DirichletMultinomial/0000755000175000017500000000000014147512672014537 5ustar nileshnileshDirichletMultinomial/DESCRIPTION0000644000175000017500000000223314136070214016232 0ustar nileshnileshPackage: DirichletMultinomial Type: Package Title: Dirichlet-Multinomial Mixture Model Machine Learning for Microbiome Data Version: 1.36.0 Author: Martin Morgan Maintainer: Martin Morgan Description: Dirichlet-multinomial mixture models can be used to describe variability in microbial metagenomic data. This package is an interface to code originally made available by Holmes, Harris, and Quince, 2012, PLoS ONE 7(2): 1-15, as discussed further in the man page for this package, ?DirichletMultinomial. License: LGPL-3 Depends: S4Vectors, IRanges Imports: stats4, methods, BiocGenerics Suggests: lattice, parallel, MASS, RColorBrewer, xtable Collate: AllGenerics.R dmn.R dmngroup.R roc.R util.R SystemRequirements: gsl biocViews: ImmunoOncology, Microbiome, Sequencing, Clustering, Classification, Metagenomics git_url: https://git.bioconductor.org/packages/DirichletMultinomial git_branch: RELEASE_3_14 git_last_commit: 926baff git_last_commit_date: 2021-10-26 Date/Publication: 2021-10-26 NeedsCompilation: yes Packaged: 2021-10-26 21:18:04 UTC; biocbuild DirichletMultinomial/README.md0000644000175000017500000000231314136047775016022 0ustar nileshnilesh # DirichletMultinomial Dirichlet-multinomial mixture models can be used to describe variability in microbial metagenomic data. This package is an interface to code originally made available by Holmes, Harris, and Quince, 2012, PLoS ONE 7(2): 1-15, as discussed further in the man page for this package, ?DirichletMultinomial. ## Installation Install [DirichletMultinomial][] from [Bioconductor][] with: ```{r} if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("DirichletMultinomial") ``` Linux and MacOS with source-level installations require the 'gsl' system dependency. On Debian or Ubuntu ``` sudo apt-get install -y libgsl-dev ``` On Fedora, CentOS or RHEL ``` sudo yum install libgsl-devel ``` On macOS (source installations are not common on macOS, so this step may is not usually necessary) ``` brew install gsl ``` ## Use See the [DirichletMultinomial][] landing page and [vignette][] for use. [DirichletMultinomial]: https://bioconductor.org/packages/DirichletMultinomial [Bioconductor]: https://bioconductor.org [vignette]: https://bioconductor.org/packages/release/bioc/vignettes/DirichletMultinomial/inst/doc/DirichletMultinomial.pdf DirichletMultinomial/man/0000755000175000017500000000000014136047775015317 5ustar nileshnileshDirichletMultinomial/man/dmngroup.Rd0000644000175000017500000000365314136047775017450 0ustar nileshnilesh\name{dmngroup} \alias{dmngroup} \title{ Dirichlet-Multinomial generative classifiers. } \description{ Fit Dirichlet-Multinomial generative classifiers to groups (rows) within a sample x taxon count matrix. } \usage{ dmngroup(count, group, k, ..., simplify = TRUE, .lapply = parallel::mclapply) } \arguments{ \item{count}{\code{matrix()} of sample x taxon counts.} \item{group}{\code{factor()} or vector to be coerced to a factor, with as many elements as there are rows in \code{count}, indicating the group to which the corresponding sample belongs.} \item{k}{\code{integer()}, the number(s) of Dirichlet components to fit.} \item{\dots}{Additional arguments, passed to \code{\link{dmn}}.} \item{simplify}{Return only the best-fit model for each group?} \item{.lapply}{An \code{lapply}-like function for application of group x k fits.} } \details{ This function divided \code{count} into groups defined by \code{group}, creates all combinations of \code{group} x \code{k}, and evaluates each using \code{\link{dmn}}. When \code{simplify=TRUE}, the best (Laplace) fit is selected for each group. } \value{ An object of class \code{dmngroup}, a list of fitted models of class \code{\link{dmn}}. When \code{simplify=TRUE}, elements are named by the group to which they correspond. } \references{ Holmes I, Harris K, Quince C, 2012 Dirichlet Multinomial Mixtures: Generative Models for Microbial Metagenomics. PLoS ONE 7(2): e30126. doi:10.1371/journal.pone.0030126. } \author{ Martin Morgan \url{mailto:mtmorgan@fhcrc.org} } \seealso{ \code{\link{dmn}}, \link{DirichletMultinomial-package}, \code{vignette("DirichletMultinomial")} } \examples{ ## best fit for groups 'Lean' and 'Obese'; full example in vignette. \dontrun{bestgrp <- dmngroup(count, pheno, k=1:5, verbose=TRUE, mc.preschedule=FALSE) } data(bestgrp) bestgrp bestgrp[["Obese"]] } \keyword{ manip } DirichletMultinomial/man/heatmapdmn.Rd0000644000175000017500000000360414136047775017727 0ustar nileshnilesh\name{heatmapdmn} \alias{heatmapdmn} \title{Heatmap representation of samples assigned to Dirichlet components.} \description{ Produce a heat map summarizing count data, grouped by Dirichlet component. } \usage{ heatmapdmn(count, fit1, fitN, ntaxa = 30, ..., transform = sqrt, lblwidth = 0.2 * nrow(count), col = .gradient) } \arguments{ \item{count}{A matrix of sample x taxon counts, as supplied to \code{\link{dmn}}.} \item{fit1}{An instance of class \code{dmn}, from a model fit to a single Dirichlet component, \code{k=1} in \code{\link{dmn}}.} \item{fitN}{An instance of class \code{dmn}, from a model fit to \code{N != 1} components, \code{k=N} in \code{\link{dmn}}.} \item{ntaxa}{The \code{ntaxa} most numerous taxa to display counts for.} \item{\dots}{Additional arguments, ignored.} \item{transform}{Transformation to apply to count data prior to visualization; this does \emph{not} influence mixture membership or taxnomic ordering.} \item{lblwidth}{The proportion of the plot to dedicate to taxanomic labels, as a fraction of the number of samples to be plotted.} \item{col}{The colors used to display (possibly transformed, by \code{transform}) count data, as used by \code{\link{image}}.} } \details{ Columns of the heat map correspond to samples. Samples are grouped by Dirichlet component, with average (Dirichlet) components summarized as a separate wide column. Rows correspond to taxonomic groups, ordered based on contribution to Dirichlet components. } \author{Martin Morgan \url{mailto:mtmorgan@fhcrc.org}} \examples{ ## counts fl <- system.file(package="DirichletMultinomial", "extdata", "Twins.csv") count <- t(as.matrix(read.csv(fl, row.names=1))) ## all and best-fit clustering data(fit) lplc <- sapply(fit, laplace) best <- fit[[which.min(lplc)]] heatmapdmn(count, fit[[1]], best, 30) } \keyword{manip} DirichletMultinomial/man/util.Rd0000644000175000017500000000331014136047775016560 0ustar nileshnilesh\name{Utilities} \alias{csubset} \title{Helpful utility functions} \description{ \code{csubset} creates a subset of a count matrix, based on identity of column phenotypes to a specified value. } \usage{ csubset(val, x, pheno, cidx = TRUE) } \arguments{ \item{val}{\code{character(1)} specifying the subset of \code{phenotype} to select.} \item{x}{A matrix of counts, with rows corresponding to samples and columns to taxonomic groups.} \item{pheno}{A \code{character()} vector of length equal to the number of rows in \code{count}, indicating the phenotype of the corresponding sample.} \item{cidx}{A \code{logical(1)} indicating whether columns (taxa) with zero counts in the count matrix following removal of taxa not satisfying \code{pheno \%in\% val} should be removed. \code{cidx=FALSE} removes the 0-count columns.} } \value{ A \code{matrix} of counts, with rows satisfying \code{pheno \%in\% val} and with columns equal either to \code{ncol(x)} (when \code{cidx=TRUE}) or the number of columns with non-zero counts after row subsetting (\code{cidx=FALSE}). } \author{Martin Morgan \url{mailto:mtmorgan@fhcrc.org}} \examples{ ## count matrix fl <- system.file(package="DirichletMultinomial", "extdata", "Twins.csv") count <- t(as.matrix(read.csv(fl, row.names=1))) ## phenotype fl <- system.file(package="DirichletMultinomial", "extdata", "TwinStudy.t") pheno0 <- scan(fl) lvls <- c("Lean", "Obese", "Overwt") pheno <- factor(lvls[pheno0 + 1], levels=lvls) names(pheno) <- rownames(count) ## subset dim(count) sum("Lean" == pheno) dim(csubset("Lean", count, pheno)) dim(csubset("Lean", count, pheno, cidx=FALSE)) } \keyword{manip} DirichletMultinomial/man/fitted.Rd0000644000175000017500000000567214136047775017077 0ustar nileshnilesh\name{model components} \alias{mixture} \alias{mixturewt} \alias{goodnessOfFit} \alias{laplace} \alias{AIC,DMN-method} \alias{BIC,DMN-method} \alias{fitted,DMN-method} \alias{predict,DMN-method} \alias{show,DMN-method} \alias{fitted,DMNGroup-method} \alias{predict,DMNGroup-method} \alias{summary,DMNGroup-method} \alias{show,DMNGroup-method} \title{Access model components.} \description{ The accessors \code{mixture} and \code{mixturewt} return information about the estimated Dirichlet components of the fitted model. Return values are described in the Values section, below. } \usage{ mixture(object, ..., assign=FALSE) mixturewt(object, ...) goodnessOfFit(object, ...) laplace(object, ...) \S4method{AIC}{DMN}(object, ..., k = 2) \S4method{BIC}{DMN}(object, ...) \S4method{fitted}{DMN}(object, ..., scale=FALSE) \S4method{predict}{DMN}(object, newdata, ..., logevidence=FALSE) \S4method{fitted}{DMNGroup}(object, ...) \S4method{predict}{DMNGroup}(object, newdata, ..., assign=FALSE) \S4method{summary}{DMNGroup}(object, ...) } \arguments{ \item{object}{An instance of class \code{\link{dmn}}.} \item{newdata}{A \code{matrix} of new sample x taxon data to be fitted to the model of \code{object}.} \item{...}{Additional arguments, available to methods, when applicable.} \item{assign}{\code{logical(1)} indicating whether the maximum per-sample mixture component should be returned (\code{assign=FALSE}), or the full mixture matrix (\code{assign=TRUE}).} \item{scale}{\code{logical(1)} indicating whether fitted values should be returned unscaled (default, \code{scaled=FALSE}) or scaled by the variability of \code{\link{mixturewt}} parameter \code{theta}.} \item{logevidence}{\code{logical(1)} indicating whether posterior probability (default, \code{logevidence=FALSE}) or log evidence \code{logical=TRUE} should be returned.} \item{k}{ignored.} } \value{ \code{mixture} with \code{assign=FALSE} returns a matrix of sample x Dirichlet component estimates. With \code{assign=TRUE} \code{mixture} returns a named vector indexing the maximal Dirichlet component of each sample. \code{mixturewt} returns a matrix with rows corresponding to mixture components, and columns \code{pi} (component weight) and \code{theta} (component variability). Small values of \code{theta} correspond to highly variable components. \code{goodnessOfFit} returns a named numeric vector of measures of goodness of fit. \code{laplace}, \code{AIC}, and \code{BIC} return the corresponding measures of goodness of fit. } \author{Martin Morgan \url{mailto:mtmorgan@fhcrc.org}} \examples{ data(fit) best <- fit[[4]] mixturewt(best) head(mixture(best), 3) head(mixture(best, assign=TRUE), 3) goodnessOfFit(best) fl <- system.file(package="DirichletMultinomial", "extdata", "Twins.csv") count <- t(as.matrix(read.csv(fl, row.names=1))) data(bestgrp) bestgrp head(predict(bestgrp, count)) } \keyword{manip} DirichletMultinomial/man/dmn.Rd0000644000175000017500000000405014136047775016363 0ustar nileshnilesh\name{dmn} \alias{dmn} \title{ Fit Dirichlet-Multinomial models to count data. } \description{ Fit Dirichlet-Multinomial models to a sample x taxon count matrix. } \usage{ dmn(count, k, verbose = FALSE, seed = runif(1, 0, .Machine$integer.max)) } \arguments{ \item{count}{\code{matrix()} of sample x taxon counts.} \item{k}{\code{integer(1)}, the number of Dirichlet components to fit.} \item{verbose}{\code{logical(1)} indicating whether progress in fit should be reported.} \item{seed}{\code{numeric(1)} random number seed.} } \details{ This implements Dirichlet-multinomial mixture models describe in the package help page, \link{DirichletMultinomial-package}. } \value{ An object of class \code{dmn}, with elements (elements are usually retrieved via functions defined in the package, not directly). \item{GoodnessOfFit}{NLE, LogDet, Laplace, AIC, and BIC criteria assessing goodness-of-fit.} \item{Group}{\code{matrix} of dimension samples x \code{k}, providing the Dirichlet parameter vectors.} \item{Mixture}{\describe{ \item{Weight}{\code{numeric()} of length \code{k}, with relative weight of each component.} }} \item{Fit}{\describe{ \item{Lower}{\code{matrix()} of dimension taxa x \code{k} with 95\% lower bounds on Dirichlet component vector estimates.} \item{Estimate}{\code{matrix()} of dimension taxa x \code{k} with Dirichlet component vector estimates.} \item{Upper}{\code{matrix()} of dimension taxa x \code{k} with 95\% upper bounds on Dirichlet component vector estimates.} }} } \references{ Holmes I, Harris K, Quince C, 2012 Dirichlet Multinomial Mixtures: Generative Models for Microbial Metagenomics. PLoS ONE 7(2): e30126. doi:10.1371/journal.pone.0030126. } \author{ Martin Morgan \url{mailto:mtmorgan@fhcrc.org} } \seealso{ \link{DirichletMultinomial-package}, \code{vignette("DirichletMultinomial")} } \examples{ data(fit) ## k = 1:7; full example in vignette lplc <- sapply(fit, laplace) plot(lplc, type="b") fit[[which.min(lplc)]] } \keyword{ manip } DirichletMultinomial/man/DMNGroup-class.Rd0000644000175000017500000000234514136047775020350 0ustar nileshnilesh\name{DMNGroup-class} \Rdversion{1.1} \docType{class} \alias{DMNGroup-class} \title{Class \code{"DMNGroup"}} \description{Result from fitting a Dirichlet-Multinomial generative classifier.