) } model_pars[, 'theta'] <- mean(theta) } return(model_pars) } bin_ind <- ceiling(x = 1:length(x = genes_step1) / bin_size) max_bin <- max(bin_ind) if (verbosity > 0) { message('Get Negative Binomial regression parameters per gene') message('Using ', length(x = genes_step1), ' genes, ', length(x = cells_step1), ' cells') } if (verbosity > 1) { pb <- txtProgressBar(min = 0, max = max_bin, style = 3) } model_pars <- list() for (i in 1:max_bin) { genes_bin_regress <- genes_step1[bin_ind == i] umi_bin <- as.matrix(umi[genes_bin_regress, cells_step1, drop=FALSE]) if (fix_slope | fix_intercept) { mu_bin <- as.matrix(mu_fixed[genes_bin_regress, cells_step1, drop=FALSE]) model_pars_bin <- model_pars_fixed[genes_bin_regress, ] intercept_bin <- model_pars_bin[, "(Intercept)"] slope_bin <- model_pars_bin[, "log_umi"] } if (!is.null(theta_given)) { theta_given_bin <- theta_given[genes_bin_regress] } # umi_bin is a matrix of counts - we want a model per row # if there are multiple workers, split up the matrix in chunks of n rows # where n is the number of workers n_workers <- 1 if (parallelly::supportsMulticore()) { n_workers <- future::nbrOfWorkers() } genes_per_worker <- nrow(umi_bin) / n_workers + .Machine$double.eps index_vec <- 1:nrow(umi_bin) index_lst <- split(index_vec, ceiling(index_vec/genes_per_worker)) # the index list will have at most n_workers entries, each one defining which genes to work on par_lst <- future_lapply( X = index_lst, FUN = function(indices) { umi_bin_worker <- umi_bin[indices, , drop = FALSE] if (fix_intercept | fix_slope){ mu_bin_worker <- mu_bin[indices, , drop = FALSE] model_pars_bin_worker <- model_pars_bin[indices, , drop = FALSE] intercept_bin_worker <- model_pars_bin_worker[, "(Intercept)"] slope_bin_worker <- model_pars_bin_worker[, "log_umi"] } if (method == 'poisson') { return(fit_poisson(umi = umi_bin_worker, model_str = model_str, data = data_step1, theta_estimation_fun = theta_estimation_fun)) } else if (method == 'qpoisson') { return(fit_qpoisson(umi = umi_bin_worker, model_str = model_str, data = data_step1)) } else if (method == 'nb_theta_given') { theta_given_bin_worker <- theta_given_bin[indices] return(fit_nb_theta_given(umi = umi_bin_worker, model_str = model_str, data = data_step1, theta_given = theta_given_bin_worker)) } else if (method == 'nb_fast') { return(fit_nb_fast(umi = umi_bin_worker, model_str = model_str, data = data_step1, theta_estimation_fun = theta_estimation_fun)) } else if (method == 'nb') { return(fit_nb(umi = umi_bin_worker, model_str = model_str, data = data_step1)) } else if (method == "glmGamPoi") { if (fix_slope | fix_intercept){ if (packageVersion("glmGamPoi")<"1.5.1"){ stop('Please install glmGamPoi >= 1.5.1 from https://github.com/const-ae/glmGamPoi') } return(fit_overdisp_mle(umi = umi_bin_worker, mu = mu_bin_worker, intercept = intercept_bin_worker, slope = slope_bin_worker)) } return(fit_glmGamPoi(umi = umi_bin_worker, model_str = model_str, data = data_step1, allow_inf_theta = exclude_poisson)) } else if (method == "glmGamPoi_offset") { return(fit_glmGamPoi_offset(umi = umi_bin_worker, model_str = model_str, data = data_step1, allow_inf_theta = exclude_poisson)) } else if (method == "nb_offset") { return(fit_nb_offset(umi = umi_bin_worker, model_str = model_str, data = data_step1, allow_inf_theta = exclude_poisson)) } }, future.seed = TRUE ) model_pars[[i]] <- do.call(rbind, par_lst) if (verbosity > 1) { setTxtProgressBar(pb, i) } } model_pars <- do.call(rbind, model_pars) if (verbosity > 1) { close(pb) } rownames(model_pars) <- genes_step1 colnames(model_pars)[1] <- 'theta' if (exclude_poisson){ genes_amean <- rowMeans(umi) genes_var <- row_var(umi) genes_amean_step1 <- genes_amean[genes_step1] genes_var_step1 <- genes_var[genes_step1] predicted_theta <- genes_amean_step1^2/(genes_var_step1-genes_amean_step1) actual_theta <- model_pars[genes_step1, "theta"] diff_theta <- predicted_theta/actual_theta model_pars <- cbind(model_pars, diff_theta) # if the naive and estimated MLE are 1000x apart, set theta estimate to Inf diff_theta_index <- rownames(model_pars[model_pars[genes_step1, "diff_theta"]< 1e-3,]) if (verbosity > 1){ message(paste("Setting estimate of ", length(diff_theta_index), "genes to inf as theta_mm/theta_mle < 1e-3")) } # Replace theta by infinity model_pars[diff_theta_index, 1] <- Inf # drop diff_theta column model_pars <- model_pars[, -dim(model_pars)[2]] } return(model_pars) } get_model_pars_nonreg <- function(genes, bin_size, model_pars_fit, regressor_data, umi, model_str_nonreg, cell_attr, verbosity) { bin_ind <- ceiling(x = 1:length(x = genes) / bin_size) max_bin <- max(bin_ind) if (verbosity > 1) { pb <- txtProgressBar(min = 0, max = max_bin, style = 3) } model_pars_nonreg <- list() for (i in 1:max_bin) { genes_bin <- genes[bin_ind == i] mu <- tcrossprod(model_pars_fit[genes_bin, -1, drop=FALSE], regressor_data) umi_bin <- as.matrix(umi[genes_bin, ]) model_pars_nonreg[[i]] <- do.call( rbind, future_lapply(X = genes_bin, FUN = function(gene) { fam <- negative.binomial(theta = model_pars_fit[gene, 'theta'], link = 'log') y <- umi_bin[gene, ] offs <- mu[gene, ] fit <- glm(as.formula(model_str_nonreg), data = cell_attr, family = fam, offset=offs) return(fit$coefficients)}, future.seed = TRUE)) if (verbosity > 1) { setTxtProgressBar(pb, i) } } if (verbosity > 1) { close(pb) } model_pars_nonreg <- do.call(rbind, model_pars_nonreg) rownames(model_pars_nonreg) <- genes return(model_pars_nonreg) } reg_model_pars <- function(model_pars, genes_log_gmean_step1, genes_log_gmean, cell_attr, batch_var, cells_step1, genes_step1, umi, bw_adjust, gmean_eps, theta_regularization, genes_amean = NULL, genes_var = NULL, exclude_poisson = FALSE, fix_intercept = FALSE, fix_slope = FALSE, use_geometric_mean = TRUE, use_geometric_mean_offset = FALSE, verbosity = 0) { genes <- names(genes_log_gmean) ## if a batch variable is set switch off excluding poisson genes if (!is.null(x = batch_var)) { exclude_poisson <- FALSE fix_slope <- FALSE } if (exclude_poisson | fix_slope | fix_intercept){ # exclude this from the fitting procedure entirely # at the regularization step if (is.null(genes_amean)) gene_amean <- rowMeans(umi) if (is.null(genes_var)) genes_var <- row_var(umi) genes_amean_step1 <- genes_amean[genes_step1] genes_var_step1 <- genes_var[genes_step1] overdispersion_factor <- genes_var - genes_amean overdispersion_factor_step1 <- overdispersion_factor[genes_step1] all_poisson_genes <- genes[overdispersion_factor<=0] # also set genes with mean < 1e-3 as poisson low_mean_genes <- genes[genes_amean<1e-3] all_poisson_genes <- union(all_poisson_genes, low_mean_genes) poisson_genes_step1 <- genes_step1[overdispersion_factor_step1<=0] if (verbosity > 1){ message(paste("# of step1 poisson genes (variance < mean):", length(poisson_genes_step1))) message(paste("# of low mean genes (mean < 0.001):", length(low_mean_genes))) } poisson_genes2 <- rownames(model_pars[!is.finite(model_pars[, 'theta']),]) poisson_genes3 <- intersect(low_mean_genes, genes_step1) poisson_genes_step1 <- union(union(poisson_genes_step1, poisson_genes2),poisson_genes3) overdispersed_genes_step1 <- setdiff(genes_step1, poisson_genes_step1) if (verbosity > 1){ message(paste("Total # of Step1 poisson genes (theta=Inf; variance < mean):", length(poisson_genes_step1))) message(paste("Total # of poisson genes (theta=Inf; variance < mean):", length(all_poisson_genes))) # call offset model message(paste("Calling offset model for all", length(all_poisson_genes), "poisson genes")) } # Call offset model with theta=inf # only the slope and intercept are used downstream mean_cell_sum <- mean(x = colSums(umi)) if ("log_umi" %in% colnames(x = model_pars)){ vst_out_offset <- cbind(rep(Inf, length(all_poisson_genes)), log(genes_amean[all_poisson_genes]) - log(mean_cell_sum), rep(log(10), length(all_poisson_genes) )) dimnames(vst_out_offset) <- list(all_poisson_genes, c('theta', '(Intercept)', 'log_umi')) } else { vst_out_offset <- cbind(rep(Inf, length(all_poisson_genes)), log(genes_amean[all_poisson_genes]) - log(mean_cell_sum)) dimnames(vst_out_offset) <- list(all_poisson_genes, c('theta', '(Intercept)')) } n_coefficients <- ncol(x = model_pars) if (n_coefficients > ncol(x = vst_out_offset)) { # set all other parameters to zero n_existing <- ncol(x = vst_out_offset) n_diff <- n_coefficients - ncol(x = vst_out_offset) vst_out_offset_remaining <- matrix(data = 0, nrow = nrow(x = vst_out_offset), ncol = n_diff, dimnames = list(all_poisson_genes, colnames(x = model_pars)[(n_existing+1):n_coefficients])) vst_out_offset <- cbind(vst_out_offset, vst_out_offset_remaining) } dispersion_par <- rep(0, dim(vst_out_offset)[1]) vst_out_offset <- cbind(vst_out_offset, dispersion_par) } # we don't regularize theta directly # prior to v0.3 we regularized log10(theta) # now we transform to overdispersion factor # variance of NB is mu * (1 + mu / theta) # (1 + mu / theta) is what we call overdispersion factor here dispersion_par <- switch(theta_regularization, 'log_theta' = log10(model_pars[, 'theta']), 'od_factor' = log10(1 + 10^genes_log_gmean_step1 / model_pars[, 'theta']), stop('theta_regularization ', theta_regularization, ' unknown - only log_theta and od_factor supported at the moment') ) model_pars_all <- model_pars model_pars <- model_pars[, colnames(model_pars) != 'theta'] model_pars <- cbind(dispersion_par, model_pars) # look for outliers in the parameters # outliers are those that do not fit the overall relationship with the mean at all outliers <- apply(model_pars, 2, function(y) is_outlier(y, genes_log_gmean_step1)) outliers <- apply(outliers, 1, any) # also call theta=inf as outliers if (exclude_poisson){ is_theta_inf <- !is.finite(model_pars_all[, "theta"]) outliers <- outliers | is_theta_inf } if (sum(outliers) > 0) { if (verbosity > 0) { message('Found ', sum(outliers), ' outliers - those will be ignored in fitting/regularization step\n') } model_pars <- model_pars[!outliers, ] genes_step1 <- rownames(model_pars) genes_log_gmean_step1 <- genes_log_gmean_step1[!outliers] } if (exclude_poisson){ if (verbosity > 1) { message('Ignoring theta inf genes') } overdispersed_genes <- setdiff(rownames(model_pars), all_poisson_genes) model_pars <- model_pars[overdispersed_genes, ] genes_step1 <- rownames(model_pars) genes_log_gmean_step1 <- genes_log_gmean_step1[overdispersed_genes] } # select bandwidth to be used for smoothing bw <- bw.SJ(genes_log_gmean_step1) * bw_adjust # for parameter predictions x_points <- pmax(genes_log_gmean, min(genes_log_gmean_step1)) x_points <- pmin(x_points, max(genes_log_gmean_step1)) # take results from step 1 and fit/predict parameters to all genes o <- order(x_points) model_pars_fit <- matrix(NA_real_, length(genes), ncol(model_pars), dimnames = list(genes, colnames(model_pars))) # fit / regularize dispersion parameter model_pars_fit[o, 'dispersion_par'] <- ksmooth(x = genes_log_gmean_step1, y = model_pars[, 'dispersion_par'], x.points = x_points, bandwidth = bw, kernel='normal')$y if (is.null(batch_var)){ # global fit / regularization for all coefficients for (i in 2:ncol(model_pars)) { model_pars_fit[o, i] <- ksmooth(x = genes_log_gmean_step1, y = model_pars[, i], x.points = x_points, bandwidth = bw, kernel='normal')$y } } else { # fit / regularize per batch batches <- unique(cell_attr[, batch_var]) for (b in batches) { sel <- cell_attr[, batch_var] == b & rownames(cell_attr) %in% cells_step1 #batch_genes_log_gmean_step1 <- log10(rowMeans(umi[genes_step1, sel])) if (use_geometric_mean){ batch_genes_log_gmean_step1 <- log10(row_gmean(umi[genes_step1, sel], eps = gmean_eps)) } else { batch_genes_log_gmean_step1 <- log10(rowMeans(umi[genes_step1, sel])) } if (any(is.infinite(batch_genes_log_gmean_step1))) { if (verbosity > 0) { message('Some genes not detected in batch ', b, ' -- assuming a low mean.') } batch_genes_log_gmean_step1[is.infinite(batch_genes_log_gmean_step1) & batch_genes_log_gmean_step1 < 0] <- min(batch_genes_log_gmean_step1[!is.infinite(batch_genes_log_gmean_step1)]) } sel <- cell_attr[, batch_var] == b #batch_genes_log_gmean <- log10(rowMeans(umi[, sel])) if (use_geometric_mean){ batch_genes_log_gmean <- log10(row_gmean(umi[, sel], eps = gmean_eps)) } else { batch_genes_log_gmean <- log10(rowMeans(umi[, sel])) } # in case some genes have not been observed in this batch batch_genes_log_gmean <- pmax(batch_genes_log_gmean, min(batch_genes_log_gmean_step1)) batch_o <- order(batch_genes_log_gmean) for (i in which(grepl(paste0(batch_var, b), colnames(model_pars)))) { model_pars_fit[batch_o, i] <- ksmooth(x = batch_genes_log_gmean_step1, y = model_pars[, i], x.points = batch_genes_log_gmean, bandwidth = bw, kernel='normal')$y } } } if (exclude_poisson){ dispersion_par <- switch(theta_regularization, 'log_theta' = rep(Inf, length(all_poisson_genes)), 'od_factor' = rep(0, length(all_poisson_genes)), stop('theta_regularization ', theta_regularization, ' unknown - only log_theta and od_factor supported at the moment') ) model_pars_fit[all_poisson_genes, "dispersion_par"] <- dispersion_par } # back-transform dispersion parameter to theta theta <- switch(theta_regularization, 'log_theta' = 10^model_pars_fit[, 'dispersion_par'], 'od_factor' = 10^genes_log_gmean / (10^model_pars_fit[, 'dispersion_par'] - 1) ) model_pars_fit <- model_pars_fit[, colnames(model_pars_fit) != 'dispersion_par'] model_pars_fit <- cbind(theta, model_pars_fit) all_genes <- rownames(model_pars_fit) # browser() if (exclude_poisson){ if (verbosity > 1) { message(paste('Replacing fit params for', length(all_poisson_genes), 'poisson genes by theta=Inf')) } for (col in intersect(colnames(x = model_pars_fit), colnames(x = vst_out_offset)) ){ stopifnot(col %in% colnames(vst_out_offset)) model_pars_fit[all_poisson_genes, col] <- vst_out_offset[all_poisson_genes, col] } } if (fix_intercept){ # Replace the fitted intercepts by those calculated from offset model col <- "(Intercept)" if (verbosity > 1) { message(paste0('Replacing regularized parameter ', col, ' by offset')) } gene_mean <- rowMeans(umi) mean_cell_sum <- mean(colSums(umi)) intercept_fixed <- log(gene_mean)[all_genes] - log(mean_cell_sum) if (use_geometric_mean_offset){ gene_gmean <- row_gmean(umi) intercept_fixed <- log(gene_gmean)[all_genes] - log(mean_cell_sum) } model_pars_fit[all_genes, col] <- intercept_fixed } if (fix_slope){ # Replace the fitted slope by those calculated from offset model col <- "log_umi" if (verbosity > 0) { message(paste0('Replacing regularized parameter ', col, ' by offset')) } model_pars_fit[all_genes, col] <- rep(log(10), length(all_genes)) } attr(model_pars_fit, 'outliers') <- outliers return(model_pars_fit) } sctransform/R/plotting.R0000644000176200001440000002466715123222545015017 0ustar liggesusers#' Plot estimated and fitted model parameters #' #' @param vst_out The output of a vst run #' @param xaxis Variable to plot on X axis; default is "gmean" #' @param show_theta Whether to show the theta parameter; default is FALSE (only the overdispersion factor is shown) #' @param show_var Whether to show the average model variance; default is FALSE #' @param verbosity An integer specifying the verbosity level: 0 (silent, no messages), 1 (show messages only), or 2 (show messages and progress bars); default is 2 #' #' @return A ggplot object #' #' @import ggplot2 #' @import reshape2 #' #' @export #' #' @examples #' \donttest{ #' vst_out <- vst(pbmc, return_gene_attr = TRUE) #' plot_model_pars(vst_out) #' } #' plot_model_pars <- function(vst_out, xaxis="gmean", show_theta = FALSE, show_var = FALSE, verbosity = 2) { if (! 'gmean' %in% names(vst_out$gene_attr)) { stop('vst_out must contain a data frame named gene_attr with a column named gmean (perhaps call vst with return_gene_attr = TRUE)') } # first handle the per-gene estimates mp <- get_model_par_mat(vst_out, model_pars = vst_out$model_pars, use_nonreg = TRUE, show_theta = show_theta, show_var = show_var, verbosity = verbosity) ordered_par_names <- colnames(mp) # second the regularized estimates mp_fit <- get_model_par_mat(vst_out, model_pars = vst_out$model_pars_fit, use_nonreg = FALSE, show_theta = show_theta, show_var = show_var, verbosity = verbosity) mpnr <- vst_out$model_pars_nonreg if (!is.null(dim(mpnr))) { colnames(mpnr) <- paste0('nonreg:', colnames(mpnr)) mp <- cbind(mp, mpnr) ordered_par_names <- c(ordered_par_names, colnames(mpnr)) } # show estimated and regularized parameters df <- melt(mp, varnames = c('gene', 'parameter'), as.is = TRUE) df$parameter <- factor(df$parameter, levels = ordered_par_names) df_fit <- melt(mp_fit, varnames = c('gene', 'parameter'), as.is = TRUE) df_fit$parameter <- factor(df_fit$parameter, levels = ordered_par_names) df$is_outl <- vst_out$model_pars_outliers df$type <- 'single gene estimate' df_fit$type <- 'regularized' df_fit$is_outl <- FALSE if (startsWith(x = vst_out$arguments$method, prefix = 'offset') | (xaxis=="amean")) { df$x <- vst_out$gene_attr[df$gene, 'amean'] df_fit$x <- vst_out$gene_attr[df_fit$gene, 'amean'] xlab <- 'Arithmetic mean of gene [log10]' } else { df$x <- vst_out$gene_attr[df$gene, 'gmean'] df_fit$x <- vst_out$gene_attr[df_fit$gene, 'gmean'] xlab <- 'Geometric mean of gene [log10]' } df_plot <- rbind(df, df_fit) df_plot$parameter <- factor(df_plot$parameter, levels = ordered_par_names) if (!vst_out$arguments$do_regularize || startsWith(x = vst_out$arguments$method, prefix = 'offset')) { df_plot <- df_plot[df_plot$type == 'single gene estimate', ] legend_pos <- 'none' } else { legend_pos <- 'bottom' } g <- ggplot(df_plot, aes_(x=~log10(x), y=~value, color=~type)) + geom_point(data=df, aes_(shape=~is_outl), size=0.5, alpha=0.5) + scale_shape_manual(values=c(16, 4), guide = FALSE) + geom_point(data=df_fit, size=0.66, alpha=0.5, shape=16) + facet_wrap(~ parameter, scales = 'free_y', ncol = ncol(mp)) + theme(legend.position = legend_pos) + xlab(label = xlab) return(g) } # helper function to plot model parameters get_model_par_mat <- function(vst_out, model_pars, use_nonreg, show_theta = FALSE, show_var = FALSE, verbosity = 2) { mp <- model_pars # transform theta to overdispersion factor if (startsWith(x = vst_out$arguments$method, prefix = 'offset')) { mp[, 1] <- log10(1 + vst_out$gene_attr[rownames(mp), 'amean'] / mp[, 'theta']) } else { mp[, 1] <- log10(1 + vst_out$gene_attr[rownames(mp), 'gmean'] / mp[, 'theta']) } colnames(mp)[1] <- 'log10(od_factor)' ordered_par_names <- colnames(mp)[c(2:ncol(mp), 1)] if (show_theta) { mp <- cbind(mp, log10(model_pars[, 'theta'])) colnames(mp)[ncol(mp)] <- 'log10(theta)' ordered_par_names <- c(ordered_par_names, 'log10(theta)') } if (show_var) { mp <- cbind(mp, log10(get_model_var(vst_out, use_nonreg = use_nonreg, verbosity = verbosity))) colnames(mp)[ncol(mp)] <- 'log10(model var)' ordered_par_names <- c(ordered_par_names, 'log10(model var)') } return(mp[, ordered_par_names]) } # helper function to plot model fit for a single gene # returns list with mean, sd, pearson residual #' @importFrom stats model.matrix get_nb_fit <- function(x, umi, gene, cell_attr, as_poisson = FALSE) { regressor_data <- model.matrix(as.formula(gsub('^y', '', x$model_str)), cell_attr) coefs <- x$model_pars_fit[gene, -1, drop=FALSE] theta <- x$model_pars_fit[gene, 1] if (as_poisson) { theta <- Inf } mu <- exp(coefs %*% t(regressor_data))[1, ] sd <- sqrt(mu + mu^2 / theta) res <- (umi[gene, ] - mu) / sd res <- pmin(res, x$arguments$res_clip_range[2]) res <- pmax(res, x$arguments$res_clip_range[1]) ret_df <- data.frame(mu = mu, sd = sd, res = res) # in case we have individual (non-regularized) parameters if (gene %in% rownames(x$model_pars)) { coefs <- x$model_pars[gene, -1, drop=FALSE] theta <- x$model_pars[gene, 1] ret_df$mu_nr <- exp(coefs %*% t(regressor_data))[1, ] ret_df$sd_nr <- sqrt(ret_df$mu_nr + ret_df$mu_nr^2 / theta) ret_df$res_nr <- (umi[gene, ] - ret_df$mu_nr) / ret_df$sd_nr ret_df$res_nr <- pmin(ret_df$res_nr, x$arguments$res_clip_range[2]) ret_df$res_nr <- pmax(ret_df$res_nr, x$arguments$res_clip_range[1]) } else { ret_df$mu_nr <- NA_real_ ret_df$sd_nr <- NA_real_ ret_df$res_nr <- NA_real_ } return(ret_df) } #' Plot observed UMI counts and model #' #' @param x The output of a vst run #' @param umi UMI count matrix #' @param goi Vector of genes to plot #' @param x_var Cell attribute to use on x axis; will be taken from x$arguments$latent_var[1] by default #' @param cell_attr Cell attributes data frame; will be taken from x$cell_attr by default #' @param do_log Log10 transform the UMI counts in plot #' @param show_fit Show the model fit #' @param show_nr Show the non-regularized model (if available) #' @param plot_residual Add panels for the Pearson residuals #' @param batches Manually specify a batch variable to break up the model plot in segments #' @param as_poisson Fix model parameter theta to Inf, effectively showing a Poisson model #' @param arrange_vertical Stack individual