} \section{Objects from the Class}{ Objects can be created by calls to \code{\link{dmngroup}}. } \section{Slots}{ All slots in this class are inheritted from \code{SimpleList}; see \sQuote{Methods}, below, for information on how to manipulate this object. } \section{Extends}{ Class \code{"\linkS4class{SimpleList}"}, directly. Class \code{"\linkS4class{List}"}, by class "SimpleList", distance 2. Class \code{"\linkS4class{Vector}"}, by class "SimpleList", distance 3. Class \code{"\linkS4class{Annotated}"}, by class "SimpleList", distance 4. } \section{Methods}{ See the \code{\link{mixture}} help page for functions that operate on \code{DMNGroup} and \code{DMN}. \code{DMNGroup} can be manipulated as a list; see \code{\linkS4class{SimpleList} } for a description of typical list-like functions. } \author{ Martin Morgan \url{mailto:mtmorgan@fhcrc.org} } \seealso{ \code{\link{mixture}}, \code{\linkS4class{DMN}}, \code{\linkS4class{SimpleList}}. } \examples{ data(bestgrp) bestgrp bestgrp[[1]] } \keyword{classes} DirichletMultinomial/man/roc.Rd0000644000175000017500000000326614136047775016400 0ustar nileshnilesh\name{roc} \alias{roc} \title{Summarize receiver-operator characteristics} \description{ Returns a \code{data.frame} summarizing the cummulative true- and false-positive probabilities from expected and observed classifications. } \usage{ roc(exp, obs, ...) } \arguments{ \item{exp}{\code{logical()} vector of expected classifications to a particular group.} \item{obs}{Predicted probability of assignment to the group identified by \code{TRUE} values in \code{exp}. The length of \code{exp} and \code{obs} must be identical.} \item{\dots}{Additional arguments, available to methods.} } \value{ A \code{data.frame} with columns \item{TruePositive}{Cummulative probability of correct assignment.} \item{FalsePositive}{Cummulative probability of incorrect assignment.} } \author{Martin Morgan \url{mailto:mtmorgan@fhcrc.org}} \examples{ library(lattice) ## count matrix fl <- system.file(package="DirichletMultinomial", "extdata", "Twins.csv") count <- t(as.matrix(read.csv(fl, row.names=1))) ## phenotype fl <- system.file(package="DirichletMultinomial", "extdata", "TwinStudy.t") pheno0 <- scan(fl) lvls <- c("Lean", "Obese", "Overwt") pheno <- factor(lvls[pheno0 + 1], levels=lvls) names(pheno) <- rownames(count) ## count data used for cross-validation, and cross-validation count <- csubset(c("Lean", "Obese"), count, pheno) data(bestgrp) ## true, false positives from single-group classifier bst <- roc(pheno[rownames(count)] == "Obese", predict(bestgrp, count)[,"Obese"]) head(bst) ## lattice plot xyplot(TruePostive ~ FalsePositive, bst, type="l", xlab="False Positive", ylab="True Positive") } \keyword{stats} DirichletMultinomial/man/DirichletMultinomial-package.Rd0000644000175000017500000000166214136047775023326 0ustar nileshnilesh\name{DirichletMultinomial-package} \alias{DirichletMultinomial-package} \docType{package} \title{ Dirichlet-Multinomial Mixture Model Machine Learning for Microbiome Data } \description{ Dirichlet-multinomial mixture models can be used to describe variability in microbial metagenomic data. This package is an interface to code originally made available by Holmes, Harris, and Qunice, 2012, PLoS ONE 7(2): 1-15. } \details{ The estimation routine is from the LGPL-licensed (as stated on the corresponding googlecode page) source \url{http://microbedmm.googlecode.com/files/MicrobeDMMv1.0.tar.gz}, retrieved 17 Feburary 2012. The algorithm is described in Holmes I, Harris K, Quince C, 2012 Dirichlet Multinomial Mixtures: Generative Models for Microbial Metagenomics. PLoS ONE 7(2): e30126. doi:10.1371/journal.pone.0030126. } \author{Maintainer: Martin Morgan \url{mailto:mtmorgan@fhcrc.org}} \keyword{package} DirichletMultinomial/man/dataobjects.Rd0000644000175000017500000000170114136047775020070 0ustar nileshnilesh\name{data} \alias{fit} \alias{bestgrp} \alias{xval} \docType{data} \title{Data objects used for examples and the vignette} \description{ These data objects correspond to steps in a typical work flow, as described in the vignette to this package. \code{fit} corresponds to \code{dmn} fits to different values of \code{k}. \code{bestgroup} is the result of the two-group generative classifier. \code{xval} summarizes leave-one-out cross validation of the classifier. } \usage{ data(fit) data(bestgrp) data(xval) } \format{ \code{fit} is a list of seven \code{\linkS4class{DMN}} objects. \code{bestgrp} is a \code{\linkS4class{DMNGroup}} object. \code{xval} is a \code{data.frame} with columns corresponding to the cross-validation group membership and the Lean and Obese posterior probabilities. } \examples{ data(fit); fit[1:2] plot(sapply(fit, laplace), type="b") data(bestgrp); bestgrp data(xval); head(xval, 3) } \keyword{datasets} DirichletMultinomial/man/cvdmngroup.Rd0000644000175000017500000000500114136047775017766 0ustar nileshnilesh\name{cvdmngroup} \alias{cvdmngroup} \title{ Cross-validation on Dirichlet-Multinomial classifiers. } \description{ Run cross-validation on Dirichlet-Multinomial generative classifiers. } \usage{ cvdmngroup(ncv, count, k, z, ..., verbose = FALSE, .lapply = parallel::mclapply) } \arguments{ \item{ncv}{\code{integer(1)} number of cross-validation groups, between 2 and \code{nrow(count)}.} \item{count}{\code{matrix} of sample x taxon counts, subsets of which are used for training and cross-validation.} \item{k}{named \code{integer()} vector of groups and number of Dirichlet components; e.g., \code{c(Lean=1, Obese=3)} performs cross-validation for models with \code{k=1} Dirichlet components for the \sQuote{Lean} group, \code{k=3} Dirichlet components for \sQuote{Obese}.} \item{z}{True group assignment.} \item{\dots}{Additional arguments, passed to \code{\link{dmn}} during each cross-validation.} \item{verbose}{\code{logical(1)} indicating whether progress should be reported} \item{.lapply}{A function used to perform the outer cross-vaildation loop, e.g., \code{lapply} for calculation on a single processor, \code{parallel::mclapply} for parallel evaluation.} } \value{ A \code{data.frame} summarizing classifications of test samples in cross-validation groups. Columns are: \item{group}{The cross-validation group in which the indivdual was used for testing.} \item{additional columns}{Named after classification groups, giving the posterior probability of assignment.} } \author{Martin Morgan \url{mailto:mtmorgan@fhcrc.org}} \seealso{ \code{\link{dmn}}, \link{DirichletMultinomial-package}, \code{vignette("DirichletMultinomial")} } \examples{ data(xval) ## result of following commands head(xval) \dontrun{ ## count matrix fl <- system.file(package="DirichletMultinomial", "extdata", "Twins.csv") count <- t(as.matrix(read.csv(fl, row.names=1))) ## phenotype fl <- system.file(package="DirichletMultinomial", "extdata", "TwinStudy.t") pheno0 <- scan(fl) lvls <- c("Lean", "Obese", "Overwt") pheno <- factor(lvls[pheno0 + 1], levels=lvls) names(pheno) <- rownames(count) ## subset keep <- c("Lean", "Obese") count <- count[pheno %in% keep,] pheno <- factor(pheno[pheno %in% keep], levels=keep) ## cross-validation, single Dirichlet component for Lean, 3 for Obese xval <- cvdmngroup(nrow(count), count, c(Lean=1, Obese=3), pheno, verbose=TRUE, mc.preschedule=FALSE) }} \keyword{stats} DirichletMultinomial/man/DMN-class.Rd0000644000175000017500000000256314136047775017335 0ustar nileshnilesh\name{DMN-class} \Rdversion{1.1} \docType{class} \alias{DMN-class} \title{Class \code{"DMN"}} \description{Result from fitting a Dirichlet-Multinomial model.} \section{Objects from the Class}{ Objects can be created by calls to \code{\link{dmn}}.. } \section{Slots}{ The contents of a slot is usually retrieved via the methods described on the \code{\link{mixture}} help page. \describe{ \item{goodnessOfFit}{NLE, LogDet, Laplace, AIC, and BIC criteria assessing goodness-of-fit.} \item{group}{\code{matrix} of dimension samples x \code{k}, providing the Dirichlet parameter vectors.} \item{mixture}{\describe{ \item{Weight}{\code{numeric()} of length \code{k}, with relative weight of each component.} }} \item{fit}{\describe{ \item{Lower}{\code{matrix()} of dimension taxa x \code{k} with 95\% lower bounds on Dirichlet component vector estimates.} \item{Estimate}{\code{matrix()} of dimension taxa x \code{k} with Dirichlet component vector estimates.} \item{Upper}{\code{matrix()} of dimension taxa x \code{k} with 95\% upper bounds on Dirichlet component vector estimates.} }} } } \section{Methods}{ See the \code{\link{mixture}} help page. } \author{ Martin Morgan \url{mailto:mtmorgan@fhcrc.org} } \seealso{ \code{\link{dmn}}, \code{\link{mixture}}. } \examples{ data(fit) fit[[4]] } \keyword{classes} DirichletMultinomial/src/0000755000175000017500000000000014136070214015313 5ustar nileshnileshDirichletMultinomial/src/dirichlet_fit.h0000644000175000017500000000026414136047775020317 0ustar nileshnilesh#ifndef _DIRICHLET_FIT_H_ #define _DIRICHLET_FIT_H_ #include SEXP dirichlet_fit(SEXP counts, SEXP n_components, SEXP verbose, SEXP seed); #endif DirichletMultinomial/src/Makevars.win0000644000175000017500000000020014136047775017613 0ustar nileshnileshPKG_LIBS += -L$(LIB_GSL)$(R_ARCH)/lib -lgsl -lgslcblas -lm PKG_CPPFLAGS += -I$(RHOME)/src/include -I$(LIB_GSL)$(R_ARCH)/include DirichletMultinomial/src/dirichlet_fit_main.h0000644000175000017500000000064614136047775021327 0ustar nileshnilesh#ifndef _DIRICHLET_FIT_MAIN_H_ #define _DIRICHLET_FIT_MAIN_H_ #include struct data_t { Rboolean verbose; int N, S, K; const int *aanX; double* adPi; /* result */ double NLE, LogDet; double *group; double *mixture_wt; double fit_laplace, fit_bic, fit_aic, *fit_lower, *fit_mpe, *fit_upper; }; void dirichlet_fit_main(struct data_t *data, int rseed); #endif DirichletMultinomial/src/dirichlet_fit.c0000644000175000017500000000564414136047775020321 0ustar nileshnilesh#include "dirichlet_fit_main.h" #include "dirichlet_fit.h" SEXP dirichlet_fit(SEXP counts, SEXP n_components, SEXP verbose, SEXP seed) { /* counts: N communities x S taxa */ struct data_t *data = (struct data_t *) R_alloc(1, sizeof(struct data_t)); /* inputs */ SEXP dim = Rf_getAttrib(counts, R_DimSymbol), dimnames = Rf_getAttrib(counts, R_DimNamesSymbol); data->verbose = LOGICAL(verbose)[0]; data->N = INTEGER(dim)[0]; data->S = INTEGER(dim)[1]; data->K = INTEGER(n_components)[0]; data->aanX = INTEGER(counts); /* results */ SEXP result, elt, sxp, nms; PROTECT(result = Rf_allocVector(VECSXP, 4)); nms = Rf_allocVector(STRSXP, 4); Rf_namesgets(result, nms); SET_STRING_ELT(nms, 0, mkChar("GoodnessOfFit")); SET_STRING_ELT(nms, 1, mkChar("Group")); SET_STRING_ELT(nms, 2, mkChar("Mixture")); SET_STRING_ELT(nms, 3, mkChar("Fit")); sxp = Rf_allocVector(REALSXP, 5); /* GoodnessOfFit */ SET_VECTOR_ELT(result, 0, sxp); nms = Rf_allocVector(STRSXP, 5); Rf_namesgets(sxp, nms); SET_STRING_ELT(nms, 0, mkChar("NLE")); SET_STRING_ELT(nms, 1, mkChar("LogDet")); SET_STRING_ELT(nms, 2, mkChar("Laplace")); SET_STRING_ELT(nms, 3, mkChar("BIC")); SET_STRING_ELT(nms, 4, mkChar("AIC")); sxp = Rf_allocMatrix(REALSXP, data->N, data->K); /* Group */ SET_VECTOR_ELT(result, 1, sxp); nms = Rf_allocVector(VECSXP, 2); Rf_dimnamesgets(sxp, nms); SET_VECTOR_ELT(nms, 0, VECTOR_ELT(dimnames, 0)); SET_VECTOR_ELT(nms, 1, R_NilValue); data->group = REAL(sxp); elt = Rf_allocVector(VECSXP, 1); /* Mixture */ SET_VECTOR_ELT(result, 2, elt); nms = Rf_allocVector(STRSXP, 1); Rf_namesgets(elt, nms); SET_STRING_ELT(nms, 0, mkChar("Weight")); sxp = Rf_allocVector(REALSXP, data->K); SET_VECTOR_ELT(elt, 0, sxp); data->mixture_wt = REAL(sxp); elt = Rf_allocVector(VECSXP, 3); /* Fit */ SET_VECTOR_ELT(result, 3, elt); nms = Rf_allocVector(STRSXP, 3); Rf_namesgets(elt, nms); SET_STRING_ELT(nms, 0, mkChar("Lower")); SET_STRING_ELT(nms, 1, mkChar("Estimate")); SET_STRING_ELT(nms, 2, mkChar("Upper")); PROTECT(nms = Rf_allocVector(VECSXP, 2)); SET_VECTOR_ELT(nms, 0, VECTOR_ELT(dimnames, 1)); SET_VECTOR_ELT(nms, 1, R_NilValue); for (int i = 0; i < 3; ++i) { sxp = Rf_allocMatrix(REALSXP, data->S, data->K); SET_VECTOR_ELT(elt, i, sxp); Rf_dimnamesgets(sxp, nms); } UNPROTECT(1); data->fit_lower = REAL(VECTOR_ELT(elt, 0)); data->fit_mpe = REAL(VECTOR_ELT(elt, 1)); data->fit_upper = REAL(VECTOR_ELT(elt, 2)); dirichlet_fit_main(data, INTEGER(seed)[0]); elt = VECTOR_ELT(result , 0); REAL(elt)[0] = data->NLE; REAL(elt)[1] = data->LogDet; REAL(elt)[2] = data->fit_laplace; REAL(elt)[3] = data->fit_bic; REAL(elt)[4] = data->fit_aic; UNPROTECT(1); return result; } DirichletMultinomial/src/Makevars0000644000175000017500000000013314136047775017024 0ustar nileshnileshPKG_CFLAGS = `gsl-config --cflags` -DR_DIRICHLETMULTINOMIAL PKG_LIBS = `gsl-config --libs` DirichletMultinomial/src/dirichlet_fit_main.c0000644000175000017500000004364214136047775021325 0ustar nileshnilesh#include #include #include #include #include #include #include #include "dirichlet_fit_main.h" /* re-map to R transient memory allocation */ #define calloc(_nelm, _elsize) R_alloc(_nelm, _elsize) #define free(_ptr) (void) _ptr const double BIG_DBL = 1.0e9; const double K_MEANS_THRESH = 1.0e-6; const double SOFT_BETA = 50.0; const double GAMMA_ITA = 0.1; const double GAMMA_NU = 0.1; const int MAX_ITER = 1000; const size_t MAX_GRAD_ITER = 1000; static void kmeans(struct data_t *data, gsl_rng *ptGSLRNG, double* adW, double **aadZ, double **aadMu) { const int S = data->S, N = data->N, K = data->K, *aanX = data->aanX; int i, j, k, iter = 0; double *aadY, *adMu; double dMaxChange = BIG_DBL; if (data->verbose) Rprintf(" Soft kmeans\n"); aadY = (double *) calloc(N * S, sizeof(double)); adMu = (double *) calloc(S, sizeof(double)); for (i = 0; i < N; i++) { double dTotal = 0.0; for (j = 0; j < S; j++) dTotal += aanX[j * N + i]; for (j = 0; j < S; j++) aadY[j * N + i] = (aanX[j * N + i]) / dTotal; } /* initialise */ for (i = 0; i < N; i++) { k = gsl_rng_uniform_int (ptGSLRNG, K); for (j = 0; j < K; j++) aadZ[j][i] = 0.0; aadZ[k][i] = 1.0; } while (dMaxChange > K_MEANS_THRESH && iter < MAX_ITER) { /* update mu */ dMaxChange = 0.0; for (i = 0; i < K; i++){ double dNormChange = 0.0; adW[i] = 0.0; for (j = 0; j < N; j++) adW[i] += aadZ[i][j]; for (j = 0; j < S; j++) { adMu[j] = 0.0; for (k = 0; k < N; k++) adMu[j] += aadZ[i][k] * aadY[j * N + k]; } for (j = 0; j < S; j++) { double dDiff = 0.0; adMu[j] /= adW[i]; dDiff = (adMu[j] - aadMu[i][j]); dNormChange += dDiff * dDiff; aadMu[i][j] = adMu[j]; } dNormChange = sqrt(dNormChange); if (dNormChange > dMaxChange) dMaxChange = dNormChange; } /* calc distances and update Z */ for (i = 0; i < N; i++) { double dNorm = 0.0, adDist[K]; for (k = 0; k < K; k++) { adDist[k] = 0.0; for (j = 0; j < S; j++) { const double dDiff = aadMu[k][j] - aadY[j * N + i]; adDist[k] += dDiff * dDiff; } adDist[k] = sqrt(adDist[k]); dNorm += exp(-SOFT_BETA * adDist[k]); } for (k = 0; k < K; k++) aadZ[k][i] = exp(-SOFT_BETA * adDist[k]) / dNorm; } iter++; if (data->verbose && (iter % 10 == 0)) Rprintf(" iteration %d change %f\n", iter, dMaxChange); } free(aadY); free(adMu); } static double neg_log_evidence_lambda_pi(const gsl_vector *lambda, void *params) { int i, j; const struct data_t *data = (const struct data_t *) params; const int S = data->S, N = data->N, *aanX = data->aanX; const double *adPi = data->adPi; double dLogE = 0.0, dLogEAlpha = 0.0, dSumAlpha = 0.0, dSumLambda = 0.0; double adSumAlphaN[N], dWeight = 0.0; for (i = 0; i < N; i++) { adSumAlphaN[i] = 0.0; dWeight += adPi[i]; } for (j = 0; j < S; j++) { const double dLambda = gsl_vector_get(lambda, j); const double dAlpha = exp(dLambda); dLogEAlpha += gsl_sf_lngamma(dAlpha); dSumLambda += dLambda; dSumAlpha += dAlpha; const double lngammaAlpha0 = gsl_sf_lngamma(dAlpha); for (i = 0; i < N; i++) { const double dN = aanX[j * N + i]; const double dAlphaN = dAlpha + dN; const double lngammaAlphaN = dN ? gsl_sf_lngamma(dAlphaN) : lngammaAlpha0; adSumAlphaN[i] += dAlphaN; /*weight by pi*/ dLogE -= adPi[i] * lngammaAlphaN; /*weight by pi*/ } } dLogEAlpha -= gsl_sf_lngamma(dSumAlpha); for(i = 0; i < N; i++) dLogE += adPi[i] * gsl_sf_lngamma(adSumAlphaN[i]); return dLogE + dWeight*dLogEAlpha + GAMMA_NU*dSumAlpha - GAMMA_ITA * dSumLambda; } static void neg_log_derive_evidence_lambda_pi(const gsl_vector *ptLambda, void *params, gsl_vector* g) { const struct data_t *data = (const struct data_t *) params; const int S = data->S, N = data->N, *aanX = data->aanX; const double *adPi = data->adPi; int i, j; double adDeriv[S], adStore[N], adAlpha[S]; double dSumStore = 0.0, dStore = 0.0; double dWeight = 0; for (i = 0; i < N; i++) { adStore[i] = 0.0; dWeight += adPi[i]; } for (j = 0; j < S; j++) { adAlpha[j] = exp(gsl_vector_get(ptLambda, j)); dStore += adAlpha[j]; adDeriv[j] = dWeight* gsl_sf_psi(adAlpha[j]); double alphaS0 = gsl_sf_psi(adAlpha[j]); for (i = 0; i < N; i++) { int dN = aanX[j * N + i]; double dAlphaN = adAlpha[j] + dN; double psiAlphaN = dN ? gsl_sf_psi(dAlphaN) : alphaS0; adDeriv[j] -= adPi[i] * psiAlphaN; adStore[i] += dAlphaN; } } for (i = 0; i < N; i++) dSumStore += adPi[i] * gsl_sf_psi(adStore[i]); dStore = dWeight * gsl_sf_psi(dStore); for (j = 0; j < S; j++) { double value = adAlpha[j] * (GAMMA_NU + adDeriv[j] - dStore + dSumStore) - GAMMA_ITA; gsl_vector_set(g, j, value); } } static void neg_log_FDF_lamba_pi(const gsl_vector *x, void *params, double *f, gsl_vector *g) { *f = neg_log_evidence_lambda_pi(x, params); neg_log_derive_evidence_lambda_pi(x, params, g); } static void optimise_lambda_k(double *adLambdaK, struct data_t *data, double *adZ) { const int S = data->S; int i, status; size_t iter = 0; const gsl_multimin_fdfminimizer_type *T; gsl_multimin_fdfminimizer *s; gsl_multimin_function_fdf fdf; gsl_vector *ptLambda; /*initialise vector*/ ptLambda = gsl_vector_alloc(S); for (i = 0; i < S; i++) gsl_vector_set(ptLambda, i, adLambdaK[i]); /*initialise function to be solved*/ data->adPi = adZ; fdf.n = S; fdf.f = neg_log_evidence_lambda_pi; fdf.df = neg_log_derive_evidence_lambda_pi; fdf.fdf = neg_log_FDF_lamba_pi; fdf.params = data; T = gsl_multimin_fdfminimizer_vector_bfgs2; s = gsl_multimin_fdfminimizer_alloc(T, S); gsl_multimin_fdfminimizer_set(s, &fdf, ptLambda, 1.0e-6, 0.1); do { iter++; status = gsl_multimin_fdfminimizer_iterate(s); if (status) break; status = gsl_multimin_test_gradient(s->gradient, 1e-3); } while (status == GSL_CONTINUE && iter < MAX_GRAD_ITER); for (i = 0; i < S; i++) adLambdaK[i] = gsl_vector_get(s->x, i); gsl_vector_free(ptLambda); gsl_multimin_fdfminimizer_free(s); } static double neg_log_evidence_i(const struct data_t *data, const int *anX, const double* adLambda, const double* aadLnGammaLambda0) { int j; const int S = data->S, N = data->N; double dLogE = 0.0, dLogEAlpha = 0.0, dSumAlpha = 0.0, dSumAlphaN = 0.0; for (j = 0; j < S; j++) { const double n = anX[j * N]; const double dAlpha = exp(adLambda[j]); const double dAlphaN = n + dAlpha; dLogEAlpha += aadLnGammaLambda0[j]; dSumAlpha += dAlpha; dSumAlphaN += dAlphaN; dLogE -= n ? gsl_sf_lngamma(dAlphaN) : aadLnGammaLambda0[j] ; } dLogEAlpha -= gsl_sf_lngamma(dSumAlpha); dLogE += gsl_sf_lngamma(dSumAlphaN); return dLogE + dLogEAlpha; } static void calc_z(double **aadZ, const struct data_t *data, const double *adW, double **aadLambda) { int i, k, j; const int N = data->N, K = data->K, S = data->S; double adStore[K]; double *aadLngammaLambda0 = (double*)calloc(S*K,sizeof(double)); for(k = 0; k < K; k++) { for(j = 0; j < S; j++) { const double dAlpha = exp(aadLambda[k][j]); aadLngammaLambda0[k*S +j] = gsl_sf_lngamma(dAlpha); } } for (i = 0; i < N; i ++) { double dSum = 0.0; double dOffset = BIG_DBL; for (k = 0; k < K; k++) { double dNegLogEviI = neg_log_evidence_i(data, data->aanX + i, aadLambda[k], aadLngammaLambda0 + k*S); if (dNegLogEviI < dOffset) dOffset = dNegLogEviI; adStore[k] = dNegLogEviI; } for (k = 0; k < K; k++) { aadZ[k][i] = adW[k] * exp(-(adStore[k] - dOffset)); dSum += aadZ[k][i]; } for (k = 0; k < K; k++) aadZ[k][i] /= dSum; } } static double neg_log_likelihood(double *adW, double** aadLambda, const struct data_t *data) { const int S = data->S, N = data->N, K = data->K, *aanX = data->aanX; int i, j, k; double adPi[K], adLogBAlpha[K]; double dRet = 0.0, dL5 = 0.0, dL6 = 0.0, dL7 = 0.0, dL8 = 0.0; double dK = K, dN = N, dS = S; double *aadLngammaLambda0 = (double*)calloc(S*K,sizeof(double)); for (k = 0; k < K; k++){ double dSumAlphaK = 0.0; adLogBAlpha[k] = 0.0; adPi[k] = adW[k]/dN; for (j = 0; j < S; j++){ double dAlpha = exp(aadLambda[k][j]); double lngammaAlpha = gsl_sf_lngamma(dAlpha); aadLngammaLambda0[k * S + j] = lngammaAlpha; dSumAlphaK += dAlpha; adLogBAlpha[k] += lngammaAlpha; } adLogBAlpha[k] -= gsl_sf_lngamma(dSumAlphaK); } for (i = 0; i < N; i++) { double dProb = 0.0, dFactor = 0.0, dSum = 0.0, adLogStore[K], dOffset = -BIG_DBL; for (j = 0; j < S; j++) { dSum += aanX[j * N + i]; dFactor += gsl_sf_lngamma(aanX[j * N + i] + 1.0); } dFactor -= gsl_sf_lngamma(dSum + 1.0); for (k = 0; k < K; k++) { double dSumAlphaKN = 0.0, dLogBAlphaN = 0.0; for (j = 0; j < S; j++) { int countN = aanX[j * N + i]; double dAlphaN = exp(aadLambda[k][j]) + countN; dSumAlphaKN += dAlphaN; dLogBAlphaN += countN ? gsl_sf_lngamma(dAlphaN) : aadLngammaLambda0[k * S + j]; } dLogBAlphaN -= gsl_sf_lngamma(dSumAlphaKN); adLogStore[k] = dLogBAlphaN - adLogBAlpha[k] - dFactor; if (adLogStore[k] > dOffset) dOffset = adLogStore[k]; } for (k = 0; k < K; k++) dProb += adPi[k]*exp(-dOffset + adLogStore[k]); dRet += log(dProb)+dOffset; } dL5 = -dS * dK * gsl_sf_lngamma(GAMMA_ITA); dL6 = GAMMA_ITA * dK * dS * log(GAMMA_NU); for (i = 0; i < K; i++) for (j = 0; j < S; j++) { dL7 += exp(aadLambda[i][j]); dL8 += aadLambda[i][j]; } dL7 *= -GAMMA_NU; dL8 *= GAMMA_ITA; return -dRet -dL5 - dL6 -dL7 -dL8; } static void hessian(gsl_matrix* ptHessian, const double* adLambda, const struct data_t *data) { const int S = data->S, N = data->N, *aanX = data->aanX; const double *adPi = data->adPi; int i = 0, j = 0; double adAlpha[S], adAJK[S], adCJK[S], adAJK0[S], adCJK0[S]; double dCK0 = 0.0, dAK0; double dCSum, dAlphaSum = 0.0, dW = 0.0, dCK = 0.0, dAK; for (j = 0; j < S; j++) { adAlpha[j] = exp(adLambda[j]); dAlphaSum += adAlpha[j]; adAJK0[j] = adAJK[j] = adCJK0[j] = adCJK[j] = 0.0; const double dPsiAlpha = gsl_sf_psi(adAlpha[j]); const double dPsi1Alpha = gsl_sf_psi_1(adAlpha[j]); for (i = 0; i < N; i++) { const int n = aanX[j * N + i]; adCJK0[j] += adPi[i] * n ? gsl_sf_psi(adAlpha[j] + n) : dPsiAlpha; adAJK0[j] += adPi[i] * dPsiAlpha; adCJK[j] += adPi[i] * n ? gsl_sf_psi_1(adAlpha[j] + n): dPsi1Alpha; adAJK[j] += adPi[i] * dPsi1Alpha; } } for (i = 0; i < N; i++) { dW += adPi[i]; dCSum = 0.0; for (j = 0; j < S; j++) dCSum += adAlpha[j] + aanX[j * N + i]; dCK += adPi[i]*gsl_sf_psi_1(dCSum); dCK0 += adPi[i]*gsl_sf_psi(dCSum); } dAK = dW * gsl_sf_psi_1(dAlphaSum); dAK0 = dW * gsl_sf_psi(dAlphaSum); for (i = 0; i < S; i++) for (j = 0; j < S; j++) { double dVal = 0.0; if (i == j) { double dG1 = -adAlpha[i] * (dAK0 - dCK0 + adCJK0[i] - adAJK0[i]); double dG2 = -adAlpha[i] * adAlpha[i]*(dAK - dCK + adCJK[i] - adAJK[i]); double dG3 = adAlpha[i]*GAMMA_NU; dVal = dG1 + dG2 + dG3; } else dVal = -adAlpha[i] * adAlpha[j] * (dAK - dCK); gsl_matrix_set(ptHessian, i, j, dVal); } } static void group_output(struct data_t *data, double** aadZ) { const int N = data->N, K = data->K; int i, k; for(k = 0; k < K; k++) for (i = 0; i < N; i++) data->group[k * N + i] = aadZ[k][i]; } static void mixture_output(struct data_t *data, double *adW, double** aadLambda, double **aadErr) { const int N = data->N, S = data->S; int i, k; for (k = 0; k < data->K; k++) data->mixture_wt[k] = adW[k] / N; for (i = 0; i < data->S; i++) { for (k = 0; k < data->K; k++) { double dErr = aadErr[k][i], dL = 0.0, dU = 0.0; int bIll = FALSE; if (dErr >= 0.0) { dErr = sqrt(dErr); if (dErr < 100.0) { dL = exp(aadLambda[k][i] - 2.0*dErr); dU = exp(aadLambda[k][i] + 2.0*dErr); } else bIll = TRUE; } else bIll = TRUE; if (bIll) dL = dU = R_NaN; data->fit_lower[k * S + i] = dL; data->fit_mpe[k * S + i] = exp(aadLambda[k][i]); data->fit_upper[k * S + i] = dU; } } } void dirichlet_fit_main(struct data_t *data, int rseed) { const int N = data->N, S = data->S, K = data->K; int i, j, k; gsl_rng *ptGSLRNG; gsl_rng_env_setup(); gsl_set_error_handler_off(); ptGSLRNG = gsl_rng_alloc(gsl_rng_default); gsl_set_error_handler_off(); gsl_rng_set(ptGSLRNG, rseed); /* allocate matrices */ double **aadZ, **aadLambda, **aadErr, *adW; adW = (double *) calloc(K, sizeof(double)); aadZ = (double **) calloc(K, sizeof(double *)); aadLambda = (double **) calloc(K, sizeof(double *)); aadErr = (double **) calloc(K, sizeof(double*)); aadZ[0] = (double *) calloc(K * N, sizeof(double)); aadLambda[0] = (double *) calloc(K * S, sizeof(double)); aadErr[0] = (double *) calloc(K * S, sizeof(double)); for (k = 1; k < K; k++) { aadZ[k] = aadZ[0] + k * N; aadLambda[k] = aadLambda[0] + k * S; aadErr[k] = aadErr[0] + k * S; } /* soft k means initialiser */ kmeans(data, ptGSLRNG, adW, aadZ, aadLambda); for (k = 0; k < K; k++) { adW[k] = 0.0; for (i = 0; i < N; i++) adW[k] += aadZ[k][i]; } if (data->verbose) Rprintf(" Expectation Maximization setup\n"); for (k = 0; k < K; k++) { for (j = 0; j < S; j++) { const double x = aadLambda[k][j]; aadLambda[k][j] = (x > 0.0) ? log(x) : -10; } optimise_lambda_k(aadLambda[k], data, aadZ[k]); } /* simple EM algorithm */ int iter = 0; double dNLL = 0.0, dNew, dChange = BIG_DBL; if (data->verbose) Rprintf(" Expectation Maximization\n"); while (dChange > 1.0e-6 && iter < 100) { calc_z(aadZ, data, adW, aadLambda); /* latent var expectation */ for (k = 0; k < K; k++) /* mixture components, given pi */ optimise_lambda_k(aadLambda[k], data, aadZ[k]); for (k = 0; k < K; k++) { /* current likelihood & weights */ adW[k] = 0.0; for(i = 0; i < N; i++) adW[k] += aadZ[k][i]; } dNew = neg_log_likelihood(adW, aadLambda, data); dChange = fabs(dNLL - dNew); dNLL = dNew; iter++; R_CheckUserInterrupt(); if (data->verbose && (iter % 10) == 0) Rprintf(" iteration %d change %f\n", iter, dChange); } /* hessian */ if (data->verbose) Rprintf(" Hessian\n"); gsl_matrix *ptHessian = gsl_matrix_alloc(S, S), *ptInverseHessian = gsl_matrix_alloc(S, S); gsl_permutation *p = gsl_permutation_alloc(S); double dLogDet = 0., dTemp; int signum, status; for (k = 0; k < K; k++) { data->adPi = aadZ[k]; if (k > 0) dLogDet += 2.0 * log(N) - log(adW[k]); hessian(ptHessian, aadLambda[k], data); status = gsl_linalg_LU_decomp(ptHessian, p, &signum); gsl_linalg_LU_invert(ptHessian, p, ptInverseHessian); for (j = 0; j < S; j++) { aadErr[k][j] = gsl_matrix_get(ptInverseHessian, j, j); dTemp = gsl_matrix_get(ptHessian, j, j); dLogDet += log(fabs(dTemp)); } } gsl_matrix_free(ptHessian); gsl_matrix_free(ptInverseHessian); gsl_permutation_free(p); /* results */ double dP = K * S + K - 1; data->NLE = dNLL; data->LogDet = dLogDet; data->fit_laplace = dNLL + 0.5 * dLogDet - 0.5 * dP * log(2. * M_PI); data->fit_bic = dNLL + 0.5 * log(N) * dP; data->fit_aic = dNLL + dP; group_output(data, aadZ); mixture_output(data, adW, aadLambda, aadErr); free(aadErr[0]); free(aadErr); free(aadLambda[0]); free(aadLambda); free(aadZ[0]); free(aadZ); free(adW); } DirichletMultinomial/src/R_init_DirichletMultinomial.c0000644000175000017500000000045014136047775023124 0ustar nileshnilesh#include #include "dirichlet_fit.h" static const R_CallMethodDef callMethods[] = { { ".dirichlet_fit", (DL_FUNC) &dirichlet_fit, 4}, {NULL, NULL, 0} }; void R_init_DirichletMultinomial(DllInfo * info) { R_registerRoutines(info, NULL, callMethods, NULL, NULL); } DirichletMultinomial/vignettes/0000755000175000017500000000000014136070214016534 5ustar nileshnileshDirichletMultinomial/vignettes/References.bib0000644000175000017500000000520014136047775021310 0ustar nileshnilesh@article{10.1371/journal.pone.0030126, author = {Holmes, , Ian AND Harris, , Keith AND Quince, , Christopher}, journal = {PLoS ONE}, publisher = {Public Library of Science}, title = {Dirichlet Multinomial Mixtures: Generative Models for Microbial Metagenomics}, year = {2012}, month = {02}, volume = {7}, url = {http://dx.doi.org/10.1371%2Fjournal.pone.0030126}, pages = {e30126}, abstract = {

We introduce Dirichlet multinomial mixtures (DMM) for the probabilistic modelling of microbial metagenomics data. This data can be represented as a frequency matrix giving the number of times each taxa is observed in each sample. The samples have different size, and the matrix is sparse, as communities are diverse and skewed to rare taxa. Most methods used previously to classify or cluster samples have ignored these features. We describe each community by a vector of taxa probabilities. These vectors are generated from one of a finite number of Dirichlet mixture components each with different hyperparameters. Observed samples are generated through multinomial sampling. The mixture components cluster communities into distinct ‘metacommunities’, and, hence, determine envirotypes or enterotypes, groups of communities with a similar composition. The model can also deduce the impact of a treatment and be used for classification. We wrote software for the fitting of DMM models using the ‘evidence framework’ (http://code.google.com/p/microbedmm/). This includes the Laplace approximation of the model evidence. We applied the DMM model to human gut microbe genera frequencies from Obese and Lean twins. From the model evidence four clusters fit this data best. Two clusters were dominated by Bacteroides and were homogenous; two had a more variable community composition. We could not find a significant impact of body mass on community structure. However, Obese twins were more likely to derive from the high variance clusters. We propose that obesity is not associated with a distinct microbiota but increases the chance that an individual derives from a disturbed enterotype. This is an example of the ‘Anna Karenina principle (AKP)’ applied to microbial communities: disturbed states having many more configurations than undisturbed. We verify this by showing that in a study of inflammatory bowel disease (IBD) phenotypes, ileal Crohn's disease (ICD) is associated with a more variable community.

}, number = {2}, doi = {10.1371/journal.pone.0030126} } DirichletMultinomial/vignettes/DirichletMultinomial.Rnw0000644000175000017500000002327514136047775023377 0ustar nileshnilesh%\VignetteIndexEntry{An introduction to DirichletMultinomial} %\VignetteDepends{} %\VignetteKeywords{Microbial metagenomic clustering and classification} %\VignettePackage{DirichletMultinomial} \documentclass[]{article} \usepackage{authblk} \usepackage{times} \usepackage{hyperref} \newcommand{\Rfunction}[1]{{\texttt{#1}}} \newcommand{\Robject}[1]{{\texttt{#1}}} \newcommand{\Rpackage}[1]{{\textit{#1}}} \newcommand{\Rfunarg}[1]{{\texttt{#1}}} \newcommand{\Rclass}[1]{{\textit{#1}}} \newcommand{\Rcode}[1]{{\texttt{#1}}} \newcommand{\software}[1]{\textsf{#1}} \newcommand{\R}{\software{R}} \newcommand{\DirichletMultinomial}{\Rpackage{DirichletMultinomial}} \title{\Rpackage{DirichletMultinomial} for Clustering and Classification of Microbiome Data} \author{Martin Morgan} \date{Modified: 6 March 2012. Compiled: \today} \begin{document} \maketitle This document illustrates the main features of the \Rpackage{DirichletMultinomial} package, and in the process replicates key tables and figures from \cite{10.1371/journal.pone.0030126}. We start by loading the package, in addition to the packages \Rpackage{lattice} (for visualization) and \Rpackage{parallel} (for use of multiple cores during cross-validation). %% <>= library(DirichletMultinomial) library(lattice) library(xtable) library(parallel) @ %% We set the width of \R{} output to 70 characters, and the number of floating point digits displayed to two. The \Robject{full} flag is set to \Rcode{FALSE}, so that cached values are used instead of re-computing during production of this vignette. The package defines a set of standard colors; we use \Rcode{.qualitative} during visualization. \Rfunction{dev.off} is redefined to return without displaying results %% <>= options(width=70, digits=2) full <- FALSE .qualitative <- DirichletMultinomial:::.qualitative dev.off <- function(...) invisible(grDevices::dev.off(...)) @ \section{Data} The data used in \cite{10.1371/journal.pone.0030126} is included in the package. We read the data in to a matrix \Robject{count} of samples $\times$ taxa. %% <>= fl <- system.file(package="DirichletMultinomial", "extdata", "Twins.csv") count <- t(as.matrix(read.csv(fl, row.names=1))) count[1:5, 1:3] @ %% Figure~\ref{fig:taxon-counts} shows the distribution of reads from each taxon, on a log scale. %% <>= cnts <- log10(colSums(count)) pdf("taxon-counts.pdf") densityplot(cnts, xlim=range(cnts), xlab="Taxon representation (log 10 count)") dev.off() @ \begin{figure} \centering \includegraphics[width=.65\textwidth]{taxon-counts} \caption{Density of taxa, across samples} \label{fig:taxon-counts} \end{figure} \section{Clustering} The \Rfunction{dmn} function fits a Dirichlet-Multinomial model, taking as input the count data and a parameter $k$ representing the number of Dirichlet components to model. Here we fit the count data to values of $k$ from 1 to 7, displaying the result for $k = 4$. A sense of the model return value is provided by the documentation for the \R{} object \Robject{fit}, \Rcode{class ? DMN}. %% <>= if (full) { fit <- mclapply(1:7, dmn, count=count, verbose=TRUE) save(fit, file=file.path(tempdir(), "fit.rda")) } else data(fit) fit[[4]] @ %% The return value can be queried for measures of fit (Laplace, AIC, BIC); these are plotted for different $k$ in Figure~\ref{fig:min-laplace}. The best fit is for $k=4$ distinct Dirichlet components. %% <>= lplc <- sapply(fit, laplace) pdf("min-laplace.pdf") plot(lplc, type="b", xlab="Number of Dirichlet Components", ylab="Model Fit") dev.off() (best <- fit[[which.min(lplc)]]) @ %% In addition to \Rfunction{laplace} goodness of fit can be assessed with the \Rfunction{AIC} and \Rfunction{BIC} functions. \begin{figure} \centering \includegraphics[width=.65\textwidth]{min-laplace} \caption{Model fit as a function of Dirichlet component number} \label{fig:min-laplace} \end{figure} The \Rfunction{mixturewt} function reports the weight $\pi$ and homogeneity $\theta$ (large values are more homogeneous) of the fitted model. \Rfunction{mixture} returns a matrix of sample x estimated Dirichlet components; the argument \Rfunarg{assign} returns a vector of length equal to the number of samples indicating the component with maximum value. %% <>= mixturewt(best) head(mixture(best), 3) @ %% The \Rfunction{fitted} function describes the contribution of each taxonomic group (each point in the panels of Figure~\ref{fig:fitted} to the Dirichlet components; the diagonal nature of the points in a panel suggest that the Dirichlet components are correlated, perhaps reflecting overall numerical abundance. %% <>= pdf("fitted.pdf") splom(log(fitted(best))) dev.off() @ \begin{figure} \centering \includegraphics[width=.65\textwidth]{fitted} \caption{Taxa fitted to Dirichlet components 1-4.} \label{fig:fitted} \end{figure} <>= @ <>= @ The posterior mean difference between the best and single-component Dirichlet multinomial model measures how each component differs from the population average; the sum is a measure of total difference from the mean. %% <>= p0 <- fitted(fit[[1]], scale=TRUE) # scale by theta p4 <- fitted(best, scale=TRUE) colnames(p4) <- paste("m", 1:4, sep="") (meandiff <- colSums(abs(p4 - as.vector(p0)))) sum(meandiff) @ %% Table~\ref{tab:meandiff} summarizes taxonomic contributions to each Dirichlet component. %% <>= diff <- rowSums(abs(p4 - as.vector(p0))) o <- order(diff, decreasing=TRUE) cdiff <- cumsum(diff[o]) / sum(diff) df <- head(cbind(Mean=p0[o], p4[o,], diff=diff[o], cdiff), 10) @ <>= xtbl <- xtable(df, caption="Taxonomic contributions (10 largest) to Dirichlet components.", label="tab:meandiff", align="lccccccc") print(xtbl, hline.after=0, caption.placement="top") @ Figure~\ref{fig:heatmap1} shows samples arranged by Dirichlet component, with samples placed into the component for which they had the largest fitted value. %% <>= pdf("heatmap1.pdf") heatmapdmn(count, fit[[1]], best, 30) dev.off() @ \begin{figure} \centering \includegraphics[width=.65\textwidth]{heatmap1} \caption{Samples arranged by Dirichlet component. Narrow columns are samples, broader columns component averages. Rows are taxonomic groups. Color represents square-root counts, with dark colors corresponding to larger counts.} \label{fig:heatmap1} \end{figure} \section{Generative classifier} The following reads in phenotypic information (`Lean', `Obese', `Overweight') for each sample. %% <>= fl <- system.file(package="DirichletMultinomial", "extdata", "TwinStudy.t") pheno0 <- scan(fl) lvls <- c("Lean", "Obese", "Overwt") pheno <- factor(lvls[pheno0 + 1], levels=lvls) names(pheno) <- rownames(count) table(pheno) @ %% Here we subset the count data into sub-counts, one for each phenotype. We retain only the Lean and Obese groups for subsequent analysis. %% <>= counts <- lapply(levels(pheno), csubset, count, pheno) sapply(counts, dim) keep <- c("Lean", "Obese") count <- count[pheno %in% keep,] pheno <- factor(pheno[pheno %in% keep], levels=keep) @ The \Rfunction{dmngroup} function identifies the best (minimum Laplace score) Dirichlet-multinomial model for each group. %% <>= if (full) { bestgrp <- dmngroup(count, pheno, k=1:5, verbose=TRUE, mc.preschedule=FALSE) save(bestgrp, file=file.path(tempdir(), "bestgrp.rda")) } else data(bestgrp) @ %% The Lean group is described by a model with one component, the Obese group by a model with three components. Three of the four Dirichlet components of the original single group (\Rcode{best}) model are represented in the Obese group, the other in the Lean group. The total Laplace score of the two group model is less than of the single-group model, indicating information gain from considering groups separately. %% <>= bestgrp lapply(bestgrp, mixturewt) c(sapply(bestgrp, laplace), `Lean+Obese`=sum(sapply(bestgrp, laplace)), Single=laplace(best)) @ The \Rfunction{predict} function assigns samples to classes; the confusion matrix shows that the classifier is moderately effective. %% <>= xtabs(~pheno + predict(bestgrp, count, assign=TRUE)) @ %% The \Rfunction{cvdmngroup} function performs cross-validation. This is a computationally expensive step. %% <>= if (full) { ## full leave-one-out; expensive! xval <- cvdmngroup(nrow(count), count, c(Lean=1, Obese=3), pheno, verbose=TRUE, mc.preschedule=FALSE) save(xval, file=file.path(tempdir(), "xval.rda")) } else data(xval) @ %% Figure~\ref{fig:roc} shows an ROC curve for the single and two-group classifier. The single group classifier is performing better than the two-group classifier. %% <>= bst <- roc(pheno[rownames(count)] == "Obese", predict(bestgrp, count)[,"Obese"]) bst$Label <- "Single" two <- roc(pheno[rownames(xval)] == "Obese", xval[,"Obese"]) two$Label <- "Two group" both <- rbind(bst, two) pars <- list(superpose.line=list(col=.qualitative[1:2], lwd=2)) pdf("roc.pdf") xyplot(TruePostive ~ FalsePositive, group=Label, both, type="l", par.settings=pars, auto.key=list(lines=TRUE, points=FALSE, x=.6, y=.1), xlab="False Positive", ylab="True Positive") dev.off() @ \begin{figure} \centering \includegraphics[width=.65\textwidth]{roc} \caption{Receiver-operator curves for the single and two-group classifiers.} \label{fig:roc} \end{figure} <>= toLatex(sessionInfo()) @ \bibliographystyle{abbrv} \bibliography{References} \end{document} DirichletMultinomial/build/0000755000175000017500000000000014136070214015623 5ustar nileshnileshDirichletMultinomial/build/vignette.rds0000644000175000017500000000041114136070214020156 0ustar nileshnileshQj0 [ Bn Q(e]]]۾|6cz@dޓ)c,c,)3r 6\u-7OpO/Nu-ue uF/ƴI;4B4yJ(۵hNS)jP2.|IW0Œ/b |]-/)ߤZ}|_?