ggplot objects or place side by side #' @param show_density Draw 2D density lines over points #' @param gg_cmds Additional ggplot layer commands #' #' @return A ggplot object #' #' @import ggplot2 #' @import reshape2 #' @importFrom gridExtra grid.arrange #' @importFrom stats bw.nrd0 #' #' @export #' #' @examples #' \donttest{ #' vst_out <- vst(pbmc, return_cell_attr = TRUE) #' plot_model(vst_out, pbmc, 'EMC4') #' } #' plot_model <- function(x, umi, goi, x_var = x$arguments$latent_var[1], cell_attr = x$cell_attr, do_log = TRUE, show_fit = TRUE, show_nr = FALSE, plot_residual = FALSE, batches = NULL, as_poisson = FALSE, arrange_vertical = TRUE, show_density = FALSE, gg_cmds = NULL) { if (is.null(batches)) { if (!is.null(x$arguments$batch_var)) { batches <- cell_attr[, x$arguments$batch_var] } else { batches <- rep(1, nrow(cell_attr)) } } df_list <- list() for (gene in goi) { nb_fit <- get_nb_fit(x, umi, gene, cell_attr, as_poisson) nb_fit$x <- cell_attr[, x_var] nb_fit$y <- umi[gene, ] nb_fit$batch <- batches nb_fit$gene <- gene nb_fit$ymin <- nb_fit$mu - nb_fit$sd nb_fit$ymax <- nb_fit$mu + nb_fit$sd nb_fit$ymin_nr <- nb_fit$mu_nr - nb_fit$sd_nr nb_fit$ymax_nr <- nb_fit$mu_nr + nb_fit$sd_nr if (do_log) { nb_fit$y <- log10(nb_fit$y + 1) nb_fit$mu <- log10(nb_fit$mu + 1) nb_fit$mu_nr <- log10(nb_fit$mu_nr + 1) nb_fit$ymin <- log10(pmax(nb_fit$ymin, 0) + 1) nb_fit$ymax <- log10(pmax(nb_fit$ymax, 0) + 1) nb_fit$ymin_nr <- log10(pmax(nb_fit$ymin_nr, 0) + 1) nb_fit$ymax_nr <- log10(pmax(nb_fit$ymax_nr, 0) + 1) } df_list[[length(df_list) + 1]] <- nb_fit[order(nb_fit$x), ] } df <- do.call(rbind, df_list) df$gene <- factor(df$gene, ordered=TRUE, levels=unique(df$gene)) g <- ggplot(df, aes_(~x, ~y)) + geom_point(alpha=0.5, shape=16) if (show_density) { bandwidths <- c(bw.nrd0(g$data$x), bw.nrd0(g$data$y)) g <- g + geom_density_2d(color = 'lightblue', size=0.5, h = bandwidths, contour_var = "ndensity") } if (show_fit) { for (b in unique(df$batch)) { g <- g + geom_line(data = df[df$batch == b, ], aes_(~x, ~mu), color='deeppink', size = 1) + geom_ribbon(data = df[df$batch == b, ], aes_(x = ~x, ymin = ~ymin, ymax = ~ymax), alpha = 0.5, fill='deeppink') } } if (show_nr) { for (b in unique(df$batch)) { g <- g + geom_line(aes_(~x, ~mu_nr), color='blue', size = 1) + geom_ribbon(aes_(x = ~x, ymin = ~ymin_nr, ymax = ~ymax_nr), alpha = 0.5, fill='blue') } } g <- g + facet_grid(~gene) + xlab(paste('Cell', x_var)) + ylab('Gene UMI counts') if (do_log) { g <- g + ylab('Gene log10(UMI + 1)') } g <- g + gg_cmds if (length(goi) == 1) { g <- g + theme(strip.text = element_blank()) } if (plot_residual) { ga_col = 1 res_range <- range(df$res) g2 <- ggplot(df, aes_(~x, ~res)) + geom_point(alpha = 0.5, shape=16) + coord_cartesian(ylim = res_range) + facet_grid(~gene) + xlab(x) + ylab('Pearson residual') + xlab(paste('Cell', x_var)) + gg_cmds + theme(strip.text = element_blank()) # strip.background = element_blank(), if (show_density) { g2 <- g2 + geom_density_2d(color = 'lightblue', size=0.5) } if (show_nr) { g3 <- ggplot(df, aes_(~x, ~res_nr)) + geom_point(alpha = 0.5, shape=16) + coord_cartesian(ylim = res_range) + facet_grid(~gene) + xlab(x) + ylab('Pearson residual non-reg.') + xlab(paste('Cell', x_var)) + gg_cmds + theme(strip.text = element_blank()) # strip.background = element_blank(), if (show_density) { g3 <- g3 + geom_density_2d(color = 'lightblue', size=0.5) } if (!arrange_vertical) { ga_col = 3 } return(grid.arrange(g, g2, g3, ncol=ga_col)) } else { if (!arrange_vertical) { ga_col = 2 } return(grid.arrange(g, g2, ncol=ga_col)) } } return(g) } sctransform/R/denoise.R0000644000176200001440000002036415123222545014573 0ustar liggesusers #' Smooth data by PCA #' #' Perform PCA, identify significant dimensions, and reverse the rotation using only significant dimensions. #' #' @param x A data matrix with genes as rows and cells as columns #' @param elbow_th The fraction of PC sdev drop that is considered significant; low values will lead to more PCs being used #' @param dims_use Directly specify PCs to use, e.g. 1:10 #' @param max_pc Maximum number of PCs computed #' @param do_plot Plot PC sdev and sdev drop #' @param scale. Boolean indicating whether genes should be divided by standard deviation after centering and prior to PCA #' #' @return Smoothed data #' #' @importFrom graphics par plot abline #' #' @export #' #' @examples #' \donttest{ #' vst_out <- vst(pbmc) #' y_smooth <- smooth_via_pca(vst_out$y, do_plot = TRUE) #' } #' smooth_via_pca <- function(x, elbow_th = 0.025, dims_use = NULL, max_pc = 100, do_plot = FALSE, scale. = FALSE) { requireNamespace('irlba', quietly = TRUE) # perform pca if (scale.) { scale. <- apply(x, 1, 'sd') } else { scale. <- rep(1, nrow(x)) } pca <- irlba::prcomp_irlba(t(x), n = max_pc, center = TRUE, scale. = scale.) if (is.null(dims_use)) { pca_sdev_drop <- c(diff(pca$sdev), 0) / -pca$sdev max_dim <- rev(which(pca_sdev_drop > elbow_th))[1] dims_use <- 1:max_dim if (do_plot) { par(mfrow=c(1,2)) plot(pca$sdev) abline(v = max_dim + 0.5, col='red') plot(pca_sdev_drop) abline(h = elbow_th, col='red') abline(v = max_dim + 0.5, col='red') par(mfrow=c(1,1)) } } new_x <- pca$rotation[, dims_use] %*% t(pca$x[, dims_use]) * pca$scale + pca$center dimnames(new_x) <- dimnames(x) return(new_x) } #' Correct data by setting all latent factors to their median values and reversing the regression model #' #' @param x A list that provides model parameters and optionally meta data; use output of vst function #' @param data The name of the entry in x that holds the data #' @param cell_attr Provide cell meta data holding latent data info #' @param as_is Use cell attributes as is and do not use the median; set to TRUE if you want to #' manually control the values of the latent factors; default is FALSE #' @param do_round Round the result to integers #' @param do_pos Set negative values in the result to zero #' @param scale_factor Replace all values of UMI in the regression model by this value. Default is NA #' which uses median of total UMI as the latent factor. #' @param verbosity An integer specifying the verbosity level: 0 (silent, no messages), 1 (show messages only), or 2 (show messages and progress bars); default is 2 #' #' @return Corrected data as UMI counts #' #' @export #' #' @examples #' \donttest{ #' vst_out <- vst(pbmc, return_cell_attr = TRUE) #' umi_corrected <- correct(vst_out) #' } #' correct <- function(x, data = 'y', cell_attr = x$cell_attr, as_is = FALSE, do_round = TRUE, do_pos = TRUE, scale_factor=NA, verbosity = 2) { if (is.character(data)) { data <- x[[data]] } # when correcting, set all latent variables to median values if (!as_is) { latent_var <- x$arguments$latent_var column_types <- sapply(X = cell_attr[, latent_var, drop=FALSE], FUN = class) non_numeric <- names(x = column_types[column_types!="numeric"]) if (any(column_types != "numeric")) { stop(paste0( "Some columns in metadata are not numeric, cannot calculate median while correcting: ", paste0(non_numeric, collapse = ","))) } cell_attr[, latent_var] <- apply(cell_attr[, latent_var, drop=FALSE], 2, function(x) rep(median(x), length(x))) } if (!is.na(scale_factor) && !is.numeric(scale_factor)){ stop("`scale_factor` should be numeric") } if (!is.na(scale_factor)){ if (verbosity>0){ message(paste("Setting log_umi for correcting counts to", scale_factor)) } cell_attr[, "log_umi"] <- log10(scale_factor) } regressor_data <- model.matrix(get_model_formula(x$model_str), cell_attr) genes <- rownames(data) bin_size <- x$arguments$bin_size if (verbosity > 0) { message('Computing corrected count matrix for ', length(genes), ' genes') } pb_setup <- setup_progress_bar(length(genes), bin_size, verbosity) bin_ind <- pb_setup$bin_ind max_bin <- pb_setup$max_bin pb <- pb_setup$pb corrected_data <- matrix(NA_real_, length(genes), nrow(regressor_data), dimnames = list(genes, rownames(regressor_data))) for (i in 1:max_bin) { genes_bin <- genes[bin_ind == i] pearson_residual <- data[genes_bin, ] coefs <- x$model_pars_fit[genes_bin, -1, drop=FALSE] theta <- x$model_pars_fit[genes_bin, 1] mu <- exp(tcrossprod(coefs, regressor_data)) variance <- mu + mu^2 / theta corrected_data[genes_bin, ] <- mu + pearson_residual * sqrt(variance) update_progress_bar(pb, i, verbosity) } close_progress_bar(pb, verbosity) if (do_round) { corrected_data <- round(corrected_data, 0) } if (do_pos) { corrected_data[corrected_data < 0] <- 0 } return(corrected_data) } #' Correct data by setting all latent factors to their median values and reversing the regression model #' #' This version does not need a matrix of Pearson residuals. It takes the count matrix as input and #' calculates the residuals on the fly. The corrected UMI counts will be rounded to the nearest #' integer and negative values clipped to 0. #' #' @param x A list that provides model parameters and optionally meta data; use output of vst function #' @param umi The count matrix #' @param cell_attr Provide cell meta data holding latent data info #' @param scale_factor Replace all values of UMI in the regression model by this value. Default is NA #' which uses median of total UMI as the latent factor. #' @param verbosity An integer specifying the verbosity level: 0 (silent, no messages), 1 (show messages only), or 2 (show messages and progress bars); default is 2 #' #' @return Corrected data as UMI counts #' #' @importFrom methods as #' #' @export #' #' @examples #' \donttest{ #' vst_out <- vst(pbmc, return_cell_attr = TRUE) #' umi_corrected <- correct_counts(vst_out, pbmc) #' } #' correct_counts <- function(x, umi, cell_attr = x$cell_attr, scale_factor = NA, verbosity = 2) { regressor_data_orig <- model.matrix(get_model_formula(x$model_str), cell_attr) # when correcting, set all latent variables to median values cell_attr[, x$arguments$latent_var] <- apply(cell_attr[, x$arguments$latent_var, drop=FALSE], 2, function(x) rep(median(x), length(x))) if (!is.na(scale_factor) && !is.numeric(scale_factor)){ stop("`scale_factor` should be numeric") } if (!is.na(scale_factor)){ if (verbosity>0){ message(paste("Setting log_umi for correcting counts to", scale_factor)) } cell_attr[, "log_umi"] <- log10(scale_factor) } regressor_data <- model.matrix(get_model_formula(x$model_str), cell_attr) genes <- rownames(umi)[rownames(umi) %in% rownames(x$model_pars_fit)] bin_size <- x$arguments$bin_size if (verbosity > 0) { message('Computing corrected UMI count matrix') } pb_setup <- setup_progress_bar(length(genes), bin_size, verbosity) bin_ind <- pb_setup$bin_ind max_bin <- pb_setup$max_bin pb <- pb_setup$pb #corrected_data <- matrix(NA_real_, length(genes), nrow(regressor_data), dimnames = list(genes, rownames(regressor_data))) corrected_data <- list() for (i in 1:max_bin) { genes_bin <- genes[bin_ind == i] coefs <- x$model_pars_fit[genes_bin, -1, drop=FALSE] theta <- x$model_pars_fit[genes_bin, 1] # get pearson residuals mu <- exp(tcrossprod(coefs, regressor_data_orig)) variance <- mu + mu^2 / theta y <- as.matrix(umi[genes_bin, , drop=FALSE]) pearson_residual <- (y - mu) / sqrt(variance) # generate output mu <- exp(tcrossprod(coefs, regressor_data)) variance <- mu + mu^2 / theta y.res <- mu + pearson_residual * sqrt(variance) y.res <- round(y.res, 0) y.res[y.res < 0] <- 0 corrected_data[[length(corrected_data) + 1]] <- make.sparse(mat = y.res) update_progress_bar(pb, i, verbosity) } close_progress_bar(pb, verbosity) corrected_data <- do.call(what = rbind, args = corrected_data) return(corrected_data) } reverse_regression <- function(pearson_residual, theta, coefs, data) { mu <- exp(data %*% coefs)[, 1] variance <- mu + mu^2 / theta return(mu + pearson_residual * sqrt(variance)) } sctransform/R/utils.R0000644000176200001440000005026415123222545014307 0ustar liggesusers # Check cell attributes; add missing ones make_cell_attr <- function(umi, cell_attr, latent_var, batch_var, latent_var_nonreg, verbosity) { if (is.null(cell_attr)) { cell_attr <- data.frame(row.names = colnames(umi)) } # Make sure count matrix has row and column names if (is.null(rownames(umi)) || is.null(colnames(umi))) { stop('count matrix must have row and column names') } # Make sure rownames of cell attributes match cell names in count matrix if (!setequal(rownames(cell_attr), colnames(umi))) { stop('cell attribute row names must match column names of count matrix') } # Do not allow certain variable names no_good <- c('(Intercept)', 'Intercept') if (any(no_good %in% c(latent_var, batch_var, latent_var_nonreg))) { stop('Do not use the following variable names for a latent variable or batch variable: ', paste(no_good, collapse = ', ')) } # these are the cell attributes that we know how to calculate given the count matrix known_attr <- c('umi', 'gene', 'log_umi', 'log_gene', 'umi_per_gene', 'log_umi_per_gene') # these are the missing cell attributes specified in latent_var missing_attr <- setdiff(c(latent_var, batch_var, latent_var_nonreg), colnames(cell_attr)) if (length(missing_attr) > 0) { if (verbosity > 0) { message('Calculating cell attributes from input UMI matrix: ', paste(missing_attr, collapse = ', ')) } unknown_attr <- setdiff(missing_attr, known_attr) if (length(unknown_attr) > 0) { stop(sprintf('Unknown cell attributes: %s. Check latent_var, batch_var and latent_var_nonreg and make sure the variables are in cell_attr', paste(unknown_attr, collapse = ', '))) } new_attr <- list() if (any(c('umi', 'log_umi', 'umi_per_gene', 'log_umi_per_gene') %in% missing_attr)) { new_attr$umi <- colSums(umi) new_attr$log_umi <- log10(new_attr$umi) } if (any(c('gene', 'log_gene', 'umi_per_gene', 'log_umi_per_gene') %in% missing_attr)) { new_attr$gene <- colSums(umi > 0) new_attr$log_gene <- log10(new_attr$gene) } if (any(c('umi_per_gene', 'log_umi_per_gene') %in% missing_attr)) { new_attr$umi_per_gene <- new_attr$umi / new_attr$gene new_attr$log_umi_per_gene <- log10(new_attr$umi_per_gene) } new_attr <- do.call(cbind, new_attr) cell_attr <- cbind(cell_attr, new_attr[, setdiff(colnames(new_attr), colnames(cell_attr)), drop = FALSE]) } # make sure no NA, NaN, Inf values are in cell attributes - they would cause # problems later on for (ca in c(latent_var, batch_var, latent_var_nonreg)) { ca_values <- cell_attr[, ca] if (any(is.na(ca_values)) || any(is.nan(ca_values)) || any(is.infinite(ca_values))) { stop('cell attribute "', ca, '" contains NA, NaN, or infinite value') } } return(cell_attr) } #' Geometric mean per row #' #' @param x matrix of class \code{matrix} or \code{dgCMatrix} #' @param eps small value to add to x to avoid log(0); default is 1 #' #' @return geometric means row_gmean <- function(x, eps = 1) { if (inherits(x = x, what = 'matrix')) { return(exp(rowMeans(log(x + eps))) - eps) } if (inherits(x = x, what = 'dgCMatrix')) { ret <- row_gmean_dgcmatrix(matrix = x, eps = eps) names(ret) <- rownames(x) return(ret) } stop('matrix x needs to be of class matrix or dgCMatrix') } #' Variance per row #' #' @param x matrix of class \code{matrix} or \code{dgCMatrix} #' #' @return variances #' #' @importFrom matrixStats rowVars row_var <- function(x) { if (inherits(x = x, what = 'matrix')) { ret <- rowVars(x) names(ret) <- rownames(x) return(ret) } if (inherits(x = x, what = 'dgCMatrix')) { ret <- row_var_dgcmatrix(x = x@x, i = x@i, rows = nrow(x), cols = ncol(x)) names(ret) <- rownames(x) return(ret) } stop('matrix x needs to be of class matrix or dgCMatrix') } #' Identify outliers #' #' @param y Dependent variable #' @param x Independent variable #' @param th Outlier score threshold #' #' @return Boolean vector #' #' @importFrom stats aggregate #' is_outlier <- function(y, x, th = 10) { #bin.width <- var(x) * bw.SJ(x) bin.width <- (max(x) - min(x)) * bw.SJ(x) / 2 eps <- .Machine$double.eps * 10 breaks1 <- seq(from = min(x) - eps, to = max(x) + bin.width, by = bin.width) breaks2 <- seq(from = min(x) - eps - bin.width/2, to = max(x) + bin.width, by = bin.width) score1 <- robust_scale_binned(y, x, breaks1) score2 <- robust_scale_binned(y, x, breaks2) return(pmin(abs(score1), abs(score2)) > th) } #' Robust scale using median and mad per bin #' #' @param y Numeric vector #' @param x Numeric vector #' @param breaks Numeric vector of breaks #' #' @return Numeric vector of scaled score #' #' @importFrom stats aggregate #' robust_scale_binned <- function(y, x, breaks) { bins <- cut(x = x, breaks = breaks, ordered_result = TRUE) tmp <- aggregate(x = y, by = list(bin=bins), FUN = robust_scale) score <- rep(0, length(x)) o <- order(bins) if (inherits(x = tmp$x, what = 'list')) { score[o] <- unlist(tmp$x) } else { score[o] <- as.numeric(t(tmp$x)) } return(score) } #' Robust scale using median and mad #' #' @param x Numeric #' #' @return Numeric #' #' @importFrom stats median mad #' robust_scale <- function(x) { return((x - median(x)) / (mad(x) + .Machine$double.eps)) } pearson_residual <- function(y, mu, theta, min_var = -Inf) { model_var <- mu + mu^2 / theta model_var[model_var < min_var] <- min_var return((y - mu) / sqrt(model_var)) } pearson_residual2 <- function(y, mu, theta, min_vars) { model_var <- mu + mu^2 / theta for (row in 1:nrow(model_var)){ var_row <- model_var[row,] min_var <- min_vars[row] var_row[var_row < min_var] <- min_var model_var[row,] <- var_row } return((y - mu) / sqrt(model_var)) } sq_deviance_residual <- function(y, mu, theta, wt=1) { 2 * wt * (y * log(pmax(1, y)/mu) - (y + theta) * log((y + theta)/(mu + theta))) } deviance_residual <- function(y, mu, theta, wt=1) { r <- 2 * wt * (y * log(pmax(1, y)/mu) - (y + theta) * log((y + theta)/(mu + theta))) sqrt(r) * sign(y - mu) } #' Return Pearson or deviance residuals of regularized models #' #' @param vst_out The output of a vst run #' @param umi The UMI count matrix that will be used #' @param residual_type What type of residuals to return; can be 'pearson' or 'deviance'; default is 'pearson' #' @param res_clip_range Numeric of length two specifying the min and max values the results will be clipped to; default is c(-sqrt(ncol(umi)), sqrt(ncol(umi))) #' @param min_variance Lower bound for the estimated variance for any gene in any cell when calculating pearson residual; default is vst_out$arguments$min_variance #' @param cell_attr Data frame of cell meta data #' @param bin_size Number of genes to put in each bin (to show progress) #' @param verbosity An integer specifying the verbosity level: 0 (silent, no messages), 1 (show messages only), or 2 (show messages and progress bars); default is 2 #' #' @return A matrix of residuals #' #' @export #' #' @examples #' \donttest{ #' vst_out <- vst(pbmc, return_cell_attr = TRUE) #' pearson_res <- get_residuals(vst_out, pbmc) #' deviance_res <- get_residuals(vst_out, pbmc, residual_type = 'deviance') #' } #' get_residuals <- function(vst_out, umi, residual_type = 'pearson', res_clip_range = c(-sqrt(ncol(umi)), sqrt(ncol(umi))), min_variance = vst_out$arguments$min_variance, cell_attr = vst_out$cell_attr, bin_size = 256, verbosity = vst_out$arguments$verbosity) { # min_variance estimated using median umi if (min_variance == "umi_median"){ # Maximum pearson residual for non-zero median UMI is 5 min_var <- (get_nz_median2(umi) / 5)^2 if (verbosity > 0) { message(paste("Setting min_variance based on median UMI: ", min_var)) } } else { if (verbosity > 0) { message(paste("Setting min_variance to: ", min_variance)) } min_var <- min_variance } regressor_data <- prepare_regressor_data(vst_out, cell_attr) model_pars <- vst_out$model_pars_fit if (!is.null(dim(vst_out$model_pars_nonreg))) { model_pars <- cbind(vst_out$model_pars_fit, vst_out$model_pars_nonreg) } genes <- rownames(umi)[rownames(umi) %in% rownames(model_pars)] if (verbosity > 0) { message('Calculating residuals of type ', residual_type, ' for ', length(genes), ' genes') } pb_setup <- setup_progress_bar(length(genes), bin_size, verbosity) bin_ind <- pb_setup$bin_ind max_bin <- pb_setup$max_bin pb <- pb_setup$pb res <- matrix(NA_real_, length(genes), nrow(regressor_data), dimnames = list(genes, rownames(regressor_data))) for (i in 1:max_bin) { genes_bin <- genes[bin_ind == i] mu <- exp(tcrossprod(model_pars[genes_bin, -1, drop=FALSE], regressor_data)) y <- as.matrix(umi[genes_bin, , drop=FALSE]) if (min_variance == "model_mean") { mu_mean_var <- matrixStats::rowMeans2(mu) res[genes_bin, ] <- switch(residual_type, 'pearson' = pearson_residual2(y, mu, model_pars[genes_bin, 'theta'], min_vars = mu_mean_var), 'deviance' = deviance_residual(y, mu, model_pars[genes_bin, 'theta']), stop('residual_type ', residual_type, ' unknown - only pearson and deviance supported at the moment')) } else if (min_variance == "model_median") { mu_median_var <- matrixStats::rowMedians(mu) res[genes_bin, ] <- switch(residual_type, 'pearson' = pearson_residual2(y, mu, model_pars[genes_bin, 'theta'], min_vars = mu_median_var), 'deviance' = deviance_residual(y, mu, model_pars[genes_bin, 'theta']), stop('residual_type ', residual_type, ' unknown - only pearson