ďp KFz%ʲď)R *DirichletMultinomial/R/0000755000175000017500000000000014136047775014745 5ustar nileshnileshDirichletMultinomial/R/AllGenerics.R0000644000175000017500000000010114136047775017250 0ustar nileshnilesh## promoted generics setGeneric("fitted") setGeneric("predict") DirichletMultinomial/R/dmn.R0000644000175000017500000001044314136047775015650 0ustar nileshnileshsetClass("DMN", representation=representation(goodnessOfFit="numeric", group="matrix", mixture="list", fit="list")) .DMN <- function(goodnessOfFit, group, mixture, fit, ...) { new("DMN", goodnessOfFit=goodnessOfFit, group=group, mixture=mixture, fit=fit, ...) } dmn <- function(count, k, verbose=FALSE, seed=runif(1, 0, .Machine$integer.max)) { if (verbose) message(sprintf("dmn, k=%d", k)) if (any(rowSums(count) == 0L)) stop("some 'rowSums()' on the dmn() count matrix equal 0") mode(count) <- "integer" ans <- .Call(.dirichlet_fit, count, as.integer(k), as.logical(verbose), as.integer(seed)) o <- order(ans$Mixture$Weight, decreasing=TRUE) ans <- within(ans, { Group <- Group[,o, drop=FALSE] Mixture$Weight <- Mixture$Weight[o] Fit <- lapply(Fit, function(elt, o) elt[, o, drop=FALSE], o) }) with(ans, .DMN(goodnessOfFit=GoodnessOfFit, group=Group, mixture=Mixture, fit=Fit)) } ## k-means mixture <- function(object, ..., assign=FALSE) { if (assign) { apply(mixture(object), 1, which.max) } else { object@group } } ## Dirichlet goodnessOfFit <- function(object, ...) object@goodnessOfFit laplace <- function(object, ...) goodnessOfFit(object)[["Laplace"]] .AIC.DMN <- function(object, ...) goodnessOfFit(object)[["AIC"]] setMethod(AIC, "DMN", .AIC.DMN) .BIC.DMN <- function(object, ...) goodnessOfFit(object)[["BIC"]] setMethod(BIC, "DMN", .BIC.DMN) mixturewt <- function(object, ...) { data.frame(pi=object@mixture$Weight, theta=colSums(fitted(object))) } .fitted.DMN <- function(object, ..., scale=FALSE) { fit <- object@fit$Estimate if (scale) fit <- scale(fit, FALSE, mixturewt(object)$theta) fit } setMethod(fitted, "DMN", .fitted.DMN) ## predict .neg_log_evidence_i <- function(x, alpha) { .B <- function(x) sum(lgamma(x)) - lgamma(sum(x)) -(.B(x + alpha) - .B(alpha)) } .predict.DMN <- function(object, newdata, ..., logevidence=FALSE) { if (is.vector(newdata)) newdata <- matrix(newdata, nrow=1) lambda <- fitted(object) K <- ncol(lambda) alpha <- sapply(seq_len(K), function(k, lamda, x) { apply(x, 1, .neg_log_evidence_i, lambda[,k]) }, lambda, newdata) if (is.vector(alpha)) alpha <- matrix(alpha, nrow=1, dimnames=list(rownames(newdata), NULL)) if (!logevidence) { wt <- mixturewt(object)$pi offset <- apply(alpha, 1, min) z <- sweep(exp(-(alpha - offset)), 2, wt, "*") z / rowSums(z) } else { alpha } } setMethod(predict, "DMN", .predict.DMN) ## print / plot setMethod(show, "DMN", function(object) { cat("class:", class(object), "\n") cat("k:", ncol(mixture(object)), "\n") cat("samples x taxa:", nrow(mixture(object)), "x", nrow(fitted(object)), "\n") cat("Laplace:", laplace(object), "BIC:", BIC(object), "AIC:", AIC(object), "\n") }) heatmapdmn <- function(count, fit1, fitN, ntaxa=30, ..., transform=sqrt, lblwidth=.2 * nrow(count), col=.gradient) { p1 <- fitted(fit1, scale=TRUE) pN <- fitted(fitN, scale=TRUE) if (!setequal(rownames(p1), rownames(pN))) stop("taxa in 'fit1' and 'fitN' differ") p1 <- p1[rownames(pN),, drop=FALSE] diff <- rowSums(abs(pN - as.vector(p1))) taxa <- rev(head(order(diff, decreasing=TRUE), ntaxa)) pN <- pN[taxa,] cl <- mixture(fitN, assign=TRUE) ncl <- length(unique(cl)) nms <- names(cl) grp <- factor(cl, levels=as.character(seq(1, ncl))) idx <- split(nms, grp) ## 2 * ncl + 1 (for labels) panels mwd <- .15 * length(cl) / ncl # 'm's take up 15% of total width wd <- c(unlist(Map(c, lapply(idx, length), mwd), use.names=FALSE), lblwidth) layout(matrix(seq(1, 2 * ncl + 1), nrow=1), widths=wd) op <- par(no.readonly=TRUE) on.exit(par(op), add=TRUE) par(mar=c(1, 0, 1, 0)) for (i in seq_along(idx)) { image(transform(count[idx[[i]], taxa, drop=FALSE]), col=col, xaxt="n", yaxt="n") image(t(transform(pN[, i, drop=FALSE])), col=col, xaxt="n", yaxt="n") } xat <- (seq_len(nrow(pN)) - 1) / (nrow(pN) - 1) axis(4, xat, labels=rownames(pN), las=1) } DirichletMultinomial/R/dmngroup.R0000644000175000017500000001007314136047775016724 0ustar nileshnileshsetClass("DMNGroup", contains="SimpleList", prototype=prototype(elementType="DMN")) .DMNGroup <- function(...) { new("DMNGroup", listData=list(...)) } ## dmngroup dmngroup <- function(count, group, k, ..., simplify=TRUE, .lapply=parallel::mclapply) { if (length(group) != nrow(count)) stop("'length(group)' does not equal 'nrow(count)'") if (!is.factor(group)) group <- factor(group) lvls <- setNames(nm=levels(group)) counts <- lapply(lvls, csubset, count, group) tasks <- expand.grid(group=names(counts), k=k) tid <- seq_len(nrow(tasks)) ans0 <- .lapply(tid, function(i, tasks, counts, ...) { count <- counts[[tasks[i,"group"]]] k <- tasks[i,"k"] dmn(count, k, ...) }, tasks, counts, ...) ans <- if (simplify) { ans1 <- split(ans0, tasks[,"group"]) opt <- lapply(ans1, function(ans) { which.min(sapply(ans, laplace)) }) Map("[[", ans1, opt) } else ans0 do.call(.DMNGroup, ans) } ## predict .predict.DMNGroup <- function(object, newdata, ..., assign=FALSE) { if (2 < length(object)) stop("only 2 groups can be used for classification") res <- lapply(object, predict, newdata, ..., logevidence=TRUE) offset <- apply(do.call(cbind, res), 1, min) prClass <- local({ nClass <- sapply(object, function(x) nrow(mixture(x))) nClass / sum(nClass) }) pr <- simplify2array(Map(function(x, alpha, prClass, offset) { prMix <- sweep(exp(-(alpha - offset)), 2, mixturewt(x)$pi, "*") rowSums(prMix) * prClass }, object, res, prClass, MoreArgs=list(offset=offset))) if (!is.matrix(pr)) { dmnms <- list(rownames(newdata), names(prClass)) pr <- matrix(pr, nrow=1, dimnames=dmnms) } if (assign) names(object)[ifelse((pr[,1] / rowSums(pr)) > .5, 1, 2)] else pr / rowSums(pr) } setMethod(predict, "DMNGroup", .predict.DMNGroup) ## cross-validation .cv_dmngroup <- function(dropidx, count, k, z, ..., verbose=FALSE) ## e.g., k = c(Lean=1, Obese=3) --> 1 group for lean, 3 for obese { tryCatch({ trainz <- z[-dropidx] u <- unique(trainz) train <- count[-dropidx,,drop=FALSE] if (!is.factor(trainz)) trainz <- factor(trainz, levels=names(k)) if (any(is.na(trainz))) stop("values of 'z' not all in 'names(k)'") if (!all(names(k) %in% as.character(trainz))) stop("not all names(k) in z subset") trains <- sapply(levels(trainz), csubset, train, trainz) fits <- Map(dmn, trains, k[levels(trainz)], ..., verbose=verbose) fits <- do.call(.DMNGroup, fits) predict(fits, count[dropidx,,drop=FALSE], assign=FALSE) }, error=function(err) { message(".cv_dmngroup error: ", conditionMessage(err)) matrix(NA_integer_, nrow=length(dropidx), ncol=length(k), dimnames=list(rownames(count)[dropidx], names(k))) }) } cvdmngroup <- function(ncv, count, k, z, ..., verbose=FALSE, .lapply=parallel::mclapply) { n <- seq_len(nrow(count)) grp <- split(sample(length(n)), cut(n, ncv)) names(grp) <- seq_along(grp) cvresult <- .lapply(names(grp), function(idx, grp, ..., verbose) { if (verbose) cat("cross-validation group", names(grp[idx]), "\n") .cv_dmngroup(grp[[idx]], ..., verbose=verbose) }, grp, count, k, z, ..., verbose=verbose) gid <- rep(seq_along(cvresult), sapply(cvresult, nrow)) cbind(data.frame(group=gid, row.names=NULL), do.call(rbind, cvresult)) } ## summary / print / plot setMethod(summary, "DMNGroup", function(object, ...) { k <- data.frame(k=sapply(object, function(elt) ncol(mixture(elt)))) sxt <- t(sapply(object, function(elt) { c(samples=nrow(mixture(elt)), taxa=nrow(fitted(elt))) })) goodness <- t(sapply(object, goodnessOfFit)) cbind(k=k, sxt, goodness) }) setMethod(show, "DMNGroup", function(object) { cat("class:", class(object), "\n") cat("summary:\n") print(summary(object)) }) DirichletMultinomial/R/util.R0000644000175000017500000000132314136047775016044 0ustar nileshnilesh## utility .gradient <- # RColorBrewer::brewer.pal(9, "YlOrRd") c("#FFFFCC", "#FFEDA0", "#FED976", "#FEB24C", "#FD8D3C", "#FC4E2A", "#E31A1C", "#BD0026", "#800026") .divergent <- # RColorBrewer::brewer.pal(9, "RdYlBu") c("#D73027", "#F46D43", "#FDAE61", "#FEE090", "#FFFFBF", "#E0F3F8", "#ABD9E9", "#74ADD1", "#4575B4") .qualitative <- # RColorBrewer::brewer.pal(10, "Paired") c("#A6CEE3", "#1F78B4", "#B2DF8A", "#33A02C", "#FB9A99", "#E31A1C", "#FDBF6F", "#FF7F00", "#CAB2D6", "#6A3D9A") csubset <- function(val, x, pheno, cidx=TRUE) { ridx <- pheno %in% val if (!cidx) cidx <- colSums(x[ridx,]) != 0 x[ridx, cidx] } DirichletMultinomial/R/roc.R0000644000175000017500000000030214136047775015646 0ustar nileshnileshroc <- function(exp, obs, ...) { exp0 <- exp[order(obs, decreasing=TRUE)] data.frame(TruePostive=cumsum(exp0) / sum(exp0), FalsePositive=cumsum(!exp0) / sum(!exp0)) } DirichletMultinomial/inst/0000755000175000017500000000000014136070214015501 5ustar nileshnileshDirichletMultinomial/inst/extdata/0000755000175000017500000000000014136047775017153 5ustar nileshnileshDirichletMultinomial/inst/extdata/TwinStudy.t0000644000175000017500000000105414136047775021312 0ustar nileshnilesh0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 2 2 1 1 0 0 0 0 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 0 2 1 1 1 1 1 2 0 0 0 1 1 2 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 0 1 1 1 2 1 1 0 0 0 2 1 1 1 2 0 0 0 0 0 0 1 0 0 1 1 1 1 0 0 0 0 0 0 1 1 1 1 0 2 0 2 0 0 0 0 2 2 1 1 1 1 2 2 1 1 2 0 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 0 0 1 1 1 1 1 1 2 2 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 DirichletMultinomial/inst/extdata/Twins.csv0000644000175000017500000023527514136047775021012 0ustar nileshnileshTaxa,TS1.2,TS10.2,TS100.2,TS100,TS101.2,TS103.2,TS103,TS104,TS105.2,TS105,TS106.2,TS106,TS107.2,TS107,TS109.2,TS109,TS10,TS11.2,TS110.2,TS110,TS111.2,TS111,TS115.2,TS115,TS116.2,TS116,TS117.2,TS117,TS118.2,TS118,TS119.2,TS119,TS11,TS12.2,TS120.2,TS120,TS124.2,TS124,TS125.2,TS125,TS126.2,TS126,TS127.2,TS127,TS128.2,TS128,TS129.2,TS129,TS12,TS13.2,TS130.2,TS130,TS131.2,TS131,TS132.2,TS132,TS133.2,TS133,TS134.2,TS134,TS135.2,TS135,TS136.2,TS136,TS137.2,TS137,TS138.2,TS138,TS139.2,TS139,TS13,TS140.2,TS140,TS141.2,TS141,TS142.2,TS142,TS143.2,TS143,TS144.2,TS144,TS145.2,TS145,TS146.2,TS146,TS147.2,TS147,TS148,TS149,TS14,TS15.2,TS150,TS151.2,TS151,TS152.2,TS152,TS153,TS154.2,TS155.2,TS155,TS156.2,TS156,TS15,TS160.2,TS160,TS161,TS162.2,TS162,TS163.2,TS163,TS164.2,TS164,TS165.2,TS165,TS166.2,TS166,TS167.2,TS167,TS168.2,TS168,TS169.2,TS169,TS16,TS170.2,TS178.2,TS178,TS179.2,TS179,TS17,TS180,TS181.2,TS181,TS182.2,TS182,TS183.2,TS183,TS184,TS185.2,TS185,TS186,TS19.2,TS190,TS191,TS192,TS193.2,TS193,TS194.2,TS194,TS195.2,TS195,TS19,TS1,TS2.2,TS20.2,TS20,TS21.2,TS21,TS22,TS23,TS25.2,TS25,TS26.2,TS26,TS27.2,TS27,TS28,TS29,TS2,TS3.2,TS30.2,TS30,TS31.2,TS31,TS32.2,TS32,TS33.2,TS33,TS34,TS35,TS37.2,TS37,TS38.2,TS38,TS39.2,TS39,TS3,TS4.2,TS43,TS44,TS49.2,TS49,TS4,TS5.2,TS50.2,TS50,TS51.2,TS51,TS55.2,TS55,TS56.2,TS56,TS57.2,TS57,TS5,TS6.2,TS61.2,TS61,TS62.2,TS62,TS63.2,TS63,TS64.2,TS64,TS65.2,TS65,TS66.2,TS66,TS67.2,TS67,TS68.2,TS68,TS69.2,TS69,TS6,TS7.2,TS70.2,TS70,TS71.2,TS71,TS72,TS73,TS74.2,TS74,TS75.2,TS75,TS76.2,TS76,TS77,TS78.2,TS78,TS7,TS8.2,TS82.2,TS82,TS83.2,TS83,TS84.2,TS84,TS85.2,TS85,TS86.2,TS86,TS87.2,TS87,TS88.2,TS88,TS89.2,TS89,TS8,TS9.2,TS90.2,TS90,TS91.2,TS91,TS92.2,TS92,TS94.2,TS94,TS95.2,TS95,TS96.2,TS96,TS97.2,TS97,TS98.2,TS98,TS99.2,TS9 Acetanaerobacterium,0,0,0,1,0,0,0,0,1,1,0,0,0,0,1,1,0,0,4,0,0,2,0,3,1,4,0,0,0,0,0,2,0,0,3,0,1,6,0,1,2,4,1,2,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,1,1,0,2,0,1,3,0,0,0,0,6,1,0,0,0,1,1,0,0,1,1,6,1,5,4,1,0,2,0,2,0,2,2,0,4,4,1,0,2,0,2,1,8,0,0,2,0,0,0,0,3,0,0,6,0,1,0,0,0,0,0,1,3,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,2,1,0,4,0,0,3,1,3,0,0,0,1,0,0,6,2,5,2,0,7,1,1,4,0,1,1,12,8,0,1,1,8,0,3,0,0,1,0,0,2,0,2,2,0,1,0,0,0,1,0,0,11,0,0,0,0,0,0,4,1,1,0,0,0,0,0,1,3,0,1,1,6,0,0,3,0,0,0,1,1,1,2,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,7,1,0,1,0,0,0,0,1,1,0,2,0,0,0,0,0,0,1,0 Acetivibrio,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,3,0,4,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,0,1,0,0,2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,2,0,4,1,0,7,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,5,0,0,0,0,0,2,0,0,1,0,0,4,37,2,2,4,0,0,0,1,4,0,0,0,0,0,0,3,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,1,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,9,0,0,0,0,0,2,0,0,0,0,0,1,0,4,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0 Acetobacterium,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 Acidaminococcus,0,0,1,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,2,8,53,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,14,0,0,0,0,4,16,1,0,0,0,0,8,2,0,0,21,30,24,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,2,1,0,0,0,0,0,13,0,0,0,0,0,0,16,5,0,0,0,0,0,0,0,0,0,57,0,0,0,0,11,19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,48,0,0,0,0,19,9,0,0,0,0,0,0,23,0,0,0,0,0,0,0,56,20,0,0,0,0,0,36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,2,23,0,0,0,1,1,2,7,16,0,0,0,0,0,1,0,0,0,0,0,0,0,0,31,15,10,6,0,11,14,0 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Akkermansia,1,0,1,1,1,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,12,12,0,0,0,20,0,0,7,4,1,0,0,0,0,0,0,0,0,0,0,0,1,1,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,24,15,8,0,0,0,4,0,1,0,28,8,0,13,1,3,1,0,0,0,0,17,0,0,0,0,0,0,0,0,0,1,9,0,0,1,3,1,4,4,1,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,2,0,5,4,4,18,7,12,0,0,8,0,4,6,22,1,0,0,80,0,0,0,4,20,1,0,1,39,0,0,9,1,18,0,6,8,0,0,6,0,10,0,0,1,0,0,0,108,0,0,0,0,0,0,3,0,0,0,0,0,1,5,3,0,9,0,1,48,0,0,0,0,0,14,6,0,0,0,0,0,0,10,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,6,0,0,6,0,0,0,0,2,3,4,10,0,1,0,0,0,0,0,6 Alistipes,41,0,3,23,0,30,1,51,9,2,2,4,11,26,17,21,0,16,268,41,39,65,0,7,6,14,7,36,14,13,9,2,21,0,1,0,73,29,2,19,24,12,25,180,16,46,35,32,0,48,0,0,3,4,31,58,7,27,63,17,52,43,40,22,11,26,5,2,16,10,78,1,5,25,13,150,15,44,18,70,16,312,53,205,264,201,70,51,33,28,20,3,156,112,89,88,33,7,5,5,29,5,155,4,6,34,2,19,74,49,22,11,378,330,5,41,27,50,105,43,23,24,61,2,0,0,4,2,3,9,6,10,14,76,1,45,4,23,17,1,2,25,4,426,19,3,18,28,68,71,21,120,99,58,284,0,43,18,0,151,147,212,277,30,49,33,6,303,53,157,83,133,512,36,93,4,73,4,11,114,22,74,9,56,104,67,30,20,7,15,0,61,0,0,0,39,290,0,0,0,16,0,0,3,16,5,1,15,23,23,119,356,413,156,46,186,283,9,9,9,21,13,161,36,6,19,26,22,24,20,81,35,29,1,1,11,18,2,3,4,119,9,23,0,3,1,2,0,21,0,12,11,4,8,13,13,44,14,421,45,83,43,2,0,0,0,45,26,38,85,56,15,3,1,1,15,14,137 Allisonella,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,3,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,1,0,2,3,0,0,1,1,2,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,4,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,6,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,6,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0 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Anaerobiospirillum,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 Anaerofilum,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,1,0 Anaerofustis,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 Anaerosporobacter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 Anaerostipes,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,1,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,2,0,3,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,3,0,3,1,1,0,0,0,0,0,0,0,1,2,0,1,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,4,1,1,2,0,0,9,0,0,0,0,1,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 Anaerotruncus,37,9,0,0,9,1,0,1,0,0,2,0,0,0,9,0,27,10,8,10,14,9,24,1,0,63,0,0,36,11,2,1,2,0,34,2,1,2,3,5,3,3,1,305,3,6,1,1,0,0,2,1,0,3,43,0,0,6,0,0,1,4,3,4,1,4,4,18,3,18,3,5,0,0,1,2,1,2,52,76,21,14,28,120,105,49,0,44,56,3,1,2,7,2,7,8,2,5,77,5,3,2,11,0,2,0,0,3,2,2,3,4,35,13,3,1,0,0,8,3,3,24,10,8,4,3,0,2,3,2,18,44,0,2,0,47,2,4,2,2,19,0,1,1,0,1,0,0,15,14,24,103,8,13,19,7,10,0,1,2,24,4,14,4,22,10,7,53,2,2,2,3,212,1,4,1,36,2,10,11,5,5,5,0,4,7,2,3,5,0,2,0,0,0,0,0,1013,0,0,7,0,1,0,0,1,3,0,3,0,3,1,9,4,10,3,4,1,1,0,9,0,0,12,0,0,0,4,2,0,1,14,14,3,2,0,10,30,0,0,0,4,0,0,0,0,0,6,1,1,2,1,5,5,40,1,2,7,0,16,0,4,24,10,2,0,0,3,1,0,111,9,0,0,0,0,8,4,1 Arthrobacter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 Asaccharobacter,0,0,0,0,0,0,0,1,0,0,1,1,1,1,0,0,0,0,2,1,1,5,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,5,3,0,0,0,0,0,0,1,4,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,19,8,0,0,2,0,35,7,1,3,1,0,5,0,0,0,0,2,0,1,1,0,0,0,0,0,4,0,1,0,0,5,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,3,0,0,0,0,3,0,1,0,2,0,2,2,0,0,2,0,1,0,0,2,1,0,2,0,0,1,0,0,0,1,5,0,0,0,0,0,0,0,3,2,0,0,0,0,1,0,0,0,1,1,0,0,0,0,0,0,1,1,0,0,1,0,1,2,0,0,0,0 Asticcacaulis,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 Atopobium,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 Bacteroides,194,227,56,555,34,552,124,415,792,60,453,275,112,292,375,709,434,613,367,99,338,342,7,32,21,224,493,359,185,754,509,517,707,254,130,3,759,507,20,726,192,33,204,356,308,1121,764,526,524,81,1110,900,606,632,913,540,895,336,314,128,655,609,154,45,86,476,280,137,338,20,1065,54,97,347,327,936,137,525,459,461,168,651,540,490,342,573,549,719,1775,97,24,29,450,387,264,479,566,219,277,279,171,352,340,29,42,256,1133,1427,513,307,584,628,403,174,22,723,59,87,435,198,193,244,1206,616,16,28,31,8,237,826,656,703,136,1482,692,1087,573,515,355,464,0,327,745,640,137,203,230,494,456,505,1075,886,268,217,620,301,1681,75,95,455,1708,547,4973,42,1103,427,44,1316,123,288,609,1562,2649,241,759,0,369,16,21,581,993,39,29,259,1529,844,772,424,42,281,256,2379,47,441,25,382,242,1194,464,0,1000,1,923,539,121,886,81,173,842,80,329,562,555,301,188,1227,372,366,122,259,698,461,808,835,47,218,253,448,532,285,945,42,170,278,234,332,857,0,539,95,864,30,59,28,565,232,12,15,563,306,304,484,27,89,641,919,610,361,1858,470,478,576,121,8,110,154,582,481,672,694,358,219,436,550,861,835,1404,2546 Barnesiella,21,0,0,5,0,5,1,0,0,0,0,0,0,0,9,6,0,0,13,15,5,26,0,0,0,0,0,0,1,0,0,0,0,0,2,1,1,1,0,1,61,5,30,60,4,8,0,3,0,23,0,0,0,0,0,0,0,0,0,0,0,0,9,10,1,6,0,0,4,3,58,5,0,3,0,0,0,2,6,0,0,1,0,44,47,0,0,9,9,1,0,0,0,0,0,0,32,7,9,3,0,0,9,0,2,0,0,0,14,9,0,0,15,23,0,0,0,0,0,0,19,3,0,0,0,0,0,0,0,0,0,0,3,17,0,27,0,12,17,0,0,7,0,0,2,0,0,0,15,10,17,71,0,18,53,0,0,0,0,0,0,0,0,12,26,0,0,1,2,21,17,0,0,22,46,4,36,0,0,5,1,19,1,42,52,1,17,13,2,0,0,37,0,0,0,20,79,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,59,13,39,23,3,2,5,3,4,0,2,0,8,13,1,0,0,0,4,4,0,0,4,1,0,0,0,2,10,0,0,0,0,1,1,0,0,3,10,0,4,4,6,12,8,72,0,23,43,0,0,0,0,0,0,0,0,13,3,0,0,0,0,0,0 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Bulleidia,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 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Butyrivibrio,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,1 Campylobacter,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 Catenibacterium,0,0,139,126,0,0,0,0,0,0,0,0,184,6,0,0,0,0,0,0,0,0,52,59,71,119,0,0,0,0,0,0,0,1,0,0,0,0,0,2,22,27,0,0,3,0,0,0,0,0,0,3,0,0,0,0,100,61,0,2,0,0,0,1,0,0,0,0,126,104,0,5,5,17,5,22,31,6,0,21,14,0,0,29,41,2,0,0,0,0,0,0,30,122,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,61,72,0,0,0,0,0,0,135,172,0,0,60,232,28,46,0,0,99,87,139,96,316,84,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,492,457,55,615,91,15,0,0,0,1,0,3,0,0,0,0,660,768,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,47,8,0,0,0,0,0,0,164,28,0,0,0,0,110,264,12,30,0,0,0,0,0,0,181,9,0,0,10,2,25,87,0,0,0,0,0,0,0,0 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Cloacibacillus,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,5,1,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,3,0,1,1,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 Clostridium,1,0,1,3,1,0,1,0,0,0,0,3,1,5,0,1,1,0,0,3,0,0,2,2,0,1,14,11,1,1,0,0,1,0,1,0,0,0,1,2,0,1,4,1,7,3,1,1,2,0,2,2,1,9,1,0,0,6,0,0,0,0,8,4,4,2,1,3,6,0,3,1,0,1,0,1,1,0,2,1,0,2,0,1,6,1,0,1,0,2,1,1,0,4,5,0,0,0,15,16,2,1,59,1,1,0,0,0,5,0,9,3,7,126,1,0,82,26,0,4,1,1,0,12,5,9,1,3,1,0,0,17,28,3,0,0,0,0,0,5,1,1,2,0,0,0,0,0,0,0,22,0,6,0,1,6,15,0,171,24,19,7,54,0,3,0,2,0,0,4,3,4,1,6,0,0,0,0,7,1,6,2,2,5,6,0,8,0,0,2,3,2,21,0,10,0,0,0,0,5,7,1,4,67,0,0,0,0,1,1,3,0,5,34,6,1,1,0,1,1,10,1,14,4,8,1,3,1,2,0,0,3,1,0,0,11,2,6,0,0,127,0,0,1,4,0,0,2,1,3,1,1,0,1,1,0,1,0,20,0,0,0,1,0,0,0,3,10,1,0,8,0,3,4,11,2,1,16 Collinsella,0,7,10,2,0,3,1,0,0,0,23,5,105,37,0,1,25,6,0,2,0,5,33,34,8,51,19,35,53,4,32,32,9,6,22,4,12,5,82,34,17,11,4,8,25,63,0,10,19,9,15,0,0,0,51,8,25,20,8,8,0,0,14,4,6,6,5,15,3,34,2,1,0,2,0,10,55,6,41,13,5,0,0,0,0,9,8,1,20,106,8,71,0,0,0,0,11,35,8,12,10,1,4,1,2,1,0,0,0,0,0,0,4,13,25,4,139,14,13,8,33,10,0,9,21,106,12,44,0,1,3,8,18,9,18,3,35,0,0,1,104,8,2,2,0,2,1,0,0,0,27,0,9,12,39,91,30,258,0,23,3,89,9,41,68,0,4,188,1,24,0,2,47,73,20,169,16,19,49,151,16,58,43,50,5,4,0,61,155,135,54,0,54,246,169,11,335,0,0,0,0,0,0,215,55,13,5,0,2,0,0,1,7,16,3,11,0,0,4,15,16,4,40,14,185,1,2,2,5,1,7,20,10,0,0,20,5,141,8,17,29,0,8,1,28,17,2,2,0,0,0,37,5,14,9,6,3,9,0,15,4,3,31,5,59,8,24,15,0,0,23,4,15,12,0,0,2,4 Coprobacillus,7,32,2,3,0,0,11,0,0,0,1,21,5,0,9,25,113,2,112,70,5,31,0,0,0,0,52,75,8,6,38,25,0,0,15,1,7,3,5,1,1,0,0,1,0,5,9,59,0,104,4,0,0,1,31,14,4,3,9,1,19,29,1,0,18,11,1,13,0,1,70,0,5,0,4,0,0,0,0,1,1,23,53,1,1,3,1,9,41,653,75,0,0,0,0,0,4,0,0,0,0,0,1,0,0,0,0,1,0,2,0,0,0,4,33,2,27,10,26,16,0,0,0,10,0,2,0,0,0,1,0,0,1,3,0,0,15,0,36,0,263,13,0,41,46,50,0,9,25,73,247,53,3,55,239,1102,862,2,24,23,124,113,62,0,10,3,0,0,86,1,0,25,271,14,0,0,2,26,6,82,20,157,50,60,29,265,7,87,0,0,11,3,551,471,243,3,82,2,0,4,57,0,0,723,0,6,1,3,1,1,46,7,12,14,20,65,13,2,24,7,27,16,161,92,7,0,2,7,23,8,2,9,2,0,1,2,8,0,1,3,36,7,7,11,12,3,0,0,15,60,0,4,0,0,3,1,0,5,53,10,0,1,5,2,26,15,0,1,0,0,0,1,41,15,8,0,0,33 Coprococcus,3,2,42,32,2,11,20,17,7,9,17,19,5,2,7,10,5,8,13,16,22,24,22,41,131,63,5,0,6,10,37,18,13,6,41,1,20,6,0,0,65,30,26,66,9,17,6,5,14,54,6,3,0,0,14,4,28,2,27,8,9,9,54,46,7,20,1,16,47,26,171,8,47,42,22,6,6,14,53,18,10,29,29,10,14,44,13,41,24,97,22,47,18,34,36,16,29,20,29,14,2,15,55,23,30,1,5,2,29,0,46,36,6,21,4,19,33,11,90,124,88,67,15,2,11,1,12,5,0,8,1,29,28,7,9,3,33,46,15,7,36,90,30,126,33,13,10,5,0,2,87,15,8,110,218,2,25,13,38,79,148,54,170,43,29,2,1,26,10,4,4,11,83,10,7,8,230,29,163,33,6,85,12,16,24,21,46,16,7,6,486,71,9,3,125,5,197,13,37,0,25,9,12,103,5,45,4,19,7,32,54,38,62,17,19,41,17,34,0,18,35,22,11,15,60,49,28,10,27,13,0,29,17,63,47,48,20,24,38,5,96,10,12,10,18,4,24,10,14,17,2,11,16,36,4,10,34,11,128,115,16,87,26,9,26,15,21,17,4,6,25,17,6,2,13,0,0,298 Coraliomargarita,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0 Coriobacterium,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 Corynebacterium,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,2,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 Desulfocurvus,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 Desulfovibrio,3,0,0,0,0,0,0,0,0,0,0,0,0,0,10,4,0,0,0,0,0,1,0,0,6,12,0,0,0,2,0,0,2,0,2,0,0,1,1,2,1,0,0,1,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,7,1,14,0,5,1,1,2,0,0,0,1,1,0,0,0,0,0,0,7,3,1,1,0,0,0,0,0,0,2,0,0,1,0,1,1,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,4,0,0,3,0,8,7,0,0,1,0,8,0,2,0,0,0,0,2,1,0,25,33,22,0,3,2,6,0,1,0,0,4,5,0,1,0,0,0,0,4,0,0,0,0,5,0,0,0,3,0,0,0,0,1,0,0,0,1,0,0,0,4,0,10,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,5,0,0,6,1,1,7,0,0,0,0,0,0,0,3,1,0,31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,0,0,0 Dialister,9,8,0,0,0,0,0,0,10,7,1,0,63,31,0,0,6,20,0,0,0,0,19,27,29,22,0,0,0,0,11,19,3,19,0,0,0,0,0,0,0,14,8,12,2,0,0,0,2,0,9,0,27,30,20,0,16,5,14,2,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,4,6,16,23,0,0,0,0,0,1,0,0,32,0,77,26,1,16,17,0,0,13,0,0,0,10,13,261,0,0,20,35,9,8,5,0,0,2,10,0,0,3,12,0,0,0,0,0,6,0,0,32,13,0,0,13,17,0,0,0,0,5,0,7,0,0,0,10,12,0,0,0,0,0,0,0,20,2,89,273,14,60,0,0,0,0,1,0,0,0,4,0,16,0,0,0,0,0,0,0,0,277,0,0,0,0,16,24,0,0,0,17,0,23,0,1,58,0,27,177,37,59,0,0,5,47,0,13,0,0,0,0,0,0,0,0,10,15,3,5,62,0,0,0,9,9,0,1,0,0,0,0,11,19,0,0,0,0,0,0,4,19,12,0,0,0,1,0,0,0,0,0,0,16,49,0,1,0,0,0,0,17,0,103,7,19,10,0,0,19,7,23,12,20,23,0,0,4,2,16,0,0,19 Dickeya,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 Dorea,3,17,87,86,81,42,39,23,0,0,18,41,118,58,19,8,46,15,9,15,24,31,13,20,12,20,105,55,41,26,17,26,0,31,37,1,15,68,69,5,6,5,5,46,15,16,3,7,73,24,17,2,92,134,53,24,45,32,1,10,8,15,15,25,43,42,10,94,10,108,43,2,31,16,1,26,90,12,67,30,16,7,0,23,43,144,44,26,63,598,22,229,18,12,2,1,31,11,10,25,20,5,72,8,8,9,77,89,26,23,17,7,6,27,48,16,110,31,5,7,23,19,23,15,46,122,26,71,2,6,2,4,27,103,33,15,15,5,22,11,261,0,18,40,16,4,11,2,20,34,143,5,1,46,159,51,97,76,7,10,49,63,65,6,25,46,11,11,13,0,3,24,106,80,65,62,463,48,42,59,39,24,48,29,28,22,25,55,18,134,111,3,108,40,361,21,365,103,89,0,45,4,40,347,40,28,10,51,11,25,17,23,17,7,29,43,5,42,1,23,20,14,2,54,55,7,11,12,79,4,28,24,74,0,0,64,14,5,31,15,90,1,12,18,51,15,43,0,10,45,20,20,39,28,5,22,26,27,107,9,25,25,69,22,96,21,10,0,6,7,7,2,69,21,15,35,1,22 Dysgonomonas,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 Eggerthella,0,2,1,1,3,0,0,1,0,1,1,0,0,2,0,0,0,0,1,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,1,1,0,0,0,5,0,0,0,0,0,1,0,0,5,5,1,2,0,0,1,0,1,3,1,2,0,0,0,0,0,0,2,0,0,0,0,0,0,1,0,0,0,4,1,0,0,2,2,0,0,70,0,0,2,4,3,4,0,0,2,4,0,0,0,0,0,0,1,6,1,1,5,1,3,5,33,0,2,0,1,0,0,0,4,1,0,2,1,0,0,0,0,1,0,0,0,0,0,0,2,2,0,0,0,0,1,0,0,0,0,0,2,0,0,2,1,0,1,21,26,7,0,20,5,2,0,1,0,2,0,4,1,1,3,0,0,0,0,2,0,3,0,0,0,0,0,1,1,2,3,2,3,2,7,5,1,0,16,5,6,0,0,0,1,16,1,0,0,14,2,0,5,0,1,0,1,2,0,1,0,0,0,0,3,1,0,0,0,0,0,0,1,1,2,4,2,0,0,2,0,0,0,1,2,0,0,0,12,1,0,1,0,0,2,0,1,2,0,0,0,1,2,0,0,0,3,0,0,2,0,1,0,1,1,0,0,0,1,0 Enterobacter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 Enterococcus,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,31,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,37,0,0,0,0,0,0,0,0,0,0,0,0,0,32,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0 Enterorhabdus,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 Escherichia/Shigella,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,14,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,3,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0 Ethanoligenens,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,17,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 Eubacterium,2,0,63,110,148,0,0,0,2,0,0,0,124,51,4,0,1,3,6,0,0,0,8,13,29,50,1,6,120,54,1,2,0,0,107,1,8,28,1,0,18,7,0,0,1,3,0,0,2,0,2,8,0,4,0,1,0,0,4,0,2,1,21,23,88,23,0,0,104,85,0,6,50,42,35,51,105,1,1,9,13,0,0,15,23,0,0,1,1,0,0,0,1,0,9,7,0,2,23,6,19,31,0,2,3,0,0,9,2,14,149,114,0,4,113,27,0,0,0,0,87,107,0,124,69,110,35,124,1,0,4,0,1,1,0,2,0,0,1,36,0,0,44,0,0,1,0,0,4,1,10,2,0,0,12,1,9,10,7,0,0,0,0,45,115,6,1,2,0,0,0,28,123,58,18,255,492,1,114,40,49,40,34,3,0,0,9,0,27,234,543,4,1,0,2,4,3,7,3,12,1,0,1,8,0,5,2,6,0,1,2,19,41,0,1,0,1,0,0,28,31,0,0,1,327,0,0,2,0,0,30,34,30,3,5,347,0,9,75,17,596,8,0,0,0,0,34,11,5,4,10,23,17,30,0,4,37,23,119,0,0,0,61,8,1,0,27,12,20,77,2,0,1,0,0,3,4,0 Faecalibacterium,83,93,89,126,34,261,33,131,138,18,149,115,327,76,145,143,361,248,484,40,188,123,115,238,137,136,247,332,123,295,153,13,281,295,178,4,124,29,21,32,88,77,157,37,338,500,258,119,132,90,203,200,12,39,83,84,315,229,301,120,64,167,215,138,37,391,72,119,146,251,648,325,686,322,488,165,191,348,229,124,185,67,116,92,204,248,145,392,989,843,349,0,158,235,105,97,328,23,20,43,284,152,901,176,218,405,325,110,534,187,335,190,59,7,17,159,22,128,115,94,169,292,443,42,128,72,228,131,44,196,230,85,527,504,8,103,30,389,488,324,2,272,129,58,202,97,264,149,83,171,133,460,120,404,1546,54,522,109,27,139,133,179,312,35,35,930,113,394,168,231,575,239,1626,322,840,46,768,119,361,387,94,247,33,147,454,511,90,48,46,3,51,176,62,59,61,71,39,165,113,0,429,0,30,403,252,116,7,96,78,87,60,220,263,180,119,467,68,208,66,372,364,228,140,828,369,343,137,318,93,71,188,81,280,0,0,369,439,387,260,172,821,69,484,255,410,297,55,271,190,65,333,586,91,70,179,18,657,155,1995,207,93,139,100,51,390,455,70,143,274,181,292,202,110,264,252,573,122,713 Fusobacterium,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0 Gemella,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 Geosporobacter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 Gordonibacter,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,1,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,2,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,3,2,0,4,1,1,1,1,0,0,0,6,0,0,0,0,0,1,0,1,2,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0 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Luteococcus,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 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Megamonas,0,0,0,0,0,0,0,0,0,0,0,0,123,296,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,325,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,322,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,152,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,58,353,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0 Megasphaera,0,0,11,10,0,0,0,0,0,0,0,0,56,44,0,0,0,0,0,0,0,0,5,4,28,35,0,0,8,5,42,204,0,0,26,1,0,0,0,4,18,10,0,0,0,0,0,0,0,0,0,0,0,0,39,0,28,39,0,0,0,5,0,0,0,0,4,25,0,0,0,0,0,0,0,0,0,27,51,92,26,0,0,0,0,0,0,72,0,0,2,1,0,0,0,0,49,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,25,0,0,0,0,0,17,37,89,20,6,0,0,12,23,30,21,81,21,0,0,0,20,124,0,1,0,0,0,0,0,0,0,158,0,0,42,1,19,117,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,11,4,18,0,0,21,14,0,0,0,0,1,0,206,98,0,0,29,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,89,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28,2,17,3,0,28,19,0,0,0,0,0,0,6,29,0,0,3,55,0,0,0,0,6,12,0,0,4,2,0,0,18,22,47,36,0,0,11,22,0,0,0,28,54,0 Methylobacterium,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 