and deviance supported at the moment')) } else { res[genes_bin, ] <- switch(residual_type, 'pearson' = pearson_residual(y, mu, model_pars[genes_bin, 'theta'], min_var = min_var), 'deviance' = deviance_residual(y, mu, model_pars[genes_bin, 'theta']), stop('residual_type ', residual_type, ' unknown - only pearson and deviance supported at the moment')) } update_progress_bar(pb, i, verbosity) } close_progress_bar(pb, verbosity) res <- clip_matrix_values(res, res_clip_range) return(res) } #' Return variance of residuals of regularized models #' #' This never creates the full residual matrix and can be used to determine highly variable genes. #' #' @param vst_out The output of a vst run #' @param umi The UMI count matrix that will be used #' @param residual_type What type of residuals to return; can be 'pearson' or 'deviance'; default is 'pearson' #' @param res_clip_range Numeric of length two specifying the min and max values the residuals will be clipped to; default is c(-sqrt(ncol(umi)), sqrt(ncol(umi))) #' @param min_variance Lower bound for the estimated variance for any gene in any cell when calculating pearson residual; default is vst_out$arguments$min_variance #' @param cell_attr Data frame of cell meta data #' @param bin_size Number of genes to put in each bin (to show progress) #' @param verbosity An integer specifying the verbosity level: 0 (silent, no messages), 1 (show messages only), or 2 (show messages and progress bars); default is 2 #' #' @return A vector of residual variances (after clipping) #' #' @export #' #' @examples #' \donttest{ #' vst_out <- vst(pbmc, return_cell_attr = TRUE) #' res_var <- get_residual_var(vst_out, pbmc) #' } #' get_residual_var <- function(vst_out, umi, residual_type = 'pearson', res_clip_range = c(-sqrt(ncol(umi)), sqrt(ncol(umi))), min_variance = vst_out$arguments$min_variance, cell_attr = vst_out$cell_attr, bin_size = 256, verbosity = vst_out$arguments$verbosity) { regressor_data <- prepare_regressor_data(vst_out, cell_attr) model_pars <- vst_out$model_pars_fit if (!is.null(dim(vst_out$model_pars_nonreg))) { model_pars <- cbind(vst_out$model_pars_fit, vst_out$model_pars_nonreg) } genes <- rownames(umi)[rownames(umi) %in% rownames(model_pars)] # min_variance estimated using median umi if (min_variance == "umi_median"){ # Maximum pearson residual for non-zero median UMI is 5 min_var <- (get_nz_median2(umi, genes) / 5)^2 if (verbosity > 0) { message(paste("Setting min_variance based on median UMI: ", min_var)) } } else { if (verbosity > 0) { message(paste("Setting min_variance to: ", min_variance)) } min_var <- min_variance } if (verbosity > 0) { message('Calculating variance for residuals of type ', residual_type, ' for ', length(genes), ' genes') } pb_setup <- setup_progress_bar(length(genes), bin_size, verbosity) bin_ind <- pb_setup$bin_ind max_bin <- pb_setup$max_bin pb <- pb_setup$pb res <- matrix(NA_real_, length(genes)) names(res) <- genes for (i in 1:max_bin) { genes_bin <- genes[bin_ind == i] mu <- exp(tcrossprod(model_pars[genes_bin, -1, drop=FALSE], regressor_data)) y <- as.matrix(umi[genes_bin, , drop=FALSE]) if (min_variance == "model_mean") { mu_mean_var <- matrixStats::rowMeans2(mu) res_mat <- switch(residual_type, 'pearson' = pearson_residual2(y, mu, model_pars[genes_bin, 'theta'], min_vars = mu_mean_var), 'deviance' = deviance_residual(y, mu, model_pars[genes_bin, 'theta']), stop('residual_type ', residual_type, ' unknown - only pearson and deviance supported at the moment')) } else if (min_variance == "model_median") { mu_median_var <- matrixStats::rowMedians(mu) res_mat <- switch(residual_type, 'pearson' = pearson_residual2(y, mu, model_pars[genes_bin, 'theta'], min_vars = mu_median_var), 'deviance' = deviance_residual(y, mu, model_pars[genes_bin, 'theta']), stop('residual_type ', residual_type, ' unknown - only pearson and deviance supported at the moment')) } else { res_mat <- switch(residual_type, 'pearson' = pearson_residual(y, mu, model_pars[genes_bin, 'theta'], min_var = min_var), 'deviance' = deviance_residual(y, mu, model_pars[genes_bin, 'theta']), stop('residual_type ', residual_type, ' unknown - only pearson and deviance supported at the moment')) } res_mat <- clip_matrix_values(res_mat, res_clip_range) res[genes_bin] <- row_var(res_mat) update_progress_bar(pb, i, verbosity) } close_progress_bar(pb, verbosity) return(res) } #' Return average variance under negative binomial model #' #' This is based on the formula var = mu + mu^2 / theta #' #' @param vst_out The output of a vst run #' @param cell_attr Data frame of cell meta data #' @param use_nonreg Use the non-regularized parameter estimates; boolean; default is FALSE #' @param bin_size Number of genes to put in each bin (to show progress) #' @param verbosity An integer specifying the verbosity level: 0 (silent, no messages), 1 (show messages only), or 2 (show messages and progress bars); default is 2 #' #' @return A named vector of variances (the average across all cells), one entry per gene. #' #' @export #' #' @examples #' \donttest{ #' vst_out <- vst(pbmc, return_cell_attr = TRUE) #' res_var <- get_model_var(vst_out) #' } #' get_model_var <- function(vst_out, cell_attr = vst_out$cell_attr, use_nonreg = FALSE, bin_size = 256, verbosity = 2) { regressor_data <- prepare_regressor_data(vst_out, cell_attr) if (use_nonreg) { model_pars <- vst_out$model_pars } else { model_pars <- vst_out$model_pars_fit } if (!is.null(dim(vst_out$model_pars_nonreg))) { model_pars <- cbind(vst_out$model_pars_fit, vst_out$model_pars_nonreg) } genes <- rownames(model_pars) if (verbosity > 0) { message('Calculating model variance for ', length(genes), ' genes') } pb_setup <- setup_progress_bar(length(genes), bin_size, verbosity) bin_ind <- pb_setup$bin_ind max_bin <- pb_setup$max_bin pb <- pb_setup$pb res <- matrix(NA_real_, length(genes)) names(res) <- genes for (i in 1:max_bin) { genes_bin <- genes[bin_ind == i] mu <- exp(tcrossprod(model_pars[genes_bin, -1, drop=FALSE], regressor_data)) model_var = mu + mu^2 / model_pars[genes_bin, 'theta'] res[genes_bin] <- rowMeans(model_var) update_progress_bar(pb, i, verbosity) } close_progress_bar(pb, verbosity) return(res) } #' Get median of non zero UMIs from a count matrix #' #' @param umi Count matrix #' @param genes A vector of genes to consider for calculating #' the median. Default is NULL which uses all genes. #' @return A numeric value representing the median of non-zero entries from the UMI matrix get_nz_median2 <- function(umi, genes = NULL){ if (is.null(genes)) { # Compute median for the entire matrix return (median(umi@x)) } else if (length(genes) == 1) { # If only one gene is being subsetted return (median(umi[genes, umi[genes,] != 0])) } else if (length(genes) > 1) { # If multiple genes are being subsetted return (median(umi[genes,]@x)) } else { stop("genes does not contain a vector of gene names") } } #' Convert a given matrix to dgCMatrix #' #' @param mat Input matrix #' #' @return A dgCMatrix make.sparse <- function(mat){ mat <- as(object = mat, Class = "Matrix") return (as(object = as(object = as(object = mat, Class = "dMatrix"), Class = "generalMatrix"), Class = "CsparseMatrix")) } #' Extract model formula from model string #' #' Helper function to convert model string (with 'y' prefix) to formula object #' #' @param model_str Model string with 'y' prefix (e.g., 'y ~ log_umi') #' #' @return Formula object without 'y' prefix get_model_formula <- function(model_str) { as.formula(gsub('^y', '', model_str)) } #' Prepare regressor data from vst object and cell attributes #' #' Helper function to create regressor data matrix, handling both regular #' and non-regularized parameters if present #' #' @param vst_out vst object containing model_str and model_pars_nonreg #' @param cell_attr Data frame of cell attributes #' #' @return Matrix of regressor data prepare_regressor_data <- function(vst_out, cell_attr) { regressor_data <- model.matrix(get_model_formula(vst_out$model_str), cell_attr) if (!is.null(dim(vst_out$model_pars_nonreg))) { regressor_data_nonreg <- model.matrix(get_model_formula(vst_out$model_str_nonreg), cell_attr) regressor_data <- cbind(regressor_data, regressor_data_nonreg) } return(regressor_data) } #' Setup progress bar for batch processing #' #' @param n_items Total number of items to process #' @param bin_size Size of each batch/bin #' @param verbosity An integer specifying the verbosity level: 0 (silent), 1 (messages only), or 2 (messages and progress bars) #' #' @return A list with bin_ind (vector), pb (progress bar object or NULL), and max_bin setup_progress_bar <- function(n_items, bin_size, verbosity) { bin_ind <- ceiling(x = 1:n_items / bin_size) max_bin <- max(bin_ind) pb <- NULL if (verbosity > 1) { pb <- txtProgressBar(min = 0, max = max_bin, style = 3) } return(list(bin_ind = bin_ind, pb = pb, max_bin = max_bin)) } #' Update progress bar #' #' @param pb Progress bar object (can be NULL) #' @param i Current iteration number #' @param verbosity An integer specifying the verbosity level: 0 (silent), 1 (messages only), or 2 (messages and progress bars) update_progress_bar <- function(pb, i, verbosity) { if (verbosity > 1 && !is.null(pb)) { setTxtProgressBar(pb, i) } } #' Close progress bar #' #' @param pb Progress bar object (can be NULL) #' @param verbosity An integer specifying the verbosity level: 0 (silent), 1 (messages only), or 2 (messages and progress bars) close_progress_bar <- function(pb, verbosity) { if (verbosity > 1 && !is.null(pb)) { close(pb) } } #' Clip matrix values to specified range #' #' @param mat Matrix to clip #' @param clip_range Numeric vector of length 2 with min and max values #' #' @return Matrix with values clipped to range clip_matrix_values <- function(mat, clip_range) { mat[mat < clip_range[1]] <- clip_range[1] mat[mat > clip_range[2]] <- clip_range[2] return(mat) } sctransform/R/fit.R0000644000176200001440000002044314731054755013737 0ustar liggesusers# Fit NB regression models using different approaches fit_poisson <- function(umi, model_str, data, theta_estimation_fun) { regressor_data <- model.matrix(as.formula(gsub('^y', '', model_str)), data) dfr <- ncol(umi) - ncol(regressor_data) par_mat <- t(apply(umi, 1, function(y) { fit <- qpois_reg(regressor_data, y, 1e-9, 100, 1.0001, TRUE) theta <- switch(theta_estimation_fun, 'theta.ml' = as.numeric(x = suppressWarnings(theta.ml(y = y, mu = fit$fitted))), 'theta.mm' = as.numeric(x = theta.mm(y = y, mu = fit$fitted, dfr = dfr)), stop('theta_estimation_fun ', theta_estimation_fun, ' unknown - only theta.ml and theta.mm supported at the moment') ) return(c(theta, fit$coefficients)) })) return(par_mat) } fit_qpoisson <- function(umi, model_str, data) { regressor_data <- model.matrix(as.formula(gsub('^y', '', model_str)), data) par_mat <- t(apply(umi, 1, function(y) { fit <- qpois_reg(regressor_data, y, 1e-9, 100, 1.0001, FALSE) return(c(fit$theta.guesstimate, fit$coefficients)) })) return(par_mat) } fit_nb_theta_given <- function(umi, model_str, data, theta_given) { par_lst <- lapply(1:nrow(umi), function(j) { y <- umi[j, ] theta <- theta_given[j] fit2 <- 0 try(fit2 <- glm(as.formula(model_str), data = data, family = negative.binomial(theta=theta)), silent=TRUE) if (inherits(x = fit2, what = 'numeric')) { return(c(theta, glm(as.formula(model_str), data = data, family = poisson)$coefficients)) } else { return(c(theta, fit2$coefficients)) } }) return(do.call(rbind, par_lst)) } fit_nb_fast <- function(umi, model_str, data, theta_estimation_fun) { regressor_data <- model.matrix(as.formula(gsub('^y', '', model_str)), data) dfr <- ncol(umi) - ncol(regressor_data) par_mat <- apply(umi, 1, function(y) { fit <- qpois_reg(regressor_data, y, 1e-9, 100, 1.0001, TRUE) theta <- switch(theta_estimation_fun, 'theta.ml' = as.numeric(x = theta.ml(y = y, mu = fit$fitted)), 'theta.mm' = as.numeric(x = theta.mm(y = y, mu = fit$fitted, dfr = dfr)), stop('theta_estimation_fun ', theta_estimation_fun, ' unknown - only theta.ml and theta.mm supported at the moment') ) fit2 <- 0 try(fit2 <- glm(as.formula(model_str), data = data, family = negative.binomial(theta=theta)), silent=TRUE) if (inherits(x = fit2, what = 'numeric')) { return(c(theta, fit$coefficients)) } else { return(c(theta, fit2$coefficients)) } }) return(t(par_mat)) } fit_nb <- function(umi, model_str, data) { par_mat <- apply(umi, 1, function(y) { fit <- 0 try(fit <- glm.nb(as.formula(model_str), data = data), silent=TRUE) if (inherits(x = fit, what = 'numeric')) { fit <- glm(as.formula(model_str), data = data, family = poisson) fit$theta <- as.numeric(x = suppressWarnings(theta.ml(y = y, mu = fit$fitted))) } return(c(fit$theta, fit$coefficients)) }) return(t(par_mat)) } # allow_inf_theta: if FALSE, replace theta by min(theta, rowmeans(mu)/1e-4) # else allow theta = Inf (poisson) fit_glmGamPoi <- function(umi, model_str, data, allow_inf_theta=FALSE) { fit <- glmGamPoi::glm_gp(data = umi, design = as.formula(gsub("y", "", model_str)), col_data = data, size_factors = FALSE) fit$theta <- 1 / fit$overdispersions if (!allow_inf_theta){ fit$theta <- pmin(1 / fit$overdispersions, rowMeans(fit$Mu) / 1e-4) } colnames(fit$Beta)[match(x = 'Intercept', colnames(fit$Beta))] <- "(Intercept)" return(cbind(fit$theta, fit$Beta)) } fit_overdisp_mle <- function(umi, mu, intercept, slope){ fit <- glmGamPoi::overdispersion_mle(umi, mu, model_matrix = NULL, do_cox_reid_adjustment = TRUE, global_estimate = FALSE, subsample = FALSE, max_iter = 200, verbose = FALSE) theta <- 1 / fit$estimate model_pars <- cbind(theta, intercept, slope) colnames(model_pars) <- c("theta", "(Intercept)", "log_umi") return (model_pars) } # Use log_umi as offset using glmGamPoi fit_glmGamPoi_offset <- function(umi, model_str, data, allow_inf_theta=FALSE) { log10_umi <- data$log_umi stopifnot(!is.null(log10_umi)) log_umi <- log(10^log10_umi) # only intercept varies includes.batch_var <- FALSE if (grepl(pattern = "\\(log_umi\\) :", x = model_str)) { includes.batch_var <- TRUE } new_formula <- gsub("y", "", model_str) includes.log_umi <- grepl(pattern = "~ log_umi", x = new_formula) if (!includes.batch_var) { # if therse is no batch variable - remove log_umi and fix it # remove log_umi from model formula if it is with batch variables new_formula <- gsub(pattern = "\\+ log_umi", replacement = "", x = new_formula) # replace log_umi with 1 if it is the only formula new_formula <- gsub(pattern = "log_umi", replacement = "1", x = new_formula) } else { log_umi <- 0 } fit <- glmGamPoi::glm_gp(data = umi, design = as.formula(new_formula), col_data = data, offset = log_umi, size_factors = FALSE) fit$theta <- 1 / fit$overdispersions if (!allow_inf_theta){ fit$theta <- pmin(1 / fit$overdispersions, rowMeans(fit$Mu) / 1e-4) } if ("Intercept" %in% colnames(x = fit$Beta)) { if (includes.log_umi){ model_pars <- cbind(fit$theta, fit$Beta[, "Intercept"], rep(log(10), nrow(umi))) dimnames(model_pars) <- list(rownames(umi), c('theta', '(Intercept)', 'log_umi')) n_coefficients <- ncol(fit$Beta) if (n_coefficients>1){ model_pars <- cbind(model_pars, fit$Beta[, 2:n_coefficients]) colnames(x = model_pars)[4:ncol(x = model_pars)] <- colnames(x = fit$Beta)[2:n_coefficients] } } else { model_pars <- cbind(fit$theta, fit$Beta) dimnames(model_pars) <- list(rownames(umi), c('theta', colnames(x = fit$Beta))) } } else { if (!includes.batch_var){ if (includes.log_umi){ model_pars <- cbind(fit$theta, rep(log(10), nrow(umi)), fit$Beta) dimnames(model_pars) <- list(rownames(umi), c('theta', 'log_umi', colnames(x = fit$Beta))) } else { model_pars <- cbind(fit$theta, fit$Beta) dimnames(model_pars) <- list(rownames(umi), c('theta', colnames(x = fit$Beta))) } } else { model_pars <- cbind(fit$theta, fit$Beta) dimnames(model_pars) <- list(rownames(umi), c('theta', colnames(x = fit$Beta))) } } colnames(x = model_pars)[match(x = 'Intercept', table = colnames(x = model_pars))] <- "(Intercept)" return(model_pars) } fit_nb_offset <- function(umi, model_str, data, allow_inf_theta=FALSE) { # remove log_umi from model formula if it is with batch variables new_formula <- gsub(pattern = "\\+ log_umi", replacement = "", x = model_str) # replace log_umi with 1 if it is the only formula new_formula <- gsub(pattern = "log_umi", replacement = "1 + offset(log_umi)", x = new_formula) log10_umi <- data$log_umi stopifnot(!is.null(log10_umi)) log_umi <- log(10^log10_umi) data$log_umi <- log_umi par_mat <- apply(umi, 1, function(y) { fit <- 0 try(fit <- glm.nb(formula = as.formula(new_formula), data = data), silent=TRUE) if (inherits(x = fit, what = 'numeric')) { fit <- glm(formula = as.formula(new_formula), data = data, family = poisson) fit$theta <- Inf #fit$theta <- as.numeric(x = suppressWarnings(theta.ml(y = y, mu = fit$fitted))) } if (!allow_inf_theta){ fit$theta <- pmin(fit$theta, mean(y) / 1e-4) } return(c(fit$theta, fit$coefficients)) }) model_pars <- t(par_mat) model_pars <- cbind(model_pars, rep(log(10), nrow(umi))) rownames(x = model_pars) <- rownames(x = umi) colnames(x = model_pars)[match(x = 'Intercept', table = colnames(x = model_pars))] <- "(Intercept)" colnames(x = model_pars)[ncol(x = model_pars)] <- "log_umi" return(model_pars) } sctransform/R/umify.R0000644000176200001440000000764514731054755014317 0ustar liggesusersdata("umify_data", envir=environment()) #' Quantile normalization of cell-level data to match typical UMI count data #' #' @param counts A matrix of class dgCMatrix with genes as rows and columns as cells #' #' @return A UMI-fied count matrix #' #' @section Details: #' sctransform::vst operates under the assumption that gene counts approximately #' follow a Negative Binomial dristribution. For UMI-based data that seems to be #' the case, however, non-UMI data does not behave in the same way. #' In some cases it might be better to to apply a transformation to such data #' to make it look like UMI data. This function applies such a transformation function. #' #' Cells in the input matrix are processed independently. For each cell #' the non-zero data is transformed to quantile values. Based on the number of genes #' detected a smooth function is used to predict the UMI-like counts. #' #' The functions have be trained on various public data sets and come as part of the #' package (see umify_data data set in this package). #' #' @importFrom magrittr %>% #' @importFrom dplyr group_by summarise pull mutate case_when #' @importFrom stats approxfun #' @importFrom rlang .data #' #' @export #' #' @examples #' \donttest{ #' silly_example <- umify(pbmc) #' } umify <- function(counts) { # check input if (!inherits(x = counts, what = 'dgCMatrix')) { stop('counts must be a dgCMatrix') } # load the group breaks needed to place cells into groups grp_breaks <- umify_data$grp_breaks K <- length(grp_breaks) - 1 w <- mean(diff(grp_breaks)) # given the umify data models, create functions that # predict the log count from the distribution quantile # create one function per group membership (based on number of genes detected) apprx_funs <- group_by(umify_data$fit_df, .data$grp) %>% summarise(fun = list(approxfun(x = .data$q, y = .data$log_y, rule = 2:1)), .groups = 'drop') %>% pull(.data$fun) names(apprx_funs) <- levels(umify_data$fit_df$grp) # for each cell in the input we need to know how many genes are detected # then determine the primary and secondary group and the weights # to be used for the linear interpolation ca <- data.frame(genes = diff(counts@p)) %>% mutate(log_genes = log10(.data$genes), grp1 = cut(.data$log_genes, breaks = grp_breaks, right = FALSE), weight = ((.data$log_genes - grp_breaks[1]) / w) %% 1, grp2 = case_when(.data$weight >= 0.5 ~ as.numeric(.data$grp1)+1, TRUE ~ as.numeric(.data$grp1)-1), grp2 = case_when(.data$grp2 < 1 ~ 1, .data$grp2 > K ~ K, TRUE ~ .data$grp2), grp2 = factor(levels(.data$grp1)[.data$grp2], levels = levels(.data$grp1)), weight = -abs(.data$weight-0.5)+1) if (any(is.na(ca$grp1))) { warning(sprintf('Cells with very few or too many genes detected. The lower limit is %d, the upper limit is %d. Setting non-zero values to NA for %d cells.', floor(10^min(grp_breaks)), floor(10^max(grp_breaks)), sum(is.na(ca$grp1)))) } # predict UMIfied counts # per cell out_vec <- rep(NA_real_, length(counts@x)) j <- 1 while (j <= ncol(counts)) { if (!is.na(ca$grp1[j])) { i_start <- counts@p[j] + 1 i_end <- counts@p[j+1] sel <- i_start:i_end q <- rank(counts@x[sel], ties.method = 'max') / length(sel) pred1 <- apprx_funs[[ca$grp1[j]]](q) pred2 <- apprx_funs[[ca$grp2[j]]](q) out_vec[sel] <- round(10^(pred1 * ca$weight[j] + pred2 * (1 - ca$weight[j]))) } j <- j + 1 } counts_new <- sparseMatrix(i = counts@i, p = counts@p, x = out_vec, dims = counts@Dim, dimnames = counts@Dimnames, giveCsparse = TRUE, index1 = FALSE) return(counts_new) } sctransform/R/differential_expression.R0000644000176200001440000011721115123222545020056 0ustar liggesusers#' Compare gene expression between two groups #' #' @param x A list that provides model parameters and optionally meta data; use output of vst function #' @param umi A matrix of UMI counts with genes as rows and cells as columns #' @param group A vector indicating the groups #' @param val1 A vector indicating the values of the group vector to treat as group 1 #' @param val2 A vector indicating the values of the group vector to treat as group 2 #' @param method Either 'LRT' for likelihood ratio test, or 't_test' for t-test #' @param bin_size Number of genes that are processed between updates of progress bar #' @param cell_attr Data frame of cell meta data #' @param y Only used if method = 't_test', this is the residual matrix; default is x$y #' @param min_cells A gene has to be detected in at least this many cells in at least one of the groups being compared to be tested #' @param weighted Balance the groups by using the appropriate weights #' @param randomize Boolean indicating whether to shuffle group labels - only set to TRUE when testing methods #' @param verbosity An integer specifying the verbosity level: 0 (silent, no messages), 1 (show messages only), or 2 (show messages and progress bars); default is 2 #' #' @return Data frame of results #' #' @import Matrix #' @importFrom future.apply future_lapply #' @importFrom future nbrOfWorkers #' @importFrom stats model.matrix p.adjust pchisq #' compare_expression <- function(x, umi, group, val1, val2, method = 'LRT', bin_size = 256, cell_attr = x$cell_attr, y = x$y, min_cells = 5, weighted = TRUE, randomize = FALSE, verbosity = 2) { if (! method %in% c('LRT', 'LRT_free', 'LRT_reg', 't_test')) { stop('method needs to be either \'LRT\', \'LRT_free\', \'LRT_reg\' or \'t_test\'') } if ('DE_test_group' %in% colnames(cell_attr)) { stop('DE_test_group cannot be a column name in cell attributes') } sel1 <- which(group %in% val1) sel2 <- which(group %in% val2) # randomize # if (randomize) { # sel.rnd <- sample(x = c(sel1, sel2), replace = FALSE) # sel1 <- sel.rnd[1:length(sel1)] # sel2 <- sel.rnd[(length(sel1)+1):length(sel.rnd)] # } use_cells <- c(sel1, sel2) group <- factor(c(rep(0, length(sel1)), rep(1, length(sel2)))) cell_attr <- cell_attr[use_cells, ] cell_attr$DE_test_group <- group if (weighted) { weights <- c(rep(1/length(sel1), length(sel1)), rep(1/length(sel2), length(sel2))) #weights <- c(rep(1/length(sel2), length(sel1)), rep(1/length(sel1), length(sel2))) weights <- weights / sum(weights) * length(use_cells) } else { weights <- rep(1, length(use_cells)) } print(table(weights)) genes <- rownames(x$model_pars_fit)[rownames(x$model_pars_fit) %in% rownames(umi)] cells_group1 <- rowSums(umi[genes, sel1] > 0) cells_group2 <- rowSums(umi[genes, sel2] > 0) genes <- genes[cells_group1 >= min_cells | cells_group2 >= min_cells] if (verbosity > 0) { message('Testing for differential gene expression between two groups') message('Cells in group 1: ', length(sel1)) message('Cells in group 2: ', length(sel2)) message('Testing ', length(genes), ' genes') } regressor_data <- model.matrix(as.formula(gsub('^y', '', x$model_str)), cell_attr) if (!is.null(dim(x$model_pars_nonreg))) { regressor_data_nonreg <- model.matrix(as.formula(gsub('^y', '', x$model_str_nonreg)), cell_attr) regressor_data <- cbind(regressor_data, regressor_data_nonreg) } # process genes in batches bin_ind <- ceiling(x = 1:length(x = genes) / bin_size) max_bin <- max(bin_ind) if (verbosity > 1) { pb <- txtProgressBar(min = 0, max = max_bin, style = 3) } res <- list() for (i in 1:max_bin) { genes_bin <- genes[bin_ind == i] if (method == 't_test') { bin_res <- future_lapply( X = genes_bin, FUN = function(gene) {model_comparison_ttest(y[gene, use_cells], group)}, future.seed = TRUE) } if (method == 'LRT') { mu <- x$model_pars_fit[genes_bin, -1, drop=FALSE] %*% t(regressor_data) # in log space y <- as.matrix(umi[genes_bin, use_cells]) bin_res <- future_lapply( X = genes_bin, FUN = function(gene) { model_comparison_lrt(y[gene, ], mu[gene, ], x$model_pars_fit[gene, 'theta'], group, weights)}, future.seed = TRUE) } if (method == 'LRT_reg') { LB <- min(x$genes_log_mean_step1) UB <- max(x$genes_log_mean_step1) y <- as.matrix(umi[genes_bin, use_cells, drop=FALSE]) if (randomize) { y <- t(apply(y, 1, sample)) #y <- t(apply(y, 1, function(x) ceiling(pmax(0, rnorm(n = length(x), mean = 0, sd = 2))))) } # get estimated model parameters and expected counts for all cells combined #y_log_mean <- log10(base::rowMeans(y)) y_log_mean <- log10(apply(y, 1, function(x) mean(x * weights))) y_log_mean <- pmax(LB, pmin(y_log_mean, UB)) names(y_log_mean) <- rownames(y) mp <- reg_pars(x$genes_log_mean_step1, x$model_pars, y_log_mean, x$arguments$bw_adjust) if (!is.null(dim(x$model_pars_nonreg))) { mp <- cbind(mp, x$model_pars_nonreg[genes_bin, ]) } mu <- exp(tcrossprod(mp[, -1, drop=FALSE], regressor_data)) sq_dev <- sapply(1:nrow(mu), function(i) sq_deviance_residual(y[i, ], mu[i, ], mp[i, 'theta'])) # same per group y0 <- y[, group==0] y_log_mean0 <- log10(base::rowMeans(y0)) y_log_mean0 <- pmax(LB, pmin(y_log_mean0, UB)) names(y_log_mean0) <- rownames(y) mp0 <- reg_pars(x$genes_log_mean_step1, x$model_pars, y_log_mean0, x$arguments$bw_adjust) if (!is.null(dim(x$model_pars_nonreg))) { mp0 <- cbind(mp0, x$model_pars_nonreg[genes_bin, ]) } mu0 <- exp(tcrossprod(mp0[, -1, drop=FALSE], regressor_data[group==0, ])) sq_dev0 <- sapply(1:nrow(mu0), function(i) sq_deviance_residual(y0[i, ], mu0[i, ], mp0[i, 'theta'])) y1 <- y[, group==1] y_log_mean1 <- log10(base::rowMeans(y1)) y_log_mean1 <- pmax(LB, pmin(y_log_mean1, UB)) names(y_log_mean1) <- rownames(y) mp1 <- reg_pars(x$genes_log_mean_step1, x$model_pars, y_log_mean1, x$arguments$bw_adjust) if (!is.null(dim(x$model_pars_nonreg))) { mp1 <- cbind(mp1, x$model_pars_nonreg[genes_bin, ]) } mu1 <- exp(tcrossprod(mp1[, -1, drop=FALSE], regressor_data[group==1, ])) sq_dev1 <- sapply(1:nrow(mu1), function(i) sq_deviance_residual(y1[i, ], mu1[i, ], mp1[i, 'theta'])) #pvals <- pchisq(base::rowSums(cbind(sq_dev0, sq_dev1)) - base::rowSums(sq_dev), df = 1, lower.tail = FALSE) pvals <- pchisq(base::colSums(sq_dev * weights) - base::colSums(rbind(sq_dev0, sq_dev1) * weights), df = 3, lower.tail = FALSE) #fold_change <- log2(10 ^ (y_log_mean1 - y_log_mean0)) # tmp stuff for fold change mu0 <- tcrossprod(mp0[, -1, drop=FALSE], regressor_data) mu1 <- tcrossprod(mp1[, -1, drop=FALSE], regressor_data) fold_change <- apply(log2(exp(mu1 - mu0)), 1, mean) #if (max(fold_change) > 0.4) browser() if ('SON' %in% genes_bin) browser() bin_res <- list(cbind(pvals, fold_change)) } if (method == 'LRT_free') { y <- as.matrix(umi[genes_bin, use_cells]) # get estimated theta bw <- bw.SJ(x$genes_log_mean_step1) y_log_mean <- log10(base::rowMeans(y)) o <- order(y_log_mean) y_theta <- rep(NA_real_, nrow(y)) y_theta[o] <- 10 ^ ksmooth(x = x$genes_log_mean_step1, y = log10(x$model_pars[, 'theta']), x.points = y_log_mean, bandwidth = bw, kernel='normal')$y names(y_theta) <- genes_bin bin_res <- future_lapply( X = genes_bin, FUN = function(gene) { return(model_comparison_lrt_free3(gene, y[gene, ], y_theta[gene], x$model_str, cell_attr, group, weights, randomize)) }, future.seed = TRUE) } res[[i]] <- do.call(rbind, bin_res) if (verbosity > 1) { setTxtProgressBar(pb, i) } } if (verbosity > 1) { close(pb) } res <- do.call(rbind, res) rownames(res) <- genes colnames(res) <- c('p_value', 'log_fc') res <- as.data.frame(res) res$fdr <- p.adjust(res$p_value, method='fdr') res <- res[order(res$p_value, -abs(res$log_fc)), ] res$mean1 <- rowMeans(umi[rownames(res), sel1]) res$mean2 <- rowMeans(umi[rownames(res), sel2]) res$mean <- rowMeans(umi[rownames(res), use_cells]) res$mean_weighted <- (res$mean1 + res$mean2) / 2 return(res) } compare_expression_full <- function(umi, cell_attr, group, val1, val2, latent_var = c('log_umi'), batch_var = NULL, latent_var_nonreg = NULL, n_genes = 2000, method = 'poisson', bin_size = 256, min_cells = 3, bw_adjust = 2, min_frac = 0, verbosity = 2) { sel1 <- which(group %in% val1) sel2 <- which(group %in% val2) det1 <- rowMeans(umi[, sel1] > 0) det2 <- rowMeans(umi[, sel2] > 0) umi <- umi[det1 >= min_frac | det2 >= min_frac, ] cells1 <- rowSums(umi[, sel1] > 0) cells2 <- rowSums(umi[, sel2] > 0) umi <- umi[cells1 >= min_cells | cells2 >= min_cells, ] vst.out0 <- vst(umi = umi[, c(sel1, sel2)], cell_attr = cell_attr[c(sel1, sel2), ], latent_var = latent_var, batch_var = batch_var, latent_var_nonreg = latent_var_nonreg, n_genes = n_genes, n_cells = NULL, method = method, do_regularize = TRUE, res_clip_range = c(-Inf, Inf), bin_size = bin_size, min_cells = min_cells, return_cell_attr = FALSE, return_gene_attr = FALSE, residual_type = 'deviance', bw_adjust = bw_adjust, verbosity = verbosity) vst.out1 <- vst(umi = umi[, sel1], cell_attr = cell_attr[sel1, ], latent_var = latent_var, batch_var = batch_var, latent_var_nonreg = latent_var_nonreg, n_genes = n_genes, n_cells = NULL, method = 'nb_theta_given', #method, do_regularize = TRUE, res_clip_range = c(-Inf, Inf), bin_size = bin_size, min_cells = min_cells, return_cell_attr = FALSE, return_gene_attr = FALSE, residual_type = 'deviance', bw_adjust = bw_adjust, theta_given = vst.out0$model_pars_fit[, 'theta'], verbosity = verbosity) vst.out2 <- vst(umi = umi[, sel2], cell_attr = cell_attr[sel2, ], latent_var = latent_var, batch_var = batch_var, latent_var_nonreg = latent_var_nonreg, n_genes = n_genes, n_cells = NULL, method = 'nb_theta_given', #method do_regularize = TRUE, res_clip_range = c(-Inf, Inf), bin_size = bin_size, min_cells = min_cells, return_cell_attr = FALSE, return_gene_attr = FALSE, residual_type = 'deviance', bw_adjust = bw_adjust, theta_given = vst.out0$model_pars_fit[, 'theta'], verbosity = verbosity) genes <- union(rownames(vst.out1$y), rownames(vst.out2$y)) genes_both <- intersect(rownames(vst.out1$y), rownames(vst.out2$y)) genes1 <- setdiff(rownames(vst.out1$y), genes_both) genes2 <- setdiff(rownames(vst.out2$y), genes_both) sq_dev_one <- base::rowSums(vst.out0$y[genes, ]^2 * 1) sq_dev_two <- rep(0, length(sq_dev_one)) names(sq_dev_two) <- genes sq_dev_two[rownames(vst.out1$y)] <- base::rowSums(vst.out1$y^2 * 1) sq_dev_two[rownames(vst.out2$y)] <- sq_dev_two[rownames(vst.out2$y)] + base::rowSums(vst.out2$y^2 * 1) pvals <- pchisq(sq_dev_one - sq_dev_two, df = 3, lower.tail = FALSE) # get log-fold change log_fc <- rep(NA_real_, length(sq_dev_one)) names(log_fc) <- genes regressor_data <- model.matrix(as.formula(gsub('^y', '', vst.out0$model_str)), cell_attr[c(sel1, sel2), ]) if (!is.null(dim(vst.out0$model_pars_nonreg))) { regressor_data_nonreg <- model.matrix(as.formula(gsub('^y', '', vst.out0$model_str_nonreg)), cell_attr[c(sel1, sel2), ]) regressor_data <- cbind(regressor_data, regressor_data_nonreg) } mp1 <- cbind(vst.out1$model_pars_fit, vst.out1$model_pars_nonreg) mp2 <- cbind(vst.out2$model_pars_fit, vst.out2$model_pars_nonreg) mu1 <- tcrossprod(mp1[genes_both, -1, drop=FALSE], regressor_data) mu2 <- tcrossprod(mp2[genes_both, -1, drop=FALSE], regressor_data) log_fc[genes_both] <- apply(log2(exp(mu2 - mu1)), 1, mean) log_fc[genes1] <- -Inf log_fc[genes2] <- Inf res <- data.frame(p_value = pvals, log_fc = log_fc) res$fdr <- p.adjust(res$p_value, method='fdr') res <- res[order(res$p_value, -abs(res$log_fc)), ] res$mean1 <- rowMeans(umi[rownames(res), sel1]) res$mean2 <- rowMeans(umi[rownames(res), sel2]) res$det1 <- rowMeans(umi[rownames(res), sel1] > 0) res$det2 <- rowMeans(umi[rownames(res), sel2] > 0) # tmp stuff # goi <- 'MALAT1' # y <- umi[goi, c(sel1, sel2)] # grp <- c(rep('A', length(sel1)), rep('B', length(sel2))) # df <- data.frame(y=y, log_umi=cell_attr[c(sel1, sel2), 'log_umi'], grp=grp) # mod0 <- glm.nb(y ~ log_umi, data = df) # mod1 <- glm.nb(y ~ log_umi + grp, data = df) # mod1 <- glm(y ~ log_umi + grp, data = df, family = negative.binomial(theta=mod0$theta)) # mod1 <- glm(y ~ log_umi:grp, data = df, family = negative.binomial(theta=mod0$theta)) return(res) } # function to get regularized model parameters reg_pars <- function(x, y.mat, x.points, bw.adjust) { bw <- bw.SJ(x) * bw.adjust o <- order(x.points) y.mat.out <- matrix(NA_real_, length(x.points), ncol(y.mat)) y.mat.out[o, 1] <- 10 ^ ksmooth(x = x, y = log10(y.mat[, 1]), x.points = x.points, bandwidth = bw*3, kernel='normal')$y for (i in 2:ncol(y.mat)) { y.mat.out[o, i] <- ksmooth(x = x, y = y.mat[, i], x.points = x.points, bandwidth = bw, kernel='normal')$y } colnames(y.mat.out) <- colnames(y.mat) rownames(y.mat.out) <- names(x.points) if (any(apply(is.na(y.mat.out), 1, any))) { browser() } return(y.mat.out) } #' @importFrom stats glm offset anova #' @importFrom MASS negative.binomial model_comparison_lrt <- function(y, offs, theta, group, weights = NULL) { fam <- negative.binomial(theta = theta) mod0 <- glm(y ~ 1 + offset(offs), family = fam, weights = weights) mod1 <- glm(y ~ 1 + offset(offs) + group, family = fam, weights = weights) p_val <- anova(mod0, mod1, test = 'LRT')$'Pr(>Chi)'[2] fold_change <- log2(exp(mod1$coefficients[2])) return(c(p_val, fold_change)) } # fixed overdispersion (theta) # different slopes model_comparison_lrt_free1 <- function(gene, y, theta, model_str, cell_attr, group, weights = NULL, randomize = FALSE) { if (randomize) { y <- sample(y) } mod0 <- glm(as.formula(model_str), data = cell_attr, family = negative.binomial(theta=theta), weights = weights) mod1_str <- paste0(model_str, ' + DE_test_group') mod1 <- glm(as.formula(mod1_str), data = cell_attr, family = negative.binomial(theta=theta), weights = weights) p_val <- anova(mod0, mod1, test = 'Chisq', dispersion = 1)$'Pr(>Chi)'[2] fold_change <- log2(exp(rev(mod1$coefficients)[1])) return(c(p_val, fold_change)) } # fixed overdispersion (theta) # fixed slopes #' @importFrom stats pchisq model_comparison_lrt_free2 <- function(gene, y, theta, model_str, cell_attr, group, weights = NULL, randomize = FALSE) { if (randomize) { y <- sample(y) } mod0 <- glm(as.formula(model_str), data = cell_attr, family = negative.binomial(theta=theta), weights = weights) offs <- log(mod0$fitted.values) - mod0$coefficients[1] mod1 <- glm(y ~ 1 + offset(offs) + group, family = negative.binomial(theta=theta), weights = weights) deviance_diff <- mod0$deviance - mod1$deviance p_val <- pchisq(q = deviance_diff, df = 1, lower.tail = FALSE) fold_change <- log2(exp(rev(mod1$coefficients)[1])) return(c(p_val, fold_change)) } # fixed overdispersion (theta) # different per-group slopes #' @importFrom stats predict model_comparison_lrt_free3 <- function(gene, y, theta, model_str, cell_attr, group, weights = NULL, randomize = FALSE) { if (randomize) { y <- sample(y) } mod0 <- glm(as.formula(model_str), data = cell_attr, family = negative.binomial(theta=theta), weights = weights) mod1_str <- paste(c('y ~', '(', gsub('^y ~ ', '', model_str), ') : DE_test_group + DE_test_group'), collapse=' ') mod1 <- glm(as.formula(mod1_str), data = cell_attr, family = negative.binomial(theta=theta), weights = weights) p_val <- anova(mod0, mod1, test = 'Chisq', dispersion = 1)$'Pr(>Chi)'[2] # to get fold change, predict data tmp.ca0 <- cell_attr tmp.ca0$DE_test_group <- factor(0) tmp.ca1 <- cell_attr tmp.ca1$DE_test_group <- factor(1) fold_change <- log2(median(predict(mod1, newdata = tmp.ca1, type = 'response')/predict(mod0, newdata = tmp.ca0, type = 'response'))) return(c(p_val, fold_change)) } model_comparison_lrt_free <- function(gene, y, theta, model_str, cell_attr, group, weights = NULL) { #print(gene) # model 0 #mod0 <- MASS::glm.nb(as.formula(model_str), data = cell_attr, weights = weights) #fit1 <- glm(as.formula(model_str), data = cell_attr, family = poisson, weights = weights) #theta1 <- as.numeric(x = theta.ml(y = y, mu = fit1$fitted, weights = weights)) #theta1b <- max(0.1, theta1) mod0 <- 0 try(mod0 <- glm(as.formula(model_str), data = cell_attr, family = negative.binomial(theta=theta), weights = weights), silent = TRUE) if (class(mod0)[1] == 'numeric') { print('mod0 failed') browser() } # model 1 #mod1_str <- paste(c('y ~', '(', gsub('^y ~ ', '', model_str), ') : DE_test_group'), collapse=' ') #mod1_str <- paste0(model_str, ' + DE_test_group') #mod1 <- MASS::glm.nb(as.formula(mod1_str), data = cell_attr, weights = weights) #fit2 <- glm(as.formula(mod1_str), data = cell_attr, family = poisson, weights = weights) #theta2 <- as.numeric(x = theta.ml(y = y, mu = fit2$fitted, weights = weights)) #theta2b <- max(0.1, theta2) #mod1 <- 0 #try(mod1 <- glm(as.formula(mod1_str), data = cell_attr, family = negative.binomial(theta=theta), weights = weights), silent = TRUE) #if (class(mod1)[1] == 'numeric') { # print('mod1 failed') # browser() #} #if (sum(y[group==0]) == 0 | sum(y[group==1]) == 0) { # print(theta1) # print(theta1b) # print(theta2) # print(theta2b) #print(anova(mod0, mod1, test = 'Chisq')) #browser() #} #print(mod0) #print(mod1) #print(anova(mod0, mod1, test = 'Chisq')) #p_val <- anova(mod0, mod1, test = 'Chisq')$'Pr(Chi)'[2] #p_val <- anova(mod0, mod1, test = 'Chisq')$'Pr(>Chi)'[2] #fold_change <- log2(exp(rev(mod1$coefficients)[1])) # alternative model 1 and p-value calculation #mod0.o <- glm(y ~ 1 + offset(log(mod0$fitted.values)), family = negative.binomial(theta=theta), weights = weights) #offs <- predict(mod0, newdata = cell_attr) - mod0$coefficients[1] offs <- log(mod0$fitted.values) - mod0$coefficients[1] mod1.o <- glm(y ~ 1 + offset(offs) + group, family = negative.binomial(theta=theta), weights = weights) grp.intercept <- mod1.o$coefficients if (grp.intercept[1] > grp.intercept[2]) { p_val <- summary(mod1.o)$coefficients[1, 4] fold_change <- log2(exp(diff(grp.intercept))) } else { p_val <- summary(mod1.o)$coefficients[2, 4] fold_change <- log2(exp(diff(grp.intercept))) } #mod1.o <- glm(y ~ 1 + offset(log(mod0$fitted.values)) + group, family = negative.binomial(theta=theta), weights = weights) #p_val <- anova(mod0.o, mod1.o, test = 'Chisq')$'Pr(>Chi)'[2] #deviance_diff <- sum(residuals(mod0, type='deviance')^2) - sum(residuals(mod1.o, type='deviance')^2) #p_val <- pchisq(q = deviance_diff, df = 1, lower.tail = FALSE) #fold_change <- log2(exp(rev(mod1.o$coefficients)[1])) # if (mean(y[group==0]) > 0.03 & mean(y[group==1]) == 0) { if (gene == 'OGFOD1') { browser() } return(c(p_val, fold_change)) } #' @importFrom stats t.test model_comparison_ttest <- function(y, group) { tt <- t.test(y ~ group) return(c(tt$p.value, diff(tt$estimate))) } #' Non-parametric differential expression test for sparse non-negative data #' #' @param y A matrix of counts; must be (or inherit from) class dgCMatrix; genes are row, #' cells are columns #' @param group_labels The group labels (e.g. cluster identities); #' will be converted to factor #' @param compare Specifies which groups to compare, see details; default is 'each_vs_rest' #' @param R The number of random permutations used to derive the p-values; default is 99 #' @param log2FC_th Threshold to remove genes from testing; absolute log2FC must be at least #' this large for a gene to be tested; default is \code{log2(1.2)} #' @param mean_th Threshold to remove genes from testing; gene mean must be at least this #' large for a gene to be tested; default is 0.05 #' @param cells_th Threshold to remove genes from testing; gene must be detected (non-zero count) #' in at least this many cells in the group with higher mean; default is 5 #' @param only_pos Test only genes with positive fold change (mean in group 1 > mean in group2); #' default is FALSE #' @param only_top_n Test only the this number of genes from both ends of the log2FC spectrum #' after all of the above filters have been applied; useful to get only the top markers; #' only used if set to a numeric value; default is NULL #' @param mean_type Which type of mean to use; if \code{'geometric'} (default) the geometric mean is #' used; to avoid \code{log(0)} we use \code{log1p} to add 1 to all counts and log-transform, #' calculate the arithmetic mean, and then back-transform and subtract 1 using \code{exp1m}; if #' this parameter is set to \code{'arithmetic'} the data is used as is #' @param verbosity Integer controlling how many messages the function prints; #' 0 is silent, 1 (default) is not #' #' @return Data frame of results #' #' @section Details: #' This model-free test is applied to each gene (row) individually but is #' optimized to make use of the efficient sparse data representation of #' the input. A permutation null distribution us used to assess the #' significance of the observed difference in mean between two groups. #' #' The observed difference in mean is compared against a distribution #' obtained by random shuffling of the group labels. For each gene every #' random permutation yields a difference in mean and from the population of #' these background differences we estimate a mean and standard #' deviation for the null distribution. #' This mean and standard deviation are used to turn the observed #' difference in mean into a z-score and then into a p-value. Finally, #' all p-values (for the tested genes) are adjusted using the Benjamini & Hochberg #' method (fdr). The log2FC values in the output are \code{log2(mean1 / mean2)}. #' Empirical p-values are also calculated: \code{emp_pval = (b + 1) / (R + 1)} #' where b is the number of times the absolute difference in mean from a random #' permutation is at least as large as the absolute value of the observed difference #' in mean, R is the number of random permutations. This is an upper bound of #' the real empirical p-value that would be obtained by enumerating all possible #' group label permutations. #' #' There are multiple ways the group comparisons can be specified based on the compare #' parameter. The default, \code{'each_vs_rest'}, does multiple comparisons, one per #' group vs all remaining cells. \code{'all_vs_all'}, also does multiple comparisons, #' covering all groups pairs. If compare is set to a length two character vector, e.g. #' \code{c('T-cells', 'B-cells')}, one comparison between those two groups is done. #' To put multiple groups on either side of a single comparison, use a list of length two. #' E.g. \code{compare = list(c('cluster1', 'cluster5'), c('cluster3'))}. #' #' @import Matrix #' @importFrom matrixStats rowMeans2 rowSds #' @importFrom stats p.adjust pnorm #' #' @export #' #' @examples #' \donttest{ #' clustering <- 1:ncol(pbmc) %% 2 #' vst_out <- vst(pbmc, return_corrected_umi = TRUE) #' de_res <- diff_mean_test(y = vst_out$umi_corrected, group_labels = clustering) #' } #' diff_mean_test <- function(y, group_labels, compare = 'each_vs_rest', R = 99, log2FC_th = log2(1.2), mean_th = 0.05, cells_th = 5, only_pos = FALSE, only_top_n = NULL, mean_type = 'geometric', verbosity = 1) { if (is.na(match(x = mean_type, table = c('geometric', 'arithmetic')))) { stop('mean_type must be geometric or arithmetic') } if (!inherits(x = y, what = 'dgCMatrix')) { stop('y must be a dgCMatrix') } if (R < 13) { stop('R must be at least 13') } if (!is.null(only_top_n) & (!is.numeric(only_top_n) | length(only_top_n) > 1)) { stop('only_top_n must be NULL or a single numeric value') } group_labels <- droplevels(as.factor(group_labels)) lab_tab <- table(group_labels) group_levels <- levels(group_labels) G <- length(group_levels) if (length(group_labels) != ncol(y)) { stop('length of group labels must be equal to the number of columns in y') } if (verbosity > 0) { message('Non-parametric DE test for count data') message(sprintf('Using %s mean and %d random permutations', mean_type, R)) message('Input: ', nrow(y), ' genes, ', ncol(y), ' cells; ', G, ' groups') } # Set up the comparisons we want to do; each comparison is a list # name1, name2, labels grp1, labels grp2 if (compare[1] == 'each_vs_rest' && G == 2) { compare <- group_levels if (verbosity > 0) { message('There are only two groups in the data. Changing compare argument from "each_vs_rest" to group levels') } } if (compare[1] == 'each_vs_rest') { comparisons <- lapply(group_levels, function(x) list(x, 'rest', x, setdiff(group_levels, x))) } else if (compare[1] == 'all_vs_all') { comparisons <- list() for (i in 1:(G-1)) { for (j in (i+1):G) { comparisons[[length(comparisons) + 1]] <- list(group_levels[i], group_levels[j], group_levels[i], group_levels[j]) } } } else if (inherits(x = compare, what = 'character') && length(compare) == 2 && all(compare %in% group_levels)) { if (compare[1] == compare[2]) { stop('Group 1 and 2 need to be different - please check your compare argument') } comparisons <- list(list(compare[1], compare[2], compare[1], compare[2])) } else if (inherits(x = compare, what = 'list') && length(compare) == 2 && all(unlist(lapply(compare, inherits, what = 'character')))) { compare <- lapply(compare, unique) if (length(intersect(compare[[1]], compare[[2]])) > 0) { stop('Intersection between group 1 and 2 - please check your compare argument') } comparisons <- list(list('group1', 'group2', compare[[1]], compare[[2]])) } else { stop("Make sure the compare argument is 'each_vs_rest' or 'all_vs_all' or a length 2 character vector with both entries present in the group_labels argument or a list of length 2 with each entry being a character vector of group labels") } # for all the genes, get the number of non-zero observations per group cells <- row_nonzero_count_grouped_dgcmatrix(matrix = y, group = group_labels) # if we want to use the geometric mean, it's fastest to convert all counts to # log1p upfront, then use expm1 of arithmetic mean later on if (mean_type == 'geometric') { y@x <- log(y@x + 1) means <- row_mean_grouped_dgcmatrix(matrix = y, group = group_labels, shuffle = FALSE) } else { means <- row_mean_grouped_dgcmatrix(matrix = y, group = group_labels, shuffle = FALSE) } # Run the test for each comparison res_lst <- lapply(comparisons, function(comp) { # we might only be using a subset of the input cells; set up here sel_columns1 <- group_labels %in% comp[[3]] sel_columns2 <- group_labels %in% comp[[4]] sel_columns <- sel_columns1 | sel_columns2 comp_group_labels <- factor(sel_columns2[sel_columns]) if (verbosity > 0) { message(sprintf('Comparing %s (group1, N = %d) to %s (group2, N = %d)', comp[[1]], sum(sel_columns1), comp[[2]], sum(sel_columns2))) } if (sum(sel_columns1) == 0 || sum(sel_columns2) == 0) { return() } comp_cells <- do.call(cbind, lapply(comp[3:4], function(x) combine_counts(cells, x))) comp_means <- do.call(cbind, lapply(comp[3:4], function(x) combine_means(means, lab_tab, x, mean_type))) res <- data.frame(gene = rownames(means), group1 = comp[[1]], mean1 = comp_means[, 1], cells1 = comp_cells[, 1], group2 = comp[[2]], mean2 = comp_means[, 2], cells2 = comp_cells[, 2]) res$mean_diff <- res$mean1 - res$mean2 res$log2FC <- log2(res$mean1 / res$mean2) # remove genes according to the filters if (log2FC_th > 0 || mean_th > 0 || cells_th > 0 || only_pos || !is.null(only_top_n)) { sel1 <- abs(res$log2FC) >= log2FC_th sel2 <- res$mean1 >= mean_th | res$mean2 >= mean_th sel3 <- (res$log2FC >= 0 & res$cells1 >= cells_th) | (res$log2FC <= 0 & res$cells2 >= cells_th) if (only_pos) { sel4 <- res$log2FC > 0 } else { sel4 <- TRUE } res <- res[sel1 & sel2 & sel3 & sel4, , drop = FALSE] if (!is.null(only_top_n)) { sel0 <- rank(-res$log2FC) <= only_top_n if (!only_pos) { sel0 <- sel0 | rank(res$log2FC) <= only_top_n } res <- res[sel0, , drop = FALSE] } if (verbosity > 0) { message(sprintf('Keeping %d genes after initial filtering', nrow(res))) } # handle the case where no genes remain after filtering if (nrow(res) == 0) { return(res) } } # now get the empirical null distribution for mean_diff y_ss <- y[rownames(res), sel_columns, drop = FALSE] if (mean_type == 'geometric') { mean_diff_rnd <- do.call(cbind, lapply(1:R, function(i) { means_r <- expm1(row_mean_grouped_dgcmatrix(matrix = y_ss, group = comp_group_labels, shuffle = TRUE)) means_r[, 1, drop = FALSE] - means_r[, 2, drop = FALSE] })) } else { mean_diff_rnd <- do.call(cbind, lapply(1:R, function(i) { means_r <- row_mean_grouped_dgcmatrix(matrix = y_ss, group = comp_group_labels, shuffle = TRUE) means_r[, 1, drop = FALSE] - means_r[, 2, drop = FALSE] })) } # use null distribution to get empirical p-values # also approximate null with normal and derive z-scores and p-values res$emp_pval <- (rowSums((abs(mean_diff_rnd) - abs(res$mean_diff)) >= 0) + 1) / (R + 1) res$emp_pval_adj <- p.adjust(res$emp_pval, method = 'BH') #res$zscore <- (res$mean_diff - rowMeans2(mean_diff_rnd)) / rowSds(mean_diff_rnd) sds <- sqrt(rowSums(mean_diff_rnd^2)/(R-1)) res$zscore <- (res$mean_diff - rowMeans2(mean_diff_rnd)) / sds res$pval <- 2 * pnorm(-abs(res$zscore)) res$pval_adj <- p.adjust(res$pval, method = 'BH') if (length(comparisons) > 1) { rownames(res) <- NULL } return(res) }) res <- Reduce(rbind, res_lst) if (length(compare) == 1 && compare == 'each_vs_rest' && !is.null(res)) { res$group1 <- factor(res$group1, levels = group_levels) res$group2 <- factor(res$group2) } if (length(compare) == 1 && compare == 'all_vs_all' && !is.null(res)) { res$group1 <- factor(res$group1, levels = group_levels) res$group2 <- factor(res$group2, levels = group_levels) } return(res) } # helper functions combine_counts <- function(group_counts, columns) { as.matrix(rowSums(group_counts[, columns, drop = FALSE])) } # combine per-group-mean to get the mean spanning multiple groups # in an act of irrational premature optimization, we pass the # log-space mean when mean_type is geometric - need to make sure to # transform with exp1m before returning combine_means <- function(means, n_items, columns, mean_type) { if (length(columns) == 1) { if (mean_type == 'arithmetic') { return(means[, columns, drop = FALSE]) } if (mean_type == 'geometric') { return(expm1(means[, columns, drop = FALSE])) } } means <- means[, columns] n_items <- n_items[columns] tmp <- sweep(x = means, MARGIN = 2, STATS = n_items, FUN = '*') if (mean_type == 'arithmetic') { return(as.matrix(rowSums(tmp) / sum(n_items))) } if (mean_type == 'geometric') { return(as.matrix(expm1(rowSums(tmp) / sum(n_items)))) } } #' Find differentially expressed genes that are conserved across samples #' #' @param y A matrix of counts; must be (or inherit from) class dgCMatrix; genes are rows, #' cells are columns #' @param group_labels The group labels (i.e. clusters or time points); #' will be converted to factor #' @param sample_labels The sample labels; will be converted to factor #' @param balanced Boolean, see details for explanation; default is TRUE #' @param compare Specifies which groups to compare, see details; currently only 'each_vs_rest' #' (the default) is supported #' @param pval_th P-value threshold used to call a gene differentially expressed when summarizing #' the tests per gene #' @param ... Parameters passed to diff_mean_test #' #' @return Data frame of results #' #' @section Details: #' This function calls diff_mean_test repeatedly and aggregates the results per group and gene. #' #' If balanced is TRUE (the default), it is assumed that each sample spans multiple groups, #' as would be the case when merging or integrating samples from the same tissue followed by #' clustering. Here the group labels would be the clusters and cluster markers would have support #' in each sample. #' #' If balanced is FALSE, an unbalanced design is assumed where each sample contributes to one #' group. An example is a time series experiment where some samples are taken from time point #' 1 while other samples are taken from time point 2. The time point would be the group label #' and the goal would be to identify differentially expressed genes between time points that #' are supported by many between-sample comparisons. #' #' Output columns: #' \describe{ #' \item{group1}{Group label of the frist group of cells} #' \item{group2}{Group label of the second group of cells; currently fixed to 'rest'} #' \item{gene}{Gene name (from rownames of input matrix)} #' \item{n_tests}{The number of tests this gene participated in for this group} #' \item{log2FC_min,median,max}{Summary statistics for log2FC across the tests} #' \item{mean1,2_median}{Median of group mean across the tests} #' \item{pval_max}{Maximum of p-values across tests} #' \item{de_tests}{Number of tests that showed this gene having a log2FC going in the same #' direction as log2FC_median and having a p-value <= pval_th} #' } #' #' The output is ordered by group1, -de_tests, -abs(log2FC_median), pval_max #' #' @import Matrix #' @importFrom dplyr n group_by summarise arrange #' @importFrom rlang .data #' #' @export #' #' @examples #' \donttest{ #' clustering <- 1:ncol(pbmc) %% 2 #' sample_id <- 1:ncol(pbmc) %% 3 #' vst_out <- vst(pbmc, return_corrected_umi = TRUE) #' de_res <- diff_mean_test_conserved(y = vst_out$umi_corrected, #' group_labels = clustering, sample_labels = sample_id) #' } #' diff_mean_test_conserved <- function(y, group_labels, sample_labels, balanced = TRUE, compare = 'each_vs_rest', pval_th = 1e-4, ...) { if (!inherits(x = y, what = 'dgCMatrix')) { stop('y must be a dgCMatrix') } group_labels <- droplevels(as.factor(group_labels)) sample_labels <- droplevels(as.factor(sample_labels)) res <- NULL if (compare[1] == 'each_vs_rest') { if (balanced) { res_lst <- lapply(levels(sample_labels), function(sl) { sel <- sample_labels == sl res <- diff_mean_test(y = y[, sel], group_labels = group_labels[sel], compare = compare, ...) if (!is.null(res)) { res$sample <- sl } res }) } else { # fix special case when there are only two groups if (length(levels(group_labels)) == 2) { group_labels_to_do <- levels(group_labels)[1] gl_rest <- levels(group_labels)[2] } else { group_labels_to_do <- levels(group_labels) gl_rest <- 'rest' } # for each group, compare each sample against all samples that are not in group individually res_lst <- lapply(group_labels_to_do, function(gl) { gl_sel <- group_labels == gl samples_in_group <- sample_labels[gl_sel] res_lst <- lapply(unique(samples_in_group), function(sl_in_group) { sl_in_group_sel <- sample_labels == sl_in_group other_samples_not_in_group <- sample_labels[!(gl_sel | sl_in_group_sel)] res_lst <- lapply(unique(other_samples_not_in_group), function(osl_not_in_group) { sel <- (gl_sel & sl_in_group_sel) | (!gl_sel & sample_labels == osl_not_in_group) tmp_group <- c(gl, gl_rest)[as.numeric(!gl_sel & sample_labels == osl_not_in_group) + 1] res <- diff_mean_test(y = y[, sel], group_labels = tmp_group[sel], compare = c(gl, gl_rest), ...) if (!is.null(res)) { res$sample1 <- sl_in_group res$sample2 <- osl_not_in_group } res }) do.call(rbind, res_lst) }) do.call(rbind, res_lst) }) } res <- do.call(rbind, res_lst) levels(res$group1) <- levels(group_labels) } if (!is.null(res)) { res <- group_by(res, .data$group1, .data$group2, .data$gene) %>% summarise(n_tests = n(), log2FC_min = min(.data$log2FC), log2FC_median = median(.data$log2FC), log2FC_max = max(.data$log2FC), mean1_median = median(.data$mean1), mean2_median = median(.data$mean2), pval_max = max(.data$pval), de_tests = sum((sign(.data$log2FC) == sign(.data$log2FC_median)) & (.data$pval <= pval_th)), .groups = 'drop') %>% arrange(.data$group1, -.data$de_tests, -abs(.data$log2FC_median), .data$pval_max) } return(res) } # non-parametric differential expression test np_de_test <- function(y, labels, N = 100, S = 100, randomize = FALSE) { if (!inherits(x = y, what = 'matrix')) { stop('y must be a matrix') } labels <- droplevels(as.factor(labels)) if (length(levels(labels)) > 2) { stop('only two groups can be compared') } if (N < 50 || S < 50) { stop('N and S must both be at least 50') } if (ncol(y) != length(labels)) { stop('number of columns in y and length of label vector must match') } labels <- as.integer(labels)-1L if (randomize) { res <- apply(y, 1, function(x) distribution_shift(mean_boot_grouped(x, sample(labels), N = 100, S = 100))) } else { res <- apply(y, 1, function(x) distribution_shift(mean_boot_grouped(x, labels, N = 100, S = 100))) } #res <- data.frame(t(res)) #colnames(res) <- c('q16_a', 'q50_a', 'q84_a', 'q16_b', 'q50_b', 'q84_b', 'div', 'z') res <- data.frame(t(res[c(2, 5, 7, 8), ])) colnames(res) <- c('mu1', 'mu2', 'div', 'z') gene <- rownames(res) res <- cbind(gene, res) rownames(res) <- NULL return(res) } sctransform/R/data.R0000644000176200001440000000155515123222545014057 0ustar liggesusers#' Peripheral Blood Mononuclear Cells (PBMCs) #' #' UMI counts for a subset of cells freely available from 10X Genomics #' #' @format A sparse matrix (dgCMatrix, see Matrix package) of molecule counts. #' There are 914 rows (genes) and 283 columns (cells). This is a downsampled #' version of a 3K PBMC dataset available from 10x Genomics. #' #' @source \url{https://www.10xgenomics.com/datasets/3-k-pbm-cs-from-a-healthy-donor-1-standard-1-1-0} "pbmc" #' Transformation functions for umify #' #' The functions have been trained on various public data sets and relate quantile #' values to log-counts. Here the expected values at various points are given. #' #' @format A list of length two. The first element is a data frame with group, quantile and #' log-counts values. The second element is a vector of breaks to be used with cut to group #' observations. 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#ifdef RCPP_USE_GLOBAL_ROSTREAM Rcpp::Rostream& Rcpp::Rcout = Rcpp::Rcpp_cout_get(); Rcpp::Rostream& Rcpp::Rcerr = Rcpp::Rcpp_cerr_get(); #endif // row_mean_dgcmatrix NumericVector row_mean_dgcmatrix(S4 matrix); RcppExport SEXP _sctransform_row_mean_dgcmatrix(SEXP matrixSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< S4 >::type matrix(matrixSEXP); rcpp_result_gen = Rcpp::wrap(row_mean_dgcmatrix(matrix)); return rcpp_result_gen; END_RCPP } // row_mean_grouped_dgcmatrix NumericMatrix row_mean_grouped_dgcmatrix(S4 matrix, IntegerVector group, bool shuffle); RcppExport SEXP _sctransform_row_mean_grouped_dgcmatrix(SEXP matrixSEXP, SEXP groupSEXP, SEXP shuffleSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< S4 >::type matrix(matrixSEXP); Rcpp::traits::input_parameter< IntegerVector >::type group(groupSEXP); Rcpp::traits::input_parameter< bool >::type shuffle(shuffleSEXP); rcpp_result_gen = Rcpp::wrap(row_mean_grouped_dgcmatrix(matrix, group, shuffle)); return rcpp_result_gen; END_RCPP } // row_gmean_dgcmatrix NumericVector row_gmean_dgcmatrix(S4 matrix, double eps); RcppExport SEXP _sctransform_row_gmean_dgcmatrix(SEXP matrixSEXP, SEXP epsSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< S4 >::type matrix(matrixSEXP); Rcpp::traits::input_parameter< double >::type eps(epsSEXP); rcpp_result_gen = Rcpp::wrap(row_gmean_dgcmatrix(matrix, eps)); return rcpp_result_gen; END_RCPP } // row_gmean_grouped_dgcmatrix NumericMatrix row_gmean_grouped_dgcmatrix(S4 matrix, IntegerVector group, double eps, bool shuffle); RcppExport SEXP _sctransform_row_gmean_grouped_dgcmatrix(SEXP matrixSEXP, SEXP groupSEXP, SEXP epsSEXP, SEXP shuffleSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< S4 >::type matrix(matrixSEXP); Rcpp::traits::input_parameter< IntegerVector >::type group(groupSEXP); Rcpp::traits::input_parameter< double >::type eps(epsSEXP); Rcpp::traits::input_parameter< bool >::type shuffle(shuffleSEXP); rcpp_result_gen = Rcpp::wrap(row_gmean_grouped_dgcmatrix(matrix, group, eps, shuffle)); return rcpp_result_gen; END_RCPP } // row_nonzero_count_dgcmatrix IntegerVector row_nonzero_count_dgcmatrix(S4 matrix); RcppExport SEXP _sctransform_row_nonzero_count_dgcmatrix(SEXP matrixSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< S4 >::type matrix(matrixSEXP); rcpp_result_gen = Rcpp::wrap(row_nonzero_count_dgcmatrix(matrix)); return rcpp_result_gen; END_RCPP } // row_nonzero_count_grouped_dgcmatrix IntegerMatrix row_nonzero_count_grouped_dgcmatrix(S4 matrix, IntegerVector group); RcppExport SEXP _sctransform_row_nonzero_count_grouped_dgcmatrix(SEXP matrixSEXP, SEXP groupSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< S4 >::type matrix(matrixSEXP); Rcpp::traits::input_parameter< IntegerVector >::type group(groupSEXP); rcpp_result_gen = Rcpp::wrap(row_nonzero_count_grouped_dgcmatrix(matrix, group)); return rcpp_result_gen; END_RCPP } // row_var_dgcmatrix NumericVector row_var_dgcmatrix(NumericVector x, IntegerVector i, int rows, int cols); RcppExport SEXP _sctransform_row_var_dgcmatrix(SEXP xSEXP, SEXP iSEXP, SEXP rowsSEXP, SEXP colsSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericVector >::type x(xSEXP); Rcpp::traits::input_parameter< IntegerVector >::type i(iSEXP); Rcpp::traits::input_parameter< int >::type rows(rowsSEXP); Rcpp::traits::input_parameter< int >::type cols(colsSEXP); rcpp_result_gen = Rcpp::wrap(row_var_dgcmatrix(x, i, rows, cols)); return rcpp_result_gen; END_RCPP } // grouped_mean_diff_per_row NumericVector grouped_mean_diff_per_row(NumericMatrix x, IntegerVector group, bool shuffle); RcppExport SEXP _sctransform_grouped_mean_diff_per_row(SEXP xSEXP, SEXP groupSEXP, SEXP shuffleSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericMatrix >::type x(xSEXP); Rcpp::traits::input_parameter< IntegerVector >::type group(groupSEXP); Rcpp::traits::input_parameter< bool >::type shuffle(shuffleSEXP); rcpp_result_gen = Rcpp::wrap(grouped_mean_diff_per_row(x, group, shuffle)); return rcpp_result_gen; END_RCPP } // mean_boot NumericVector mean_boot(NumericVector x, int N, int S); RcppExport SEXP _sctransform_mean_boot(SEXP xSEXP, SEXP NSEXP, SEXP SSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericVector >::type x(xSEXP); Rcpp::traits::input_parameter< int >::type N(NSEXP); Rcpp::traits::input_parameter< int >::type S(SSEXP); rcpp_result_gen = Rcpp::wrap(mean_boot(x, N, S)); return rcpp_result_gen; END_RCPP } // mean_boot_grouped NumericMatrix mean_boot_grouped(NumericVector x, IntegerVector group, int N, int S); RcppExport SEXP _sctransform_mean_boot_grouped(SEXP xSEXP, SEXP groupSEXP, SEXP NSEXP, SEXP SSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericVector >::type x(xSEXP); Rcpp::traits::input_parameter< IntegerVector >::type group(groupSEXP); Rcpp::traits::input_parameter< int >::type N(NSEXP); Rcpp::traits::input_parameter< int >::type S(SSEXP); rcpp_result_gen = Rcpp::wrap(mean_boot_grouped(x, group, N, S)); return rcpp_result_gen; END_RCPP } // distribution_shift NumericVector distribution_shift(NumericMatrix x); RcppExport SEXP _sctransform_distribution_shift(SEXP xSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericMatrix >::type x(xSEXP); rcpp_result_gen = Rcpp::wrap(distribution_shift(x)); return rcpp_result_gen; END_RCPP } // qpois_reg List qpois_reg(NumericMatrix X, NumericVector Y, const double tol, const int maxiters, const double minphi, const bool returnfit); RcppExport SEXP _sctransform_qpois_reg(SEXP XSEXP, SEXP YSEXP, SEXP tolSEXP, SEXP maxitersSEXP, SEXP minphiSEXP, SEXP returnfitSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericMatrix >::type X(XSEXP); Rcpp::traits::input_parameter< NumericVector >::type Y(YSEXP); Rcpp::traits::input_parameter< const double >::type tol(tolSEXP); Rcpp::traits::input_parameter< const int >::type maxiters(maxitersSEXP); Rcpp::traits::input_parameter< const double >::type minphi(minphiSEXP); Rcpp::traits::input_parameter< const bool >::type returnfit(returnfitSEXP); rcpp_result_gen = Rcpp::wrap(qpois_reg(X, Y, tol, maxiters, minphi, returnfit)); return rcpp_result_gen; END_RCPP } static const R_CallMethodDef CallEntries[] = { {"_sctransform_row_mean_dgcmatrix", (DL_FUNC) &_sctransform_row_mean_dgcmatrix, 1}, {"_sctransform_row_mean_grouped_dgcmatrix", (DL_FUNC) &_sctransform_row_mean_grouped_dgcmatrix, 3}, {"_sctransform_row_gmean_dgcmatrix", (DL_FUNC) &_sctransform_row_gmean_dgcmatrix, 2}, {"_sctransform_row_gmean_grouped_dgcmatrix", (DL_FUNC) &_sctransform_row_gmean_grouped_dgcmatrix, 4}, {"_sctransform_row_nonzero_count_dgcmatrix", (DL_FUNC) &_sctransform_row_nonzero_count_dgcmatrix, 1}, {"_sctransform_row_nonzero_count_grouped_dgcmatrix", (DL_FUNC) &_sctransform_row_nonzero_count_grouped_dgcmatrix, 2}, {"_sctransform_row_var_dgcmatrix", (DL_FUNC) &_sctransform_row_var_dgcmatrix, 4}, {"_sctransform_grouped_mean_diff_per_row", (DL_FUNC) &_sctransform_grouped_mean_diff_per_row, 3}, {"_sctransform_mean_boot", (DL_FUNC) &_sctransform_mean_boot, 3}, {"_sctransform_mean_boot_grouped", (DL_FUNC) &_sctransform_mean_boot_grouped, 4}, {"_sctransform_distribution_shift", (DL_FUNC) &_sctransform_distribution_shift, 1}, {"_sctransform_qpois_reg", (DL_FUNC) &_sctransform_qpois_reg, 6}, {NULL, NULL, 0} }; RcppExport void R_init_sctransform(DllInfo *dll) { R_registerRoutines(dll, NULL, CallEntries, NULL, NULL); R_useDynamicSymbols(dll, FALSE); } sctransform/src/utils.cpp0000644000176200001440000002447514731054755015275 0ustar liggesusers// [[Rcpp::depends(RcppArmadillo)]] #include "RcppArmadillo.h" #include "math.h" using namespace Rcpp; // from Rcpp gallery https://gallery.rcpp.org/articles/stl-random-shuffle/ // wrapper around R's RNG such that we get a uniform distribution over // [0,n) as required by the STL algorithm // inline int randWrapper(const int n) { return floor(unif_rand()*n); } std::random_device rd; std::mt19937 randWrapper(rd()); // [[Rcpp::export]] NumericVector row_mean_dgcmatrix(S4 matrix) { NumericVector x = matrix.slot("x"); IntegerVector i = matrix.slot("i"); IntegerVector dim = matrix.slot("Dim"); int rows = dim[0]; int cols = dim[1]; NumericVector ret(rows, 0.0); int x_length = x.length(); for (int k=0; k(dn[0]); } return ret; } // [[Rcpp::export]] NumericMatrix row_mean_grouped_dgcmatrix(S4 matrix, IntegerVector group, bool shuffle) { NumericVector x = matrix.slot("x"); IntegerVector i = matrix.slot("i"); IntegerVector p = matrix.slot("p"); IntegerVector dim = matrix.slot("Dim"); int rows = dim[0]; int cols = dim[1]; CharacterVector levs = group.attr("levels"); int groups = levs.length(); NumericMatrix ret(rows, groups); IntegerVector groupsize(groups, 0); int x_length = x.length(); if (shuffle) { group = clone(group); std::shuffle(group.begin(), group.end(), randWrapper); } int col = 0; for (int k=0; k=p[col]) { ++col; ++groupsize[group[col-1]-1]; } ret(i[k], group[col-1]-1) += x[k]; } while (col < cols) { ++col; ++groupsize[group[col-1]-1]; } for (int j=0; j(dn[0]); } return ret; } // [[Rcpp::export]] NumericVector row_gmean_dgcmatrix(S4 matrix, double eps) { NumericVector x = matrix.slot("x"); IntegerVector i = matrix.slot("i"); IntegerVector dim = matrix.slot("Dim"); int rows = dim[0]; int cols = dim[1]; NumericVector ret(rows, 0.0); IntegerVector nzero(rows, cols); int x_length = x.length(); double log_eps = log(eps); for (int k=0; k(dn[0]); } return ret; } // [[Rcpp::export]] NumericMatrix row_gmean_grouped_dgcmatrix(S4 matrix, IntegerVector group, double eps, bool shuffle) { NumericVector x = matrix.slot("x"); IntegerVector i = matrix.slot("i"); IntegerVector p = matrix.slot("p"); IntegerVector dim = matrix.slot("Dim"); int rows = dim[0]; int cols = dim[1]; CharacterVector levs = group.attr("levels"); int groups = levs.length(); NumericMatrix ret(rows, groups); IntegerVector groupsize(groups, 0); int x_length = x.length(); IntegerMatrix nonzero(rows, groups); double log_eps = log(eps); if (shuffle) { group = clone(group); std::shuffle(group.begin(), group.end(), randWrapper); } int col = 0; for (int k=0; k=p[col]) { ++col; ++groupsize[group[col-1]-1]; } ret(i[k], group[col-1]-1) += log(x[k] + eps); ++nonzero(i[k], group[col-1]-1); } while (col < cols) { ++col; ++groupsize[group[col-1]-1]; } for (int j=0; j(dn[0]); } return ret; } // [[Rcpp::export]] IntegerVector row_nonzero_count_dgcmatrix(S4 matrix) { IntegerVector i = matrix.slot("i"); IntegerVector dim = matrix.slot("Dim"); int rows = dim[0]; IntegerVector ret(rows, 0); int i_len = i.length(); for(int k = 0; k < i_len; ++k) { ret[i[k]]++; } List dn = matrix.slot("Dimnames"); if (dn[0] != R_NilValue) { ret.attr("names") = as(dn[0]); } return ret; } // [[Rcpp::export]] IntegerMatrix row_nonzero_count_grouped_dgcmatrix(S4 matrix, IntegerVector group) { IntegerVector p = matrix.slot("p"); IntegerVector i = matrix.slot("i"); int i_length = i.length(); IntegerVector dim = matrix.slot("Dim"); int rows = dim[0]; CharacterVector levs = group.attr("levels"); int groups = levs.length(); IntegerMatrix ret(rows, groups); int col = 0; for (int k=0; k=p[col]) { ++col; } ret(i[k], group[col-1]-1)++; } colnames(ret) = levs; List dn = matrix.slot("Dimnames"); if (dn[0] != R_NilValue) { rownames(ret) = as(dn[0]); } return ret; } // [[Rcpp::export]] NumericVector row_var_dgcmatrix(NumericVector x, IntegerVector i, int rows, int cols) { NumericVector rowmean(rows, 0.0); int x_length = x.length(); for (int k=0; k(x)); arma::uvec::const_iterator it = indices.begin(); arma::uvec::const_iterator it_end = indices.end(); for (; it != it_end; ++it) { if ((*it) < N) { cs += 1; if ((q0i < 3) & (r0 == qidx[q0i])) { res[q0i] = x[*it]; q0i++; } r0++; } else { cs -= 1; if ((q1i < 3) & (r1 == qidx[q1i])) { res[q1i+3] = x[*it]; q1i++; } r1++; } cs_sum += cs; } res[6] = (double) cs_sum / N / N; // add z-score-like score if (res[4] > res[1]) { // second group has higher mean sd0 = res[2] - res[1]; sd1 = res[4] - res[3]; } else { sd0 = res[1] - res[0]; sd1 = res[5] - res[4]; } //res[7] = (res[4] - res[1]) / sqrt(sd0 * sd1); //res[7] = (res[4] - res[1]) / ((sd0 + sd1) / 2); res[7] = (res[4] - res[1]) / sqrt((sd0*sd0 + sd1*sd1) / 2); return res; } // The following function was taken from the Rfast package // with kind permission from the authors. // It has been slightly adopted for our use case here. // [[Rcpp::export]] List qpois_reg(NumericMatrix X, NumericVector Y, const double tol, const int maxiters, const double minphi, const bool returnfit){ const unsigned int n=X.nrow(), pcols=X.ncol(), d=pcols; arma::colvec b_old(d, arma::fill::zeros), b_new(d), L1(d), yhat(n), y(Y.begin(), n, false), m(n), phi(n); arma::vec unique_vals; arma::mat L2, x(X.begin(), n, pcols, false), x_tr(n, pcols); double dif; // Identify the intercept term(s) and initialize the coefficients for(int i=0;itol;){ yhat=x*b_old; m=(exp(yhat)); phi=y-m; L1=x_tr*phi; L2=x.each_col()%m; L2=x_tr*L2; b_new=b_old+solve(L2,L1,arma::solve_opts::fast); dif=sum(abs(b_new-b_old)); b_old=b_new; if(++ij==maxiters) break; } double p=sum(arma::square(phi)/m)/(n-pcols); NumericVector coefs = NumericVector(b_new.begin(), b_new.end()); coefs.names() = colnames(X); List l; l["coefficients"]=coefs; l["phi"]=p; l["theta.guesstimate"]=mean(m)/(std::max(p, minphi)-1); if(returnfit){ l["fitted"]=NumericVector(m.begin(), m.end()); } return l; } sctransform/src/Makevars.win0000644000176200001440000000010114731054755015675 0ustar liggesusersCXX_STD = CXX17 PKG_LIBS = $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS) sctransform/src/Makevars0000644000176200001440000000010114731054755015101 0ustar liggesusersCXX_STD = CXX17 PKG_LIBS = $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS) sctransform/NAMESPACE0000644000176200001440000000334115123210676014036 0ustar liggesusers# Generated by roxygen2: do not edit by hand export(correct) export(correct_counts) export(diff_mean_test) export(diff_mean_test_conserved) export(generate) export(get_model_var) export(get_residual_var) export(get_residuals) export(plot_model) export(plot_model_pars) export(smooth_via_pca) export(umify) export(vst) import(Matrix) import(ggplot2) import(reshape2) importFrom(MASS,glm.nb) importFrom(MASS,negative.binomial) importFrom(MASS,theta.ml) importFrom(MASS,theta.mm) importFrom(dplyr,arrange) importFrom(dplyr,case_when) importFrom(dplyr,group_by) importFrom(dplyr,mutate) importFrom(dplyr,n) importFrom(dplyr,pull) importFrom(dplyr,summarise) importFrom(future,nbrOfWorkers) importFrom(future.apply,future_lapply) importFrom(graphics,abline) importFrom(graphics,par) importFrom(graphics,plot) importFrom(gridExtra,grid.arrange) importFrom(magrittr,"%>%") importFrom(matrixStats,rowMeans2) importFrom(matrixStats,rowSds) importFrom(matrixStats,rowVars) importFrom(methods,as) importFrom(rlang,.data) importFrom(stats,aggregate) importFrom(stats,anova) importFrom(stats,approx) importFrom(stats,approxfun) importFrom(stats,as.formula) importFrom(stats,bw.SJ) importFrom(stats,bw.nrd0) importFrom(stats,density) importFrom(stats,df.residual) importFrom(stats,glm) importFrom(stats,glm.fit) importFrom(stats,ksmooth) importFrom(stats,mad) importFrom(stats,median) importFrom(stats,model.matrix) importFrom(stats,offset) importFrom(stats,p.adjust) importFrom(stats,pchisq) importFrom(stats,pnorm) importFrom(stats,poisson) importFrom(stats,predict) importFrom(stats,t.test) importFrom(stats,var) importFrom(utils,capture.output) importFrom(utils,packageVersion) importFrom(utils,setTxtProgressBar) importFrom(utils,txtProgressBar) useDynLib(sctransform) sctransform/LICENSE0000644000176200001440000010450514731054755013640 0ustar liggesusers GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007 Copyright (C) 2007 Free Software Foundation, Inc. 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But first, please read . sctransform/NEWS.md0000644000176200001440000001372615123222545013723 0ustar liggesusers# News All notable changes will be documented in this file. ## [0.4.3] - 2025-12-25 - Fixed imports for supportsMulticore() to use parallely [#209](https://github.com/satijalab/sctransform/issues/209) - Switch to future_apply [#210](https://github.com/satijalab/sctransform/issues/210) - Remove deprecated argument 'verbose' in favor of 'verbosity' - Added helper functions for progress bar and parsing formulas ## [0.4.2] - 2025-04-30 ### Fixed - Fixed citation orcid - Fix for future_lapply when using a single core [#201](https://github.com/satijalab/sctransform/issues/201) ## [0.4.1] - 2023-10-18 ### Fixed - Fixed column name setting in `fit_nb_offset` ### Changed - Verbose messages when invoking `v2`: messages are only invoked if verbosity > 1. ## [0.4.0] - 2023-09-18 ### Added - Add `fit_nb_offset` to support vst.flavor='v2' by default ### Fixed - Updated cpp utilities to adhere to C++17 standards (`std::random_shuffle` -> `std::shuffle`) - Handling of extra variables in `latent_var` when `vst.flavor="v2"` ### Changed - Changed `get_nz_median2` to support `genes` argument; thanks @boomanaiden154 and @ScreachingFire. [#155](https://github.com/satijalab/sctransform/pull/155) - Replaced `get_nz_median` with faster alternative `get_nz_median2` across all calls - Removed `get_nz_median` - Updated `make_cell_attr` to be flexible for named vectors; thanks @moi-taga [#171](https://github.com/satijalab/sctransform/pull/171) ## [0.3.5] - 2022-09-21 ### Fixed - Specify required Matrix version to >= 1.5.0 ## [0.3.4] - 2022-08-19 ### Added - Add `make.sparse` to handle `dgCMatrix` coercions ### Fixed - Convert bitwise operators to boolean operators in utils.cpp ## [0.3.3] - 2022-01-13 ### Added - `vst.flavor` argument to `vst()` to allow for invoking running updated regularization (sctransform v2, proposed in [Satija and Choudhary, 2021](https://doi.org/10.1101/2021.07.07.451498). See paper for details. - `scale_factor` to `correct()` to allow for a custom library size when correcting counts ## [0.3.2] - 2021-07-28 ### Added - Add future.seed = TRUE to all `future_lapply()` calls ### Changed - Wrap MASS::theta.ml() in suppressWarnings() ### Fixed - Fix logical comparison of vectors of length one in `diff_mean_test()` ## [0.3.2] - 2020-02-11 ### Added - `compare` argument to the nonparametric differential expression test `diff_mean_test()` to allow for multiple comparisons and various ways to specify which groups to compare - Input checking at various places in `vst()` and `diff_mean_test()` ### Changed - Major speed improvements for `diff_mean_test()` - Changed the `labels` argument to `group_labels` in `diff_mean_test()` ### Fixed - Fix bug where factors in cell attributes gave error when checking for NA, NaN, inf ## [0.3.2] - 2020-12-16 ### Added - Ability to control the values of latent variables when calculating corrected counts - Offset model as method, including the ability to use a single estimated theta for all genes - Nonparametric differential expression test for sparse non-negative data ### Changed - Improve poor coefficient initialization in quasi poisson regression - When plotting model, do not show density by default; change bandwidth to `bw.nrd0` - Updates to C++ code to use sparse matrices as S4 objects - Add check for NA, NaN, Inf values in cell attributes ### Fixed - Remove biocViews from DESCRIPTION - not needed and was causing problems with deploying shiny apps - Fix bug where a coefficient was given the wrong name when using `glmGamPoi` (only affected runs with a batch variable set) ## [0.3.1] - 2020-10-08 ### Added - Add a `qpoisson` method for parameter estimation that uses fast Rcpp quasi poisson regression where possible (based on `Rfast` package); this adds `RcppArmadillo` dependency ### Changed - Remove `poisson_fast` method (replaced by `qpoisson`) - Use `matrixStats` package and remove `RcppEigen` dependency - Use quasi poisson regression where possible - Define cell detection event as counts >= 0.01 (instead of > 0) - this only matters to people playing around with fractional counts (see [issue #65](https://github.com/satijalab/sctransform/issues/65)) - Internal code restructuring and improvements ### Fixed - Fix inefficiency of using `match.call()` in `vst()` when called via `do.call` ## [0.3] - 2020-09-19 ### Added - Add support for `glmGamPoi` as method to estimate the model parameters; thanks @yuhanH for his pull request - Add option to use `theta.mm` or`theta.ml` to estimate theta when `method = 'poisson'` or `method = 'nb_fast'` - Add a `poisson_fast` method for parameter estimation that uses the `speedglm` package and `theta.mm` by default - Add ability to plot overdispersion factor in `plot_model_pars` - Add and return time stamps at various steps in the `vst` function - Add functions to calculate grouped arithmetic and geometric mean per row for sparse matrices (`dgCMatrix`) - might come in handy some time ### Changed - Default theta regularization is now based on overdispersion factor (`1 + m / theta` where m is the geometric mean of the observed counts) not `log10(theta)`; old behavior available via `theta_regularization` parameter - Refactored model fitting code - is now more efficient when using parallel processing - Changed how message and progress bar output is controlled; integer `verbosity` parameter controls all output: 0 for no output, 1 for only messages, 2 for messages and progress bars - Increased default bin size (genes being processed simultaneously) from 256 to 500 - Better input checking for cell attributes; more efficient calculation of missing ones ### Fixed - Some non-regularized model parameters were not plotted ## [0.2.1] - 2019-12-17 ### Added - Add function to generate data given the output of a vst run - Add cpp support for dense integer matrices - Minimum variance parameter added to vst function ## [0.2.0] - 2019-04-12 ### Added - Rcpp versions of utility functions - Helper functions to get corrected UMI and variance of pearson residuals for large UMI matrices ### Changed - lots of things sctransform/inst/0000755000176200001440000000000015120100760013560 5ustar liggesuserssctransform/inst/CITATION0000644000176200001440000000314715120100760014722 0ustar liggesuserscitHeader("To cite sctransform in publications, please use:") bibentry(bibtype = "article", author = c(person(given = "Christoph", family = "Hafemeister", email = "christoph.hafemeister@nyu.edu", role = "aut", comment = c(ORCID = "0000-0001-6365-8254")), person(given = "Rahul", family = "Satija", email = "rsatija@nygenome.org", role = c("aut", "ctb"), comment = c(ORCID = "0000-0001-9448-8833"))), title = "Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression", journal = "Genome Biology", year = "2019", volume = "20", pages = "296", doi = "10.1186/s13059-019-1874-1", url = "https://doi.org/10.1186/s13059-019-1874-1", textVersion = "Hafemeister, C. & Satija, R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol 20, 296 (2019)." ) bibentry(bibtype = "article", author = c(person(given = "Saket", family = "Choudhary", email = "saketc@iitb.ac.in", role = c("aut", "cre"), comment = c(ORCID = "0000-0001-5202-7633")), person(given = "Rahul", family = "Satija", email = "rsatija@nygenome.org", role = c("aut", "ctb"), comment = c(ORCID = "0000-0001-9448-8833"))), title = "Comparison and evaluation of statistical error models for scRNA-seq", journal = "Genome Biology", year = "2022", volume = "23", pages = "20", doi = "10.1186/s13059-021-02584-9", url = "https://doi.org/10.1186/s13059-021-02584-9", textVersion = "Choudhary, S. & Satija, R. Comparison and evaluation of statistical error models for scRNA-seq. Genome Biology 23, 1-20 (2022)." ) sctransform/README.md0000644000176200001440000000534115123222545014076 0ustar liggesusers# sctransform ## R package for normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression The sctransform package was developed by Christoph Hafemeister in [Rahul Satija's lab](https://satijalab.org/) at the New York Genome Center and described in [Hafemeister and Satija, Genome Biology 2019](https://doi.org/10.1186/s13059-019-1874-1). Recent updates are described in [(Choudhary and Satija, Genome Biology, 2022)](https://doi.org/10.1186/s13059-021-02584-9). Core functionality of this package has been integrated into [Seurat](https://satijalab.org/seurat/), an R package designed for QC, analysis, and exploration of single cell RNA-seq data. ## Quick start Installation: ```r # Install sctransform from CRAN install.packages("sctransform") # Or the development version from GitHub: remotes::install_github("satijalab/sctransform", ref="develop") ``` Running sctransform: ```r # Running sctransform on a UMI matrix normalized_data <- sctransform::vst(umi_count_matrix)$y # v1 regularization normalized_data <- sctransform::vst(umi_count_matrix, vst.flavor="v1")$y # Running sctransform on a Seurat object seurat_object <- Seurat::SCTransform(seurat_object) # v1 regularization seurat_object <- Seurat::SCTransform(seurat_object, vst.flavor="v1") ``` ## Help For usage examples see vignettes in inst/doc or use the built-in help after installation `?sctransform::vst` Available vignettes: - [Variance stabilizing transformation](https://htmlpreview.github.io/?https://github.com/satijalab/sctransform/blob/supp_html/supplement/variance_stabilizing_transformation.html) - [Using sctransform in Seurat](https://htmlpreview.github.io/?https://github.com/satijalab/sctransform/blob/supp_html/supplement/seurat.html) - [Examples of how to perform normalization, feature selection, integration, and differential expression with sctransform v2 regularization](https://satijalab.org/seurat/articles/sctransform_vignette) Please use [the issue tracker](https://github.com/satijalab/sctransform/issues) if you encounter a problem ## References - Hafemeister, C. & Satija, R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biology 20, 296 (2019). [https://doi.org/10.1186/s13059-019-1874-1](https://doi.org/10.1186/s13059-019-1874-1). An early version of this work was used in the paper [Developmental diversification of cortical inhibitory interneurons, Nature 555, 2018](https://github.com/ChristophH/in-lineage). - Choudhary, S. & Satija, R. Comparison and evaluation of statistical error models for scRNA-seq. Genome Biology 23.1 (2022). [https://doi.org/10.1186/s13059-021-02584-9](https://doi.org/10.1186/s13059-021-02584-9) sctransform/build/0000755000176200001440000000000015123223607013713 5ustar liggesuserssctransform/build/partial.rdb0000644000176200001440000000007515123223607016042 0ustar liggesusers‹‹àb```b`aeb`b1…À€… H02°0piÖ¼ÄÜÔb C"Éð¸F$7sctransform/man/0000755000176200001440000000000015123222545013367 5ustar liggesuserssctransform/man/get_model_formula.Rd0000644000176200001440000000067315123222545017350 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utils.R \name{get_model_formula} \alias{get_model_formula} \title{Extract model formula from model string} \usage{ get_model_formula(model_str) } \arguments{ \item{model_str}{Model string with 'y' prefix (e.g., 'y ~ log_umi')} } \value{ Formula object without 'y' prefix } \description{ Helper function to convert model string (with 'y' prefix) to formula object } sctransform/man/get_residual_var.Rd0000644000176200001440000000305515123222545017200 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utils.R \name{get_residual_var} \alias{get_residual_var} \title{Return variance of residuals of regularized models} \usage{ get_residual_var( vst_out, umi, residual_type = "pearson", res_clip_range = c(-sqrt(ncol(umi)), sqrt(ncol(umi))), min_variance = vst_out$arguments$min_variance, cell_attr = vst_out$cell_attr, bin_size = 256, verbosity = vst_out$arguments$verbosity ) } \arguments{ \item{vst_out}{The output of a vst run} \item{umi}{The UMI count matrix that will be used} \item{residual_type}{What type of residuals to return; can be 'pearson' or 'deviance'; default is 'pearson'} \item{res_clip_range}{Numeric of length two specifying the min and max values the residuals will be clipped to; default is c(-sqrt(ncol(umi)), sqrt(ncol(umi)))} \item{min_variance}{Lower bound for the estimated variance for any gene in any cell when calculating pearson residual; default is vst_out$arguments$min_variance} \item{cell_attr}{Data frame of cell meta data} \item{bin_size}{Number of genes to put in each bin (to show progress)} \item{verbosity}{An integer specifying the verbosity level: 0 (silent, no messages), 1 (show messages only), or 2 (show messages and progress bars); default is 2} } \value{ A vector of residual variances (after clipping) } \description{ This never creates the full residual matrix and can be used to determine highly variable genes. } \examples{ \donttest{ vst_out <- vst(pbmc, return_cell_attr = TRUE) res_var <- get_residual_var(vst_out, pbmc) } } sctransform/man/robust_scale.Rd0000644000176200001440000000044715123210676016352 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utils.R \name{robust_scale} \alias{robust_scale} \title{Robust scale using median and mad} \usage{ robust_scale(x) } \arguments{ \item{x}{Numeric} } \value{ Numeric } \description{ Robust scale using median and mad } sctransform/man/plot_model.Rd0000644000176200001440000000300115123210676016010 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plotting.R \name{plot_model} \alias{plot_model} \title{Plot observed UMI counts and model} \usage{ plot_model( x, umi, goi, x_var = x$arguments$latent_var[1], cell_attr = x$cell_attr, do_log = TRUE, show_fit = TRUE, show_nr = FALSE, plot_residual = FALSE, batches = NULL, as_poisson = FALSE, arrange_vertical = TRUE, show_density = FALSE, gg_cmds = NULL ) } \arguments{ \item{x}{The output of a vst run} \item{umi}{UMI count matrix} \item{goi}{Vector of genes to plot} \item{x_var}{Cell attribute to use on x axis; will be taken from x$arguments$latent_var[1] by default} \item{cell_attr}{Cell attributes data frame; will be taken from x$cell_attr by default} \item{do_log}{Log10 transform the UMI counts in plot} \item{show_fit}{Show the model fit} \item{show_nr}{Show the non-regularized model (if available)} \item{plot_residual}{Add panels for the Pearson residuals} \item{batches}{Manually specify a batch variable to break up the model plot in segments} \item{as_poisson}{Fix model parameter theta to Inf, effectively showing a Poisson model} \item{arrange_vertical}{Stack individual ggplot objects or place side by side} \item{show_density}{Draw 2D density lines over points} \item{gg_cmds}{Additional ggplot layer commands} } \value{ A ggplot object } \description{ Plot observed UMI counts and model } \examples{ \donttest{ vst_out <- vst(pbmc, return_cell_attr = TRUE) plot_model(vst_out, pbmc, 'EMC4') } } sctransform/man/clip_matrix_values.Rd0000644000176200001440000000067315123222545017556 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utils.R \name{clip_matrix_values} \alias{clip_matrix_values} \title{Clip matrix values to specified range} \usage{ clip_matrix_values(mat, clip_range) } \arguments{ \item{mat}{Matrix to clip} \item{clip_range}{Numeric vector of length 2 with min and max values} } \value{ Matrix with values clipped to range } \description{ Clip matrix values to specified range } sctransform/man/diff_mean_test_conserved.Rd0000644000176200001440000000562115123210676020703 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/differential_expression.R \name{diff_mean_test_conserved} \alias{diff_mean_test_conserved} \title{Find differentially expressed genes that are conserved across samples} \usage{ diff_mean_test_conserved( y, group_labels, sample_labels, balanced = TRUE, compare = "each_vs_rest", pval_th = 1e-04, ... ) } \arguments{ \item{y}{A matrix of counts; must be (or inherit from) class dgCMatrix; genes are rows, cells are columns} \item{group_labels}{The group labels (i.e. clusters or time points); will be converted to factor} \item{sample_labels}{The sample labels; will be converted to factor} \item{balanced}{Boolean, see details for explanation; default is TRUE} \item{compare}{Specifies which groups to compare, see details; currently only 'each_vs_rest' (the default) is supported} \item{pval_th}{P-value threshold used to call a gene differentially expressed when summarizing the tests per gene} \item{...}{Parameters passed to diff_mean_test} } \value{ Data frame of results } \description{ Find differentially expressed genes that are conserved across samples } \section{Details}{ This function calls diff_mean_test repeatedly and aggregates the results per group and gene. If balanced is TRUE (the default), it is assumed that each sample spans multiple groups, as would be the case when merging or integrating samples from the same tissue followed by clustering. Here the group labels would be the clusters and cluster markers would have support in each sample. If balanced is FALSE, an unbalanced design is assumed where each sample contributes to one group. An example is a time series experiment where some samples are taken from time point 1 while other samples are taken from time point 2. The time point would be the group label and the goal would be to identify differentially expressed genes between time points that are supported by many between-sample comparisons. Output columns: \describe{ \item{group1}{Group label of the frist group of cells} \item{group2}{Group label of the second group of cells; currently fixed to 'rest'} \item{gene}{Gene name (from rownames of input matrix)} \item{n_tests}{The number of tests this gene participated in for this group} \item{log2FC_min,median,max}{Summary statistics for log2FC across the tests} \item{mean1,2_median}{Median of group mean across the tests} \item{pval_max}{Maximum of p-values across tests} \item{de_tests}{Number of tests that showed this gene having a log2FC going in the same direction as log2FC_median and having a p-value <= pval_th} } The output is ordered by group1, -de_tests, -abs(log2FC_median), pval_max } \examples{ \donttest{ clustering <- 1:ncol(pbmc) \%\% 2 sample_id <- 1:ncol(pbmc) \%\% 3 vst_out <- vst(pbmc, return_corrected_umi = TRUE) de_res <- diff_mean_test_conserved(y = vst_out$umi_corrected, group_labels = clustering, sample_labels = sample_id) } } sctransform/man/setup_progress_bar.Rd0000644000176200001440000000115315123222545017566 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utils.R \name{setup_progress_bar} \alias{setup_progress_bar} \title{Setup progress bar for batch processing} \usage{ setup_progress_bar(n_items, bin_size, verbosity) } \arguments{ \item{n_items}{Total number of items to process} \item{bin_size}{Size of each batch/bin} \item{verbosity}{An integer specifying the verbosity level: 0 (silent), 1 (messages only), or 2 (messages and progress bars)} } \value{ A list with bin_ind (vector), pb (progress bar object or NULL), and max_bin } \description{ Setup progress bar for batch processing } sctransform/man/compare_expression.Rd0000644000176200001440000000324015123222545017562 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/differential_expression.R \name{compare_expression} \alias{compare_expression} \title{Compare gene expression between two groups} \usage{ compare_expression( x, umi, group, val1, val2, method = "LRT", bin_size = 256, cell_attr = x$cell_attr, y = x$y, min_cells = 5, weighted = TRUE, randomize = FALSE, verbosity = 2 ) } \arguments{ \item{x}{A list that provides model parameters and optionally meta data; use output of vst function} \item{umi}{A matrix of UMI counts with genes as rows and cells as columns} \item{group}{A vector indicating the groups} \item{val1}{A vector indicating the values of the group vector to treat as group 1} \item{val2}{A vector indicating the values of the group vector to treat as group 2} \item{method}{Either 'LRT' for likelihood ratio test, or 't_test' for t-test} \item{bin_size}{Number of genes that are processed between updates of progress bar} \item{cell_attr}{Data frame of cell meta data} \item{y}{Only used if method = 't_test', this is the residual matrix; default is x$y} \item{min_cells}{A gene has to be detected in at least this many cells in at least one of the groups being compared to be tested} \item{weighted}{Balance the groups by using the appropriate weights} \item{randomize}{Boolean indicating whether to shuffle group labels - only set to TRUE when testing methods} \item{verbosity}{An integer specifying the verbosity level: 0 (silent, no messages), 1 (show messages only), or 2 (show messages and progress bars); default is 2} } \value{ Data frame of results } \description{ Compare gene expression between two groups } sctransform/man/make.sparse.Rd0000644000176200001440000000046515123210676016076 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utils.R \name{make.sparse} \alias{make.sparse} \title{Convert a given matrix to dgCMatrix} \usage{ make.sparse(mat) } \arguments{ \item{mat}{Input matrix} } \value{ A dgCMatrix } \description{ Convert a given matrix to dgCMatrix } sctransform/man/prepare_regressor_data.Rd0000644000176200001440000000106415123222545020401 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utils.R \name{prepare_regressor_data} \alias{prepare_regressor_data} \title{Prepare regressor data from vst object and cell attributes} \usage{ prepare_regressor_data(vst_out, cell_attr) } \arguments{ \item{vst_out}{vst object containing model_str and model_pars_nonreg} \item{cell_attr}{Data frame of cell attributes} } \value{ Matrix of regressor data } \description{ Helper function to create regressor data matrix, handling both regular and non-regularized parameters if present } sctransform/man/generate.Rd0000644000176200001440000000207315123210676015454 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/generate.R \name{generate} \alias{generate} \title{Generate data from regularized models.} \usage{ generate( vst_out, genes = rownames(vst_out$model_pars_fit), cell_attr = vst_out$cell_attr, n_cells = nrow(cell_attr) ) } \arguments{ \item{vst_out}{A list that provides model parameters and optionally meta data; use output of vst function} \item{genes}{The gene names for which to generate data; default is rownames(vst_out$model_pars_fit)} \item{cell_attr}{Provide cell meta data holding latent data info; default is vst_out$cell_attr} \item{n_cells}{Number of cells to generate; default is nrow(cell_attr)} } \value{ Generated data as dgCMatrix } \description{ Generate data from regularized models. This generates data from the background, i.e. no residuals are added to the simulated data. The cell attributes for the generated cells are sampled from the input with replacement. } \examples{ \donttest{ vst_out <- vst(pbmc, return_cell_attr = TRUE) generated_data <- generate(vst_out) } } sctransform/man/get_residuals.Rd0000644000176200001440000000306515123222545016514 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utils.R \name{get_residuals} \alias{get_residuals} \title{Return Pearson or deviance residuals of regularized models} \usage{ get_residuals( vst_out, umi, residual_type = "pearson", res_clip_range = c(-sqrt(ncol(umi)), sqrt(ncol(umi))), min_variance = vst_out$arguments$min_variance, cell_attr = vst_out$cell_attr, bin_size = 256, verbosity = vst_out$arguments$verbosity ) } \arguments{ \item{vst_out}{The output of a vst run} \item{umi}{The UMI count matrix that will be used} \item{residual_type}{What type of residuals to return; can be 'pearson' or 'deviance'; default is 'pearson'} \item{res_clip_range}{Numeric of length two specifying the min and max values the results will be clipped to; default is c(-sqrt(ncol(umi)), sqrt(ncol(umi)))} \item{min_variance}{Lower bound for the estimated variance for any gene in any cell when calculating pearson residual; default is vst_out$arguments$min_variance} \item{cell_attr}{Data frame of cell meta data} \item{bin_size}{Number of genes to put in each bin (to show progress)} \item{verbosity}{An integer specifying the verbosity level: 0 (silent, no messages), 1 (show messages only), or 2 (show messages and progress bars); default is 2} } \value{ A matrix of residuals } \description{ Return Pearson or deviance residuals of regularized models } \examples{ \donttest{ vst_out <- vst(pbmc, return_cell_attr = TRUE) pearson_res <- get_residuals(vst_out, pbmc) deviance_res <- get_residuals(vst_out, pbmc, residual_type = 'deviance') } } sctransform/man/plot_model_pars.Rd0000644000176200001440000000170215123222545017041 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plotting.R \name{plot_model_pars} \alias{plot_model_pars} \title{Plot estimated and fitted model parameters} \usage{ plot_model_pars( vst_out, xaxis = "gmean", show_theta = FALSE, show_var = FALSE, verbosity = 2 ) } \arguments{ \item{vst_out}{The output of a vst run} \item{xaxis}{Variable to plot on X axis; default is "gmean"} \item{show_theta}{Whether to show the theta parameter; default is FALSE (only the overdispersion factor is shown)} \item{show_var}{Whether to show the average model variance; default is FALSE} \item{verbosity}{An integer specifying the verbosity level: 0 (silent, no messages), 1 (show messages only), or 2 (show messages and progress bars); default is 2} } \value{ A ggplot object } \description{ Plot estimated and fitted model parameters } \examples{ \donttest{ vst_out <- vst(pbmc, return_gene_attr = TRUE) plot_model_pars(vst_out) } } sctransform/man/update_progress_bar.Rd0000644000176200001440000000073315123222545017713 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utils.R \name{update_progress_bar} \alias{update_progress_bar} \title{Update progress bar} \usage{ update_progress_bar(pb, i, verbosity) } \arguments{ \item{pb}{Progress bar object (can be NULL)} \item{i}{Current iteration number} \item{verbosity}{An integer specifying the verbosity level: 0 (silent), 1 (messages only), or 2 (messages and progress bars)} } \description{ Update progress bar } sctransform/man/row_var.Rd0000644000176200001440000000044215123210676015337 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utils.R \name{row_var} \alias{row_var} \title{Variance per row} \usage{ row_var(x) } \arguments{ \item{x}{matrix of class \code{matrix} or \code{dgCMatrix}} } \value{ variances } \description{ Variance per row } sctransform/man/umify.Rd0000644000176200001440000000235215123210676015013 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/umify.R \name{umify} \alias{umify} \title{Quantile normalization of cell-level data to match typical UMI count data} \usage{ umify(counts) } \arguments{ \item{counts}{A matrix of class dgCMatrix with genes as rows and columns as cells} } \value{ A UMI-fied count matrix } \description{ Quantile normalization of cell-level data to match typical UMI count data } \section{Details}{ sctransform::vst operates under the assumption that gene counts approximately follow a Negative Binomial dristribution. For UMI-based data that seems to be the case, however, non-UMI data does not behave in the same way. In some cases it might be better to to apply a transformation to such data to make it look like UMI data. This function applies such a transformation function. Cells in the input matrix are processed independently. For each cell the non-zero data is transformed to quantile values. Based on the number of genes detected a smooth function is used to predict the UMI-like counts. The functions have be trained on various public data sets and come as part of the package (see umify_data data set in this package). } \examples{ \donttest{ silly_example <- umify(pbmc) } } sctransform/man/umify_data.Rd0000644000176200001440000000111415123210676015777 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/data.R \docType{data} \name{umify_data} \alias{umify_data} \title{Transformation functions for umify} \format{ A list of length two. The first element is a data frame with group, quantile and log-counts values. The second element is a vector of breaks to be used with cut to group observations. } \usage{ umify_data } \description{ The functions have been trained on various public data sets and relate quantile values to log-counts. Here the expected values at various points are given. } \keyword{datasets} sctransform/man/diff_mean_test.Rd0000644000176200001440000001034615123210676016633 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/differential_expression.R \name{diff_mean_test} \alias{diff_mean_test} \title{Non-parametric differential expression test for sparse non-negative data} \usage{ diff_mean_test( y, group_labels, compare = "each_vs_rest", R = 99, log2FC_th = log2(1.2), mean_th = 0.05, cells_th = 5, only_pos = FALSE, only_top_n = NULL, mean_type = "geometric", verbosity = 1 ) } \arguments{ \item{y}{A matrix of counts; must be (or inherit from) class dgCMatrix; genes are row, cells are columns} \item{group_labels}{The group labels (e.g. cluster identities); will be converted to factor} \item{compare}{Specifies which groups to compare, see details; default is 'each_vs_rest'} \item{R}{The number of random permutations used to derive the p-values; default is 99} \item{log2FC_th}{Threshold to remove genes from testing; absolute log2FC must be at least this large for a gene to be tested; default is \code{log2(1.2)}} \item{mean_th}{Threshold to remove genes from testing; gene mean must be at least this large for a gene to be tested; default is 0.05} \item{cells_th}{Threshold to remove genes from testing; gene must be detected (non-zero count) in at least this many cells in the group with higher mean; default is 5} \item{only_pos}{Test only genes with positive fold change (mean in group 1 > mean in group2); default is FALSE} \item{only_top_n}{Test only the this number of genes from both ends of the log2FC spectrum after all of the above filters have been applied; useful to get only the top markers; only used if set to a numeric value; default is NULL} \item{mean_type}{Which type of mean to use; if \code{'geometric'} (default) the geometric mean is used; to avoid \code{log(0)} we use \code{log1p} to add 1 to all counts and log-transform, calculate the arithmetic mean, and then back-transform and subtract 1 using \code{exp1m}; if this parameter is set to \code{'arithmetic'} the data is used as is} \item{verbosity}{Integer controlling how many messages the function prints; 0 is silent, 1 (default) is not} } \value{ Data frame of results } \description{ Non-parametric differential expression test for sparse non-negative data } \section{Details}{ This model-free test is applied to each gene (row) individually but is optimized to make use of the efficient sparse data representation of the input. A permutation null distribution us used to assess the significance of the observed difference in mean between two groups. The observed difference in mean is compared against a distribution obtained by random shuffling of the group labels. For each gene every random permutation yields a difference in mean and from the population of these background differences we estimate a mean and standard deviation for the null distribution. This mean and standard deviation are used to turn the observed difference in mean into a z-score and then into a p-value. Finally, all p-values (for the tested genes) are adjusted using the Benjamini & Hochberg method (fdr). The log2FC values in the output are \code{log2(mean1 / mean2)}. Empirical p-values are also calculated: \code{emp_pval = (b + 1) / (R + 1)} where b is the number of times the absolute difference in mean from a random permutation is at least as large as the absolute value of the observed difference in mean, R is the number of random permutations. This is an upper bound of the real empirical p-value that would be obtained by enumerating all possible group label permutations. There are multiple ways the group comparisons can be specified based on the compare parameter. The default, \code{'each_vs_rest'}, does multiple comparisons, one per group vs all remaining cells. \code{'all_vs_all'}, also does multiple comparisons, covering all groups pairs. If compare is set to a length two character vector, e.g. \code{c('T-cells', 'B-cells')}, one comparison between those two groups is done. To put multiple groups on either side of a single comparison, use a list of length two. E.g. \code{compare = list(c('cluster1', 'cluster5'), c('cluster3'))}. } \examples{ \donttest{ clustering <- 1:ncol(pbmc) \%\% 2 vst_out <- vst(pbmc, return_corrected_umi = TRUE) de_res <- diff_mean_test(y = vst_out$umi_corrected, group_labels = clustering) } } sctransform/man/robust_scale_binned.Rd0000644000176200001440000000067015123210676017667 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utils.R \name{robust_scale_binned} \alias{robust_scale_binned} \title{Robust scale using median and mad per bin} \usage{ robust_scale_binned(y, x, breaks) } \arguments{ \item{y}{Numeric vector} \item{x}{Numeric vector} \item{breaks}{Numeric vector of breaks} } \value{ Numeric vector of scaled score } \description{ Robust scale using median and mad per bin } sctransform/man/correct_counts.Rd0000644000176200001440000000240315123222545016711 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/denoise.R \name{correct_counts} \alias{correct_counts} \title{Correct data by setting all latent factors to their median values and reversing the regression model} \usage{ correct_counts( x, umi, cell_attr = x$cell_attr, scale_factor = NA, verbosity = 2 ) } \arguments{ \item{x}{A list that provides model parameters and optionally meta data; use output of vst function} \item{umi}{The count matrix} \item{cell_attr}{Provide cell meta data holding latent data info} \item{scale_factor}{Replace all values of UMI in the regression model by this value. Default is NA which uses median of total UMI as the latent factor.} \item{verbosity}{An integer specifying the verbosity level: 0 (silent, no messages), 1 (show messages only), or 2 (show messages and progress bars); default is 2} } \value{ Corrected data as UMI counts } \description{ This version does not need a matrix of Pearson residuals. It takes the count matrix as input and calculates the residuals on the fly. The corrected UMI counts will be rounded to the nearest integer and negative values clipped to 0. } \examples{ \donttest{ vst_out <- vst(pbmc, return_cell_attr = TRUE) umi_corrected <- correct_counts(vst_out, pbmc) } } sctransform/man/vst.Rd0000644000176200001440000002205615123222545014477 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/vst.R \name{vst} \alias{vst} \title{Variance stabilizing transformation for UMI count data} \usage{ vst( umi, cell_attr = NULL, latent_var = c("log_umi"), batch_var = NULL, latent_var_nonreg = NULL, n_genes = 2000, n_cells = NULL, method = "poisson", do_regularize = TRUE, theta_regularization = "od_factor", res_clip_range = c(-sqrt(ncol(umi)), sqrt(ncol(umi))), bin_size = 500, min_cells = 5, residual_type = "pearson", return_cell_attr = FALSE, return_gene_attr = TRUE, return_corrected_umi = FALSE, min_variance = -Inf, bw_adjust = 3, gmean_eps = 1, theta_estimation_fun = "theta.ml", theta_given = NULL, exclude_poisson = FALSE, use_geometric_mean = TRUE, use_geometric_mean_offset = FALSE, fix_intercept = FALSE, fix_slope = FALSE, scale_factor = NA, vst.flavor = NULL, verbosity = 2 ) } \arguments{ \item{umi}{A matrix of UMI counts with genes as rows and cells as columns} \item{cell_attr}{A data frame containing the dependent variables; if omitted a data frame with umi and gene will be generated} \item{latent_var}{The independent variables to regress out as a character vector; must match column names in cell_attr; default is c("log_umi")} \item{batch_var}{The dependent variables indicating which batch a cell belongs to; no batch interaction terms used if omiited} \item{latent_var_nonreg}{The non-regularized dependent variables to regress out as a character vector; must match column names in cell_attr; default is NULL} \item{n_genes}{Number of genes to use when estimating parameters (default uses 2000 genes, set to NULL to use all genes)} \item{n_cells}{Number of cells to use when estimating parameters (default uses all cells)} \item{method}{Method to use for initial parameter estimation; one of 'poisson', 'qpoisson', 'nb_fast', 'nb', 'nb_theta_given', 'glmGamPoi', 'offset', 'offset_shared_theta_estimate', 'glmGamPoi_offset'; default is 'poisson'} \item{do_regularize}{Boolean that, if set to FALSE, will bypass parameter regularization and use all genes in first step (ignoring n_genes); default is FALSE} \item{theta_regularization}{Method to use to regularize theta; use 'log_theta' for the behavior prior to version 0.3; default is 'od_factor'} \item{res_clip_range}{Numeric of length two specifying the min and max values the results will be clipped to; default is c(-sqrt(ncol(umi)), sqrt(ncol(umi)))} \item{bin_size}{Number of genes to process simultaneously; this will determine how often the progress bars are updated and how much memory is being used; default is 500} \item{min_cells}{Only use genes that have been detected in at least this many cells; default is 5} \item{residual_type}{What type of residuals to return; can be 'pearson', 'deviance', or 'none'; default is 'pearson'} \item{return_cell_attr}{Make cell attributes part of the output; default is FALSE} \item{return_gene_attr}{Calculate gene attributes and make part of output; default is TRUE} \item{return_corrected_umi}{If set to TRUE output will contain corrected UMI matrix; see \code{correct} function} \item{min_variance}{Lower bound for the estimated variance for any gene in any cell when calculating pearson residual; one of 'umi_median', 'model_median', 'model_mean' or a numeric. default is -Inf. When set to 'umi_median' uses (median of non-zero UMIs / 5)^2 as the minimum variance so that a median UMI (often 1) results in a maximum pearson residual of 5. When set to 'model_median' or 'model_mean' uses the mean/median of the model estimated mu per gene as the minimum_variance.#'} \item{bw_adjust}{Kernel bandwidth adjustment factor used during regurlarization; factor will be applied to output of bw.SJ; default is 3} \item{gmean_eps}{Small value added when calculating geometric mean of a gene to avoid log(0); default is 1} \item{theta_estimation_fun}{Character string indicating which method to use to estimate theta (when method = poisson); default is 'theta.ml', but 'theta.mm' seems to be a good and fast alternative} \item{theta_given}{If method is set to nb_theta_given, this should be a named numeric vector of fixed theta values for the genes; if method is offset, this should be a single value; default is NULL} \item{exclude_poisson}{Exclude poisson genes (i.e. mu < 0.001 or mu > variance) from regularization; default is FALSE} \item{use_geometric_mean}{Use geometric mean instead of arithmetic mean for all calculations ; default is TRUE} \item{use_geometric_mean_offset}{Use geometric mean instead of arithmetic mean in the offset model; default is FALSE} \item{fix_intercept}{Fix intercept as defined in the offset model; default is FALSE} \item{fix_slope}{Fix slope to log(10) (equivalent to using library size as an offset); default is FALSE} \item{scale_factor}{Replace all values of UMI in the regression model by this value instead of the median UMI; default is NA} \item{vst.flavor}{When set to `v2` sets method = glmGamPoi_offset, n_cells=2000, and exclude_poisson = TRUE which causes the model to learn theta and intercept only besides excluding poisson genes from learning and regularization; default is NULL which uses the original sctransform model} \item{verbosity}{An integer specifying the verbosity level: 0 (silent, no messages), 1 (show messages only), or 2 (show messages and progress bars); default is 2} } \value{ A list with components \item{y}{Matrix of transformed data, i.e. Pearson residuals, or deviance residuals; empty if \code{residual_type = 'none'}} \item{umi_corrected}{Matrix of corrected UMI counts (optional)} \item{model_str}{Character representation of the model formula} \item{model_pars}{Matrix of estimated model parameters per gene (theta and regression coefficients)} \item{model_pars_outliers}{Vector indicating whether a gene was considered to be an outlier} \item{model_pars_fit}{Matrix of fitted / regularized model parameters} \item{model_str_nonreg}{Character representation of model for non-regularized variables} \item{model_pars_nonreg}{Model parameters for non-regularized variables} \item{genes_log_gmean_step1}{log-geometric mean of genes used in initial step of parameter estimation} \item{cells_step1}{Cells used in initial step of parameter estimation} \item{arguments}{List of function call arguments} \item{cell_attr}{Data frame of cell meta data (optional)} \item{gene_attr}{Data frame with gene attributes such as mean, detection rate, etc. (optional)} \item{times}{Time stamps at various points in the function} } \description{ Apply variance stabilizing transformation to UMI count data using a regularized Negative Binomial regression model. This will remove unwanted effects from UMI data and return Pearson residuals. Uses future_lapply; you can set the number of cores it will use to n with plan(strategy = "multicore", workers = n). If n_genes is set, only a (somewhat-random) subset of genes is used for estimating the initial model parameters. For details see \doi{10.1186/s13059-019-1874-1}. } \section{Details}{ In the first step of the algorithm, per-gene glm model parameters are learned. This step can be done on a subset of genes and/or cells to speed things up. If \code{method} is set to 'poisson', a poisson regression is done and the negative binomial theta parameter is estimated using the response residuals in \code{theta_estimation_fun}. If \code{method} is set to 'qpoisson', coefficients and overdispersion (phi) are estimated by quasi poisson regression and theta is estimated based on phi and the mean fitted value - this is currently the fastest method with results very similar to 'glmGamPoi' If \code{method} is set to 'nb_fast', coefficients and theta are estimated as in the 'poisson' method, but coefficients are then re-estimated using a proper negative binomial model in a second call to glm with \code{family = MASS::negative.binomial(theta = theta)}. If \code{method} is set to 'nb', coefficients and theta are estimated by a single call to \code{MASS::glm.nb}. If \code{method} is set to 'glmGamPoi', coefficients and theta are estimated by a single call to \code{glmGamPoi::glm_gp}. A special case is \code{method = 'offset'}. Here no regression parameters are learned, but instead an offset model is assumed. The latent variable is set to log_umi and a fixed slope of log(10) is used (offset). The intercept is given by log(gene_mean) - log(avg_cell_umi). See Lause et al. \doi{10.1186/s13059-021-02451-7} for details. Theta is set to 100 by default, but can be changed using the \code{theta_given} parameter (single numeric value). If the offset method is used, the following parameters are overwritten: \code{cell_attr <- NULL, latent_var <- c('log_umi'), batch_var <- NULL, latent_var_nonreg <- NULL, n_genes <- NULL, n_cells <- NULL, do_regularize <- FALSE}. Further, \code{method = 'offset_shared_theta_estimate'} exists where the 250 most highly expressed genes with detection rate of at least 0.5 are used to estimate a theta that is then shared across all genes. Thetas are estimated per individual gene using 5000 randomly selected cells. The final theta used for all genes is then the average. } \examples{ \donttest{ vst_out <- vst(pbmc) } } sctransform/man/smooth_via_pca.Rd0000644000176200001440000000175315123210676016661 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/denoise.R \name{smooth_via_pca} \alias{smooth_via_pca} \title{Smooth data by PCA} \usage{ smooth_via_pca( x, elbow_th = 0.025, dims_use = NULL, max_pc = 100, do_plot = FALSE, scale. = FALSE ) } \arguments{ \item{x}{A data matrix with genes as rows and cells as columns} \item{elbow_th}{The fraction of PC sdev drop that is considered significant; low values will lead to more PCs being used} \item{dims_use}{Directly specify PCs to use, e.g. 1:10} \item{max_pc}{Maximum number of PCs computed} \item{do_plot}{Plot PC sdev and sdev drop} \item{scale.}{Boolean indicating whether genes should be divided by standard deviation after centering and prior to PCA} } \value{ Smoothed data } \description{ Perform PCA, identify significant dimensions, and reverse the rotation using only significant dimensions. } \examples{ \donttest{ vst_out <- vst(pbmc) y_smooth <- smooth_via_pca(vst_out$y, do_plot = TRUE) } } sctransform/man/close_progress_bar.Rd0000644000176200001440000000065715123222545017543 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utils.R \name{close_progress_bar} \alias{close_progress_bar} \title{Close progress bar} \usage{ close_progress_bar(pb, verbosity) } \arguments{ \item{pb}{Progress bar object (can be NULL)} \item{verbosity}{An integer specifying the verbosity level: 0 (silent), 1 (messages only), or 2 (messages and progress bars)} } \description{ Close progress bar } sctransform/man/get_model_var.Rd0000644000176200001440000000176515123222545016476 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utils.R \name{get_model_var} \alias{get_model_var} \title{Return average variance under negative binomial model} \usage{ get_model_var( vst_out, cell_attr = vst_out$cell_attr, use_nonreg = FALSE, bin_size = 256, verbosity = 2 ) } \arguments{ \item{vst_out}{The output of a vst run} \item{cell_attr}{Data frame of cell meta data} \item{use_nonreg}{Use the non-regularized parameter estimates; boolean; default is FALSE} \item{bin_size}{Number of genes to put in each bin (to show progress)} \item{verbosity}{An integer specifying the verbosity level: 0 (silent, no messages), 1 (show messages only), or 2 (show messages and progress bars); default is 2} } \value{ A named vector of variances (the average across all cells), one entry per gene. } \description{ This is based on the formula var = mu + mu^2 / theta } \examples{ \donttest{ vst_out <- vst(pbmc, return_cell_attr = TRUE) res_var <- get_model_var(vst_out) } } sctransform/man/correct.Rd0000644000176200001440000000270515123222545015323 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/denoise.R \name{correct} \alias{correct} \title{Correct data by setting all latent factors to their median values and reversing the regression model} \usage{ correct( x, data = "y", cell_attr = x$cell_attr, as_is = FALSE, do_round = TRUE, do_pos = TRUE, scale_factor = NA, verbosity = 2 ) } \arguments{ \item{x}{A list that provides model parameters and optionally meta data; use output of vst function} \item{data}{The name of the entry in x that holds the data} \item{cell_attr}{Provide cell meta data holding latent data info} \item{as_is}{Use cell attributes as is and do not use the median; set to TRUE if you want to manually control the values of the latent factors; default is FALSE} \item{do_round}{Round the result to integers} \item{do_pos}{Set negative values in the result to zero} \item{scale_factor}{Replace all values of UMI in the regression model by this value. Default is NA which uses median of total UMI as the latent factor.} \item{verbosity}{An integer specifying the verbosity level: 0 (silent, no messages), 1 (show messages only), or 2 (show messages and progress bars); default is 2} } \value{ Corrected data as UMI counts } \description{ Correct data by setting all latent factors to their median values and reversing the regression model } \examples{ \donttest{ vst_out <- vst(pbmc, return_cell_attr = TRUE) umi_corrected <- correct(vst_out) } } sctransform/man/get_nz_median2.Rd0000644000176200001440000000102715123210676016545 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utils.R \name{get_nz_median2} \alias{get_nz_median2} \title{Get median of non zero UMIs from a count matrix} \usage{ get_nz_median2(umi, genes = NULL) } \arguments{ \item{umi}{Count matrix} \item{genes}{A vector of genes to consider for calculating the median. Default is NULL which uses all genes.} } \value{ A numeric value representing the median of non-zero entries from the UMI matrix } \description{ Get median of non zero UMIs from a count matrix } sctransform/man/is_outlier.Rd0000644000176200001440000000054315123210676016040 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utils.R \name{is_outlier} \alias{is_outlier} \title{Identify outliers} \usage{ is_outlier(y, x, th = 10) } \arguments{ \item{y}{Dependent variable} \item{x}{Independent variable} \item{th}{Outlier score threshold} } \value{ Boolean vector } \description{ Identify outliers } sctransform/man/row_gmean.Rd0000644000176200001440000000060615123210676015640 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utils.R \name{row_gmean} \alias{row_gmean} \title{Geometric mean per row} \usage{ row_gmean(x, eps = 1) } \arguments{ \item{x}{matrix of class \code{matrix} or \code{dgCMatrix}} \item{eps}{small value to add to x to avoid log(0); default is 1} } \value{ geometric means } \description{ Geometric mean per row } sctransform/man/pbmc.Rd0000644000176200001440000000114015123222545014573 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/data.R \docType{data} \name{pbmc} \alias{pbmc} \title{Peripheral Blood Mononuclear Cells (PBMCs)} \format{ A sparse matrix (dgCMatrix, see Matrix package) of molecule counts. There are 914 rows (genes) and 283 columns (cells). This is a downsampled version of a 3K PBMC dataset available from 10x Genomics. } \source{ \url{https://www.10xgenomics.com/datasets/3-k-pbm-cs-from-a-healthy-donor-1-standard-1-1-0} } \usage{ pbmc } \description{ UMI counts for a subset of cells freely available from 10X Genomics } \keyword{datasets} sctransform/DESCRIPTION0000644000176200001440000000370215130402306014315 0ustar liggesusersPackage: sctransform Type: Package Title: Variance Stabilizing Transformations for Single Cell UMI Data Version: 0.4.3 Date: 2025-12-25 Authors@R: c( person(given = "Christoph", family = "Hafemeister", email = "christoph.hafemeister@nyu.edu", role = "aut", comment = c(ORCID = "0000-0001-6365-8254")), person(given = "Saket", family = "Choudhary", email = "saketc@iitb.ac.in", role = c("aut", "cre"), comment = c(ORCID = "0000-0001-5202-7633")), person(given = "Rahul", family = "Satija", email = "rsatija@nygenome.org", role = "ctb", comment = c(ORCID = "0000-0001-9448-8833")) ) Description: A normalization method for single-cell UMI count data using a variance stabilizing transformation. The transformation is based on a negative binomial regression model with regularized parameters. As part of the same regression framework, this package also provides functions for batch correction, and data correction. See Hafemeister and Satija (2019) , and Choudhary and Satija (2022) for more details. URL: https://github.com/satijalab/sctransform BugReports: https://github.com/satijalab/sctransform/issues License: GPL-3 | file LICENSE Encoding: UTF-8 LazyData: true Depends: R (>= 3.6.0) LinkingTo: RcppArmadillo, Rcpp (>= 0.11.0) SystemRequirements: C++17 Imports: dplyr, magrittr, MASS, Matrix (>= 1.5-0), methods, future.apply, future, parallelly, ggplot2, reshape2, rlang, gridExtra, matrixStats Suggests: irlba, testthat, knitr Enhances: glmGamPoi RoxygenNote: 7.3.3 NeedsCompilation: yes Packaged: 2025-12-25 12:01:11 UTC; saket Author: Christoph Hafemeister [aut] (ORCID: ), Saket Choudhary [aut, cre] (ORCID: ), Rahul Satija [ctb] (ORCID: ) Maintainer: Saket Choudhary Repository: CRAN Date/Publication: 2026-01-10 07:50:30 UTC