Microbacterium,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 Mitsuokella,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,21,21,16,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,42,2,29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,17,15,0,0,0,0,0,0 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Moryella,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 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Olsenella,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,6,13,98,0,0,0,0,0,0,0,0,20,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,63,157,13,99,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,99,1177,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,17,0,0,0,0,0,0,2,1,0,0,0,0,1,6,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,7,2,0,0,0,0,0,0 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Oscillibacter,113,0,3,12,1,29,2,37,6,0,2,1,3,2,11,12,3,0,151,11,45,68,51,57,59,487,0,5,8,11,7,5,8,0,53,2,58,42,2,35,200,11,44,134,17,53,46,38,0,31,11,2,4,1,127,49,79,26,45,14,47,33,59,24,27,35,1,8,16,5,122,37,49,35,6,77,2,18,36,68,21,431,112,287,133,84,15,53,73,18,7,0,131,38,136,135,10,70,108,191,66,39,171,13,23,10,3,19,4,5,154,68,126,71,2,32,9,17,116,54,70,85,241,8,5,0,51,30,21,17,18,17,24,78,2,13,2,25,10,3,0,19,3,40,5,5,13,23,35,28,3,242,67,21,15,10,15,2,1,298,357,58,255,113,434,70,2,819,10,29,22,133,465,30,21,6,154,3,24,8,6,48,1,13,34,33,33,37,10,6,6,29,2,7,1,16,267,10,0,0,7,1,8,18,3,71,3,14,8,18,22,69,61,96,26,124,274,9,13,23,19,32,23,36,5,33,39,30,18,13,13,46,10,3,2,32,33,0,5,4,133,19,29,4,7,4,14,5,9,1,24,68,66,143,41,5,65,37,426,30,39,109,8,0,3,0,101,33,49,93,122,39,0,0,2,5,16,127 Oxalobacter,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 Papillibacter,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,19,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,40,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 Parabacteroides,5,1,1,0,0,2,2,0,0,0,1,2,1,5,1,16,1,2,91,1,51,39,0,0,1,2,0,0,0,0,4,5,0,0,1,0,1,0,0,1,1,0,0,4,0,9,0,2,0,1,0,0,4,1,2,7,0,1,0,0,2,4,2,3,0,0,0,0,1,0,2,0,0,1,0,1,0,3,2,1,0,14,3,0,0,9,4,46,3,0,0,1,3,0,3,4,1,5,3,2,0,1,8,2,1,8,3,4,0,0,1,0,1,1,0,1,0,0,0,0,1,0,44,1,0,1,0,0,1,0,0,0,2,4,1,1,0,8,0,0,0,1,1,0,0,4,1,4,1,14,8,7,7,9,18,1,10,10,2,9,44,2,7,1,35,9,0,32,1,11,10,6,10,2,8,1,8,1,2,9,4,4,0,8,10,5,7,5,19,3,0,44,0,0,0,3,13,7,1,1,0,0,8,2,2,3,0,2,9,0,0,13,8,6,7,68,3,0,2,1,12,2,119,6,0,0,1,1,1,0,1,0,0,0,0,0,0,0,0,0,13,9,2,1,2,1,0,1,1,0,0,1,2,4,2,1,1,0,57,1,1,2,0,0,1,1,5,2,0,0,1,1,0,1,0,0,0,3 Paraeggerthella,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 Paraprevotella,0,0,0,9,0,0,0,0,0,0,0,0,0,0,0,1,0,0,23,2,0,0,0,2,13,4,0,0,0,0,0,0,0,8,2,1,0,0,0,0,2,0,0,0,0,0,0,0,14,0,0,0,0,0,0,0,0,0,0,0,0,0,6,4,2,20,0,0,4,1,0,5,5,9,7,0,0,2,0,3,2,0,0,0,0,0,0,14,242,0,0,3,0,0,0,0,0,0,3,5,0,0,0,2,5,0,0,0,0,0,0,0,6,5,2,6,0,0,0,0,0,0,0,0,1,0,0,0,0,16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,23,0,0,15,42,0,0,0,3,0,0,2,3,0,0,0,0,0,4,0,0,0,0,0,0,5,0,165,7,0,0,0,0,0,8,0,0,1,0,0,0,13,8,0,0,11,0,0,0,0,0,1,0,33,0,0,0,0,0,0,0,0,4,0,0,23,0,0,47,2,4,0,0,0,0,0,18,46,0,0,2,30,3,2,0,0,6,0,0,0,0,0,6,4,0,0,16,0,0,0,0,0,6,7,0,0,0,0 Parasutterella,1,0,0,0,0,0,3,0,0,0,0,0,0,0,12,2,0,0,0,0,8,3,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,8,0,1,3,5,2,15,1,0,0,0,0,0,0,0,10,5,17,13,0,0,11,20,0,0,0,1,0,0,0,0,0,0,0,0,0,11,0,0,2,1,0,0,0,2,4,19,16,0,0,0,0,2,2,12,1,5,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,19,1,2,7,3,0,0,11,5,0,0,0,0,0,0,9,7,0,0,1,0,11,7,6,1,0,0,2,0,0,0,2,7,0,0,0,1,1,0,0,0,0,7,0,0,0,0,0,0,0,4,3,2,0,0,0,11,27,0,0,0,0,0,6,26,8,0,0,3,3,1,0,16,0,0,0,0,0,15,1,3,0,0,0,224,40,0,0,0,0,11,1,0,1,0,0,0,0,3,7,2,0,7,4,0,1,12,0,0,0,0,0,3,1,0,0,0,2,13,13,0,0,0,0,0,0,0,1,1,0,0,0,0,8,0,2,10,0,0,8,4,0,0,0,5,0,1,0,0,0,0,0,0,17,6,6,2,0,0,4,3,9,8 Pediococcus,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 Pelomonas,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 Peptococcus,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 Peptostreptococcus,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 Phascolarctobacterium,0,0,0,0,2,9,6,10,0,0,0,0,0,0,22,31,0,0,0,0,21,45,0,1,0,0,0,0,0,0,0,0,0,0,0,0,36,35,1,18,3,8,2,0,9,36,17,19,0,5,0,0,0,0,1,23,0,0,0,0,38,43,0,0,0,0,6,21,0,0,35,0,0,0,0,0,0,0,0,0,0,39,40,10,21,46,14,1,0,1,0,10,0,0,18,14,0,0,0,0,0,0,0,0,0,0,0,0,0,0,21,5,6,5,0,36,0,0,0,0,0,0,0,0,0,0,0,0,2,28,0,0,0,0,12,36,11,18,2,0,4,0,0,0,10,1,9,19,76,15,63,0,0,0,0,0,0,18,7,14,96,17,100,0,0,17,1,1,3,11,36,0,0,0,0,0,0,0,0,50,20,6,0,0,0,38,0,0,0,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,39,0,10,12,3,5,0,8,3,4,1,0,15,8,0,0,16,0,0,1,4,16,1,9,15,47,0,0,10,5,0,0,0,0,0,132,0,0,0,25,4,0,0,0,0,0,0,0,0,9,12,12,15,0,0,12,4,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0 Porphyromonas,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 Prevotella,0,0,0,0,1,0,0,0,0,0,0,0,11,72,5,15,0,0,0,0,6,24,60,107,46,17,0,0,4,0,0,0,0,0,114,4,0,1,0,0,0,1,0,0,1,0,0,1,0,3,0,2,0,0,0,0,0,0,0,0,0,0,202,32,2,147,0,0,0,0,0,243,49,0,0,2,0,41,1,146,168,0,0,0,0,0,0,1,0,5,708,0,0,1,0,0,0,13,31,38,11,2,1612,229,189,0,0,1,0,0,1,1,0,0,0,1,0,1,0,5,19,46,12,2,139,1,203,2,0,0,1,0,634,0,0,0,0,0,0,0,0,0,0,0,13,40,0,0,0,0,0,0,0,842,1632,0,2,0,0,0,1,1,2,0,1,0,0,0,1,0,1,0,2,443,994,1,71,10,417,0,0,10,68,7,0,0,0,0,40,164,17,0,0,0,0,356,206,1,1,4,0,0,0,2,653,0,0,5,0,0,0,0,0,1,0,1,0,0,0,37,131,1,0,756,18,0,0,0,0,0,0,19,17,0,0,0,0,8,0,55,668,0,33,109,37,11,59,106,76,15,51,119,24,30,0,0,0,0,1,0,0,0,232,6,1,0,0,0,0,0,7,22,0,0,0,0,0,0 Propionibacterium,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 Proteus,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 Pseudobutyrivibrio,6,5,0,0,7,0,1,0,0,0,1,0,0,0,0,0,0,39,0,0,0,0,0,2,0,0,1,25,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,121,0,0,0,0,4,1,0,0,2,2,7,2,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,3,0,0,0,0,1,2,0,0,0,1,0,4,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,2,0,0,0,0,0,34,18,0,0,1,0,0,13,1,0,349,0,0,0,0,0,2,4,0,0,0,0,0,0,1,0,1,0,1,6,1,0,0,2,24,0,1,0,0,77,0,0,0,1,0,0,14,1,286,0,0,0,0,0,61,0,0,8,0,0,0,3,0,0,0,0,72,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,20,2,0,0,0,0,1,0,0,0,6,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,138,0,0,0,69 Pyramidobacter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 Ralstonia,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 Rhizobium,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 Robinsoniella,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,1,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0 Roseburia,8,42,19,58,19,17,87,19,50,29,22,78,50,34,51,35,82,100,50,10,87,118,16,13,27,14,83,76,25,26,68,39,105,30,21,0,20,48,9,33,9,9,73,34,158,95,49,21,62,15,56,62,16,96,63,30,33,42,90,28,40,159,119,66,43,27,13,40,141,41,55,13,6,123,58,6,13,35,108,25,38,8,30,7,9,38,187,57,374,132,105,133,4,12,2,1,5,20,23,13,35,32,141,5,8,59,55,38,52,75,54,51,5,2,50,114,26,20,24,72,20,10,51,42,52,8,19,2,17,31,13,38,101,133,29,33,223,121,66,28,84,21,33,33,45,53,5,1,75,11,602,28,15,120,482,154,245,146,49,19,11,17,12,11,79,29,30,116,25,33,48,24,83,55,37,15,117,36,82,212,107,34,38,45,187,63,45,97,18,60,257,29,118,14,13,35,164,152,51,111,134,124,202,232,32,19,9,148,42,43,71,60,70,23,71,133,25,65,12,88,154,72,71,133,42,29,12,34,16,16,71,53,50,419,234,101,129,223,53,20,300,10,36,22,81,60,40,22,98,54,19,79,21,82,65,83,86,33,132,150,50,28,69,13,43,12,26,24,68,45,34,40,28,151,51,25,88,270 Rothia,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0 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Selenomonas,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,1,0,0,0,0,0,0 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Sneathia,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 Solirubrobacter,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 Solobacterium,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,3,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,0,0,0,0,0,0,0,0,0 Sphingomonas,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 Sporacetigenium,0,0,54,25,0,0,0,0,0,0,3,33,24,2,0,0,4,3,0,0,0,0,0,0,2,1,4,0,0,0,9,0,0,1,0,0,0,0,0,0,14,0,1,19,21,28,3,2,0,1,0,0,4,8,40,0,1,0,0,5,0,0,6,0,1,2,0,0,0,0,0,5,0,0,4,0,0,2,16,0,0,0,0,0,8,9,0,16,19,75,1,0,0,45,0,0,0,4,0,7,12,0,0,0,0,0,0,0,6,0,3,0,5,0,3,0,55,4,3,0,0,0,0,0,4,26,0,25,0,0,0,1,0,4,3,0,5,0,1,0,0,0,0,0,0,0,0,2,0,0,0,2,8,0,0,7,6,0,3,5,0,5,6,0,0,11,0,28,0,0,0,3,0,0,1,0,0,0,13,29,12,3,0,14,15,0,0,0,0,0,3,31,0,0,0,0,19,6,18,5,0,0,0,1,0,15,0,47,0,0,0,6,0,0,0,0,0,0,1,0,0,0,0,0,1,5,2,0,34,0,0,0,7,0,0,0,0,0,0,0,56,1,0,0,0,0,2,6,0,0,0,10,0,0,0,0,0,0,40,0,0,1,1,0,13,2,9,3,1,3,50,1,0,0,1,13,0,0 Sporobacter,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,5,0,1,0,3,5,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,2,2,0,1,3,0,0,9,0,1,0,0,0,0,4,0,0,2,1,0,0,0,7,0,0,1,0,0,0,6,3,0,5,0,0,0,3,0,0,0,0,0,0,0,0,0,0,4,20,0,0,0,0,0,0,0,2,0,2,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,15,0,0,0,0,0,0,2,14,0,0,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,17,0,0,1,0,0,0,0,33,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,10,0,0,0,0,6,0,15,2,0,0,0,0,0,0,0,1,0,0,16,0,0,0,0,0,0,13 Streptococcus,0,1,9,4,7,3,2,1,0,1,20,25,11,11,1,1,7,5,1,8,14,39,1,0,1,2,0,3,2,0,1,2,8,11,13,0,3,1,0,1,393,332,0,1,13,17,1,8,23,5,1,0,2,2,18,2,3,12,5,0,3,1,1,1,1,0,1,1,2,9,7,1,0,16,16,3,2,12,2,20,69,0,2,0,0,4,9,1,29,72,19,9,5,1,2,0,1,0,0,0,4,19,17,0,0,0,0,0,3,4,0,3,0,1,31,1,3,0,0,2,2,2,34,9,10,9,3,5,0,0,9,5,5,3,38,6,2,4,4,1,13,5,1,0,1,0,0,3,1,1,5,0,1,4,2,6,12,97,47,8,61,8,19,21,55,4,0,1,177,2,0,1,30,8,1,3,79,2,9,13,0,26,40,3,3,1,0,1,40,10,14,0,3,8,31,7,19,2,3,2,1,9,5,3,18,4,2,12,1,7,0,1,2,2,4,0,1,0,2,1,1,1,433,9,5,3,0,6,12,9,2,0,7,0,1,14,33,9,19,0,2,0,3,4,1,0,6,0,0,0,0,1,3,15,1,1,0,1,2,20,58,47,9,5,6,0,0,2,7,5,6,25,3,6,2,1,0,12 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library(DirichletMultinomial) library(lattice) library(xtable) library(parallel) ################################################### ### code chunk number 2: colors ################################################### options(width=70, digits=2) full <- FALSE .qualitative <- DirichletMultinomial:::.qualitative dev.off <- function(...) invisible(grDevices::dev.off(...)) ################################################### ### code chunk number 3: data-input ################################################### fl <- system.file(package="DirichletMultinomial", "extdata", "Twins.csv") count <- t(as.matrix(read.csv(fl, row.names=1))) count[1:5, 1:3] ################################################### ### code chunk number 4: taxon-counts ################################################### cnts <- log10(colSums(count)) pdf("taxon-counts.pdf") densityplot(cnts, xlim=range(cnts), xlab="Taxon representation (log 10 count)") dev.off() ################################################### ### code chunk number 5: fit ################################################### if (full) { fit <- mclapply(1:7, dmn, count=count, verbose=TRUE) save(fit, file=file.path(tempdir(), "fit.rda")) } else data(fit) fit[[4]] ################################################### ### code chunk number 6: min-laplace ################################################### lplc <- sapply(fit, laplace) pdf("min-laplace.pdf") plot(lplc, type="b", xlab="Number of Dirichlet Components", ylab="Model Fit") dev.off() (best <- fit[[which.min(lplc)]]) ################################################### ### code chunk number 7: mix-weight ################################################### mixturewt(best) head(mixture(best), 3) ################################################### ### code chunk number 8: fitted ################################################### pdf("fitted.pdf") splom(log(fitted(best))) dev.off() ################################################### ### code chunk number 9: isoMDS ################################################### ################################################### ### code chunk number 10: isoMDS-plot ################################################### ################################################### ### code chunk number 11: posterior-mean-diff ################################################### p0 <- fitted(fit[[1]], scale=TRUE) # scale by theta p4 <- fitted(best, scale=TRUE) colnames(p4) <- paste("m", 1:4, sep="") (meandiff <- colSums(abs(p4 - as.vector(p0)))) sum(meandiff) ################################################### ### code chunk number 12: table-1 ################################################### diff <- rowSums(abs(p4 - as.vector(p0))) o <- order(diff, decreasing=TRUE) cdiff <- cumsum(diff[o]) / sum(diff) df <- head(cbind(Mean=p0[o], p4[o,], diff=diff[o], cdiff), 10) ################################################### ### code chunk number 13: xtable ################################################### xtbl <- xtable(df, caption="Taxonomic contributions (10 largest) to Dirichlet components.", label="tab:meandiff", align="lccccccc") print(xtbl, hline.after=0, caption.placement="top") ################################################### ### code chunk number 14: heatmap-similarity ################################################### pdf("heatmap1.pdf") heatmapdmn(count, fit[[1]], best, 30) dev.off() ################################################### ### code chunk number 15: twin-pheno ################################################### fl <- system.file(package="DirichletMultinomial", "extdata", "TwinStudy.t") pheno0 <- scan(fl) lvls <- c("Lean", "Obese", "Overwt") pheno <- factor(lvls[pheno0 + 1], levels=lvls) names(pheno) <- rownames(count) table(pheno) ################################################### ### code chunk number 16: subsets ################################################### counts <- lapply(levels(pheno), csubset, count, pheno) sapply(counts, dim) keep <- c("Lean", "Obese") count <- count[pheno %in% keep,] pheno <- factor(pheno[pheno %in% keep], levels=keep) ################################################### ### code chunk number 17: fit-several- ################################################### if (full) { bestgrp <- dmngroup(count, pheno, k=1:5, verbose=TRUE, mc.preschedule=FALSE) save(bestgrp, file=file.path(tempdir(), "bestgrp.rda")) } else data(bestgrp) ################################################### ### code chunk number 18: best-several ################################################### bestgrp lapply(bestgrp, mixturewt) c(sapply(bestgrp, laplace), `Lean+Obese`=sum(sapply(bestgrp, laplace)), Single=laplace(best)) ################################################### ### code chunk number 19: confusion ################################################### xtabs(~pheno + predict(bestgrp, count, assign=TRUE)) ################################################### ### code chunk number 20: cross-validate ################################################### if (full) { ## full leave-one-out; expensive! xval <- cvdmngroup(nrow(count), count, c(Lean=1, Obese=3), pheno, verbose=TRUE, mc.preschedule=FALSE) save(xval, file=file.path(tempdir(), "xval.rda")) } else data(xval) ################################################### ### code chunk number 21: ROC-dmngroup ################################################### bst <- roc(pheno[rownames(count)] == "Obese", predict(bestgrp, count)[,"Obese"]) bst$Label <- "Single" two <- roc(pheno[rownames(xval)] == "Obese", xval[,"Obese"]) two$Label <- "Two group" both <- rbind(bst, two) pars <- list(superpose.line=list(col=.qualitative[1:2], lwd=2)) pdf("roc.pdf") xyplot(TruePostive ~ FalsePositive, group=Label, both, type="l", par.settings=pars, auto.key=list(lines=TRUE, points=FALSE, x=.6, y=.1), xlab="False Positive", ylab="True Positive") dev.off() ################################################### ### code chunk number 22: sessionInfo ################################################### toLatex(sessionInfo()) DirichletMultinomial/inst/doc/DirichletMultinomial.Rnw0000644000175000017500000002327514136047775023111 0ustar nileshnilesh%\VignetteIndexEntry{An introduction to DirichletMultinomial} %\VignetteDepends{} %\VignetteKeywords{Microbial metagenomic clustering and classification} %\VignettePackage{DirichletMultinomial} \documentclass[]{article} \usepackage{authblk} \usepackage{times} \usepackage{hyperref} \newcommand{\Rfunction}[1]{{\texttt{#1}}} \newcommand{\Robject}[1]{{\texttt{#1}}} \newcommand{\Rpackage}[1]{{\textit{#1}}} \newcommand{\Rfunarg}[1]{{\texttt{#1}}} \newcommand{\Rclass}[1]{{\textit{#1}}} \newcommand{\Rcode}[1]{{\texttt{#1}}} \newcommand{\software}[1]{\textsf{#1}} \newcommand{\R}{\software{R}} \newcommand{\DirichletMultinomial}{\Rpackage{DirichletMultinomial}} \title{\Rpackage{DirichletMultinomial} for Clustering and Classification of Microbiome Data} \author{Martin Morgan} \date{Modified: 6 March 2012. Compiled: \today} \begin{document} \maketitle This document illustrates the main features of the \Rpackage{DirichletMultinomial} package, and in the process replicates key tables and figures from \cite{10.1371/journal.pone.0030126}. We start by loading the package, in addition to the packages \Rpackage{lattice} (for visualization) and \Rpackage{parallel} (for use of multiple cores during cross-validation). %% <>= library(DirichletMultinomial) library(lattice) library(xtable) library(parallel) @ %% We set the width of \R{} output to 70 characters, and the number of floating point digits displayed to two. The \Robject{full} flag is set to \Rcode{FALSE}, so that cached values are used instead of re-computing during production of this vignette. The package defines a set of standard colors; we use \Rcode{.qualitative} during visualization. \Rfunction{dev.off} is redefined to return without displaying results %% <>= options(width=70, digits=2) full <- FALSE .qualitative <- DirichletMultinomial:::.qualitative dev.off <- function(...) invisible(grDevices::dev.off(...)) @ \section{Data} The data used in \cite{10.1371/journal.pone.0030126} is included in the package. We read the data in to a matrix \Robject{count} of samples $\times$ taxa. %% <>= fl <- system.file(package="DirichletMultinomial", "extdata", "Twins.csv") count <- t(as.matrix(read.csv(fl, row.names=1))) count[1:5, 1:3] @ %% Figure~\ref{fig:taxon-counts} shows the distribution of reads from each taxon, on a log scale. %% <>= cnts <- log10(colSums(count)) pdf("taxon-counts.pdf") densityplot(cnts, xlim=range(cnts), xlab="Taxon representation (log 10 count)") dev.off() @ \begin{figure} \centering \includegraphics[width=.65\textwidth]{taxon-counts} \caption{Density of taxa, across samples} \label{fig:taxon-counts} \end{figure} \section{Clustering} The \Rfunction{dmn} function fits a Dirichlet-Multinomial model, taking as input the count data and a parameter $k$ representing the number of Dirichlet components to model. Here we fit the count data to values of $k$ from 1 to 7, displaying the result for $k = 4$. A sense of the model return value is provided by the documentation for the \R{} object \Robject{fit}, \Rcode{class ? DMN}. %% <>= if (full) { fit <- mclapply(1:7, dmn, count=count, verbose=TRUE) save(fit, file=file.path(tempdir(), "fit.rda")) } else data(fit) fit[[4]] @ %% The return value can be queried for measures of fit (Laplace, AIC, BIC); these are plotted for different $k$ in Figure~\ref{fig:min-laplace}. The best fit is for $k=4$ distinct Dirichlet components. %% <>= lplc <- sapply(fit, laplace) pdf("min-laplace.pdf") plot(lplc, type="b", xlab="Number of Dirichlet Components", ylab="Model Fit") dev.off() (best <- fit[[which.min(lplc)]]) @ %% In addition to \Rfunction{laplace} goodness of fit can be assessed with the \Rfunction{AIC} and \Rfunction{BIC} functions. \begin{figure} \centering \includegraphics[width=.65\textwidth]{min-laplace} \caption{Model fit as a function of Dirichlet component number} \label{fig:min-laplace} \end{figure} The \Rfunction{mixturewt} function reports the weight $\pi$ and homogeneity $\theta$ (large values are more homogeneous) of the fitted model. \Rfunction{mixture} returns a matrix of sample x estimated Dirichlet components; the argument \Rfunarg{assign} returns a vector of length equal to the number of samples indicating the component with maximum value. %% <>= mixturewt(best) head(mixture(best), 3) @ %% The \Rfunction{fitted} function describes the contribution of each taxonomic group (each point in the panels of Figure~\ref{fig:fitted} to the Dirichlet components; the diagonal nature of the points in a panel suggest that the Dirichlet components are correlated, perhaps reflecting overall numerical abundance. %% <>= pdf("fitted.pdf") splom(log(fitted(best))) dev.off() @ \begin{figure} \centering \includegraphics[width=.65\textwidth]{fitted} \caption{Taxa fitted to Dirichlet components 1-4.} \label{fig:fitted} \end{figure} <>= @ <>= @ The posterior mean difference between the best and single-component Dirichlet multinomial model measures how each component differs from the population average; the sum is a measure of total difference from the mean. %% <>= p0 <- fitted(fit[[1]], scale=TRUE) # scale by theta p4 <- fitted(best, scale=TRUE) colnames(p4) <- paste("m", 1:4, sep="") (meandiff <- colSums(abs(p4 - as.vector(p0)))) sum(meandiff) @ %% Table~\ref{tab:meandiff} summarizes taxonomic contributions to each Dirichlet component. %% <>= diff <- rowSums(abs(p4 - as.vector(p0))) o <- order(diff, decreasing=TRUE) cdiff <- cumsum(diff[o]) / sum(diff) df <- head(cbind(Mean=p0[o], p4[o,], diff=diff[o], cdiff), 10) @ <>= xtbl <- xtable(df, caption="Taxonomic contributions (10 largest) to Dirichlet components.", label="tab:meandiff", align="lccccccc") print(xtbl, hline.after=0, caption.placement="top") @ Figure~\ref{fig:heatmap1} shows samples arranged by Dirichlet component, with samples placed into the component for which they had the largest fitted value. %% <>= pdf("heatmap1.pdf") heatmapdmn(count, fit[[1]], best, 30) dev.off() @ \begin{figure} \centering \includegraphics[width=.65\textwidth]{heatmap1} \caption{Samples arranged by Dirichlet component. Narrow columns are samples, broader columns component averages. Rows are taxonomic groups. Color represents square-root counts, with dark colors corresponding to larger counts.} \label{fig:heatmap1} \end{figure} \section{Generative classifier} The following reads in phenotypic information (`Lean', `Obese', `Overweight') for each sample. %% <>= fl <- system.file(package="DirichletMultinomial", "extdata", "TwinStudy.t") pheno0 <- scan(fl) lvls <- c("Lean", "Obese", "Overwt") pheno <- factor(lvls[pheno0 + 1], levels=lvls) names(pheno) <- rownames(count) table(pheno) @ %% Here we subset the count data into sub-counts, one for each phenotype. We retain only the Lean and Obese groups for subsequent analysis. %% <>= counts <- lapply(levels(pheno), csubset, count, pheno) sapply(counts, dim) keep <- c("Lean", "Obese") count <- count[pheno %in% keep,] pheno <- factor(pheno[pheno %in% keep], levels=keep) @ The \Rfunction{dmngroup} function identifies the best (minimum Laplace score) Dirichlet-multinomial model for each group. %% <>= if (full) { bestgrp <- dmngroup(count, pheno, k=1:5, verbose=TRUE, mc.preschedule=FALSE) save(bestgrp, file=file.path(tempdir(), "bestgrp.rda")) } else data(bestgrp) @ %% The Lean group is described by a model with one component, the Obese group by a model with three components. Three of the four Dirichlet components of the original single group (\Rcode{best}) model are represented in the Obese group, the other in the Lean group. The total Laplace score of the two group model is less than of the single-group model, indicating information gain from considering groups separately. %% <>= bestgrp lapply(bestgrp, mixturewt) c(sapply(bestgrp, laplace), `Lean+Obese`=sum(sapply(bestgrp, laplace)), Single=laplace(best)) @ The \Rfunction{predict} function assigns samples to classes; the confusion matrix shows that the classifier is moderately effective. %% <>= xtabs(~pheno + predict(bestgrp, count, assign=TRUE)) @ %% The \Rfunction{cvdmngroup} function performs cross-validation. This is a computationally expensive step. %% <>= if (full) { ## full leave-one-out; expensive! xval <- cvdmngroup(nrow(count), count, c(Lean=1, Obese=3), pheno, verbose=TRUE, mc.preschedule=FALSE) save(xval, file=file.path(tempdir(), "xval.rda")) } else data(xval) @ %% Figure~\ref{fig:roc} shows an ROC curve for the single and two-group classifier. The single group classifier is performing better than the two-group classifier. %% <>= bst <- roc(pheno[rownames(count)] == "Obese", predict(bestgrp, count)[,"Obese"]) bst$Label <- "Single" two <- roc(pheno[rownames(xval)] == "Obese", xval[,"Obese"]) two$Label <- "Two group" both <- rbind(bst, two) pars <- list(superpose.line=list(col=.qualitative[1:2], lwd=2)) pdf("roc.pdf") xyplot(TruePostive ~ FalsePositive, group=Label, both, type="l", par.settings=pars, auto.key=list(lines=TRUE, points=FALSE, x=.6, y=.1), xlab="False Positive", ylab="True Positive") dev.off() @ \begin{figure} \centering \includegraphics[width=.65\textwidth]{roc} \caption{Receiver-operator curves for the single and two-group classifiers.} \label{fig:roc} \end{figure} <>= toLatex(sessionInfo()) @ \bibliographystyle{abbrv} \bibliography{References} \end{document} DirichletMultinomial/inst/doc/DirichletMultinomial.pdf0000644000175000017500000106640214136070214023074 0ustar nileshnilesh%PDF-1.5 % 19 0 obj << /Length 1865 /Filter /FlateDecode >> stream xڵXKoFW>Qh>I P( !h$m%@{ܝ7uf2JZ3[̤S"73'Ѕ-׳ɻˤ^nFwۮ7ί`ԘC*d^R64|4y ~zVx g 0nPR2|ĽGS* p{>͍qŕk܀]Ѯ3 XP,pt.B/eK/Qxa󰹙/@xX _F1*KnߒȤCcJzBN94B"(DX-v/NI+KVwޮhx`]`o Ԗ[ \*!$ME6څFa7P:*C c[ ܷeI8{6;& =2ZH8hA!+D޲x-m*OǁeySsmb[/ɯ^mr#qH Ŗ]'!(?zS}K>VuO1qf!tq:K.:mҜqwL}>DeՉeƑC㍰BTI5'Ofl¬CPy~v!saAwvu2r0K?W[f#*4e{ʘknjfzى6e`\p`YM=TL98Qn.¿MK!|,#Ղ_g-u 2R4=,゙E֓g]ܴn?Ң+luՐUu~FToy?q2UaK)leW[$,#]4*`:iZ=dw1K*Oovg6`}TzUhJC {o \n\^1;>xtž;S@!qfOmh?,b嗩- ܊,3p HTGl{?ePPbH zTj|:.YbBLj)[4 =e% }5LJigiBѦC}Y"Ip_x,Tnz 2^`_&?Ó$D?2vcMy釓@IuS:zOO6{b=A>F$]Rvz-]mY OK> stream xڝVߏ4~_S*o3ɉw'@:$ЬĉG/ݣAk9`A{ wHGDiяn>~ d5,4 z򑭒M]'N0\>0&KLLRo٬;J"bϫNTԘO"։fr5>mG$|Vݡ5Sz֬Pa:/"w4eh,hopI)qrpi+.EPy@/) xdHqnGh>Aԯ#EBﱘB9zBm NPQ Gc'H.L+]cqfUmYYDx]u*1}]pń*'*=KCN glgIHSjeUS zÍJ BWNϳ-GP: z* +ȼ 7WhF5|PY) s 7{ # endstream endobj 35 0 obj << /Type /XObject /Subtype /Form /FormType 1 /PTEX.FileName (/tmp/RtmpHnRJbM/Rbuild3b679c19452021/DirichletMultinomial/vignettes/taxon-counts.pdf) /PTEX.PageNumber 1 /PTEX.InfoDict 42 0 R /BBox [0 0 504 504] /Resources << /ProcSet [ /PDF /Text ] /Font << /F2 43 0 R>> /ExtGState << >>/ColorSpace << /sRGB 44 0 R >>>> /Length 10217 /Filter /FlateDecode >> stream x}K-93oGLyH%.R,*{=@K& Y^zۑ?~?߾{?~?-RJe?ry^3 P7??o~?~ݷ`_|壴n0_1Ǩt}Gu?~_߭?Ƿ߭-MqӃ5oc|÷>Wzo~q[Tt^&z PzT W|TckDw*(V[V<z\t]3ي|tr*GP}.WzTc+nLw_MW컡CZexV@8Oϵ;_^4 +=17;xA7'a9%\{n"a폺J\Y"ov인Dgƻ t x71t^JzQSFWcI5*i cI1G0^w :(ݫ7;yuMW<_]Me6^=~^2\z%G5ttskӈ<:wd-M pr ] >УSXs#W%v5w_W^VfrJk.q=ڹ]l74^m17;x}6-vxa?mSRބ ]hdIcz,uIa} 0%%ߤNi8Wu=tثUCt&rJ@jLcp _f2Xs뇯%p,ᦝ2v~`  O=z[^Q )جd󞻭>4n vYKW3@F`KGݰ,7z%^5t!f3jpJvka,5]-f`@_ @e1Ft'V6j4t ^u^@[@q7v`J4 2]k c+nLw*GOҰzZ}Z_1?-{3ېO~N0i ރ+=pA4^/HXGCyc6Y핋b'7JʇFql:U&UM/19ᓦ3y؛'.'4=J 7N<|Y0r>C0h#سH5k!9!l[0pQl~cq\y4)j +k G6M!Gܦ@WzTc+nLwjɲde/U@%c~ P,\s`Xc앏aM٦ٽ P3&^O:_H>uCO, TZ9řvK[l:Ua2g:,ݫi1`x-}J_T hmq)^}epA4oN}%x$>8.x=7D<.cEy%yHp|GHr?=)y3u0m tثU0ݤ^Q0+^߅M6ma\Є@wTn ela3#lf~6_hЂw;C%PIs"T2et9\{caB+b* :kz#3N_;ѼeyLz/6uH'`{4pMu{z łh0J* h w'[‚@ 7*w,资c knDwjd& b2^+$:iՀxU՘Ɗӝ8 c)hXJy}j p0R`}!c]^S?1t!+/ƥ(>&M_)'{EB kt^n%t|Pǹ57;y/._:\t|˗N/.+W|pxc ܾ㰁a/6p;lq \6pb.oE:8L_K eu:<ʷ@ W>tkBݫhkcM>OFGCkc+Sn_jwx69,l`z 5 Hv ,1ɫU:#i8 ĭ X*KʺhPNAWv%4_)ɫL-V!ݠL=rHHMDIt )2rH0yLcō^xҤ-tA } "?C)1`Ř-bP6 Gt۽ PƚѝBGX`uYm5fe2׾%zգQ!/{ }pͷN^2L)J!vv'\ۓ~vh,hw"{2O8`?V0oAd 8K_,*dwt:wckDw;A!0A4L9Rw awnt;/ 270eI ^FL#kgZ3Q;@urAe@QYRbocA' 7Wzc6";x}i da~Mêim+3*Pch9KMsK|DmێYT'U8~*ۏ* hi JE;rͺ7F^ÏRRi ^;<^ ^>2}* ؃x٬0WdYF?Pg«c&z͡bLa͍N^Ò&A;D T]T8PF66bG E#wSLϽ>?XgW@K>eثѾZE-Q5,:%|ѐ3U՘7;yS#Zxjd`;%\iYwJt|ͨ{E>oD$2LKXOkHi1W_/EU[oK܇ݓO#}nOg;$ƒ;x=-7 !ECu!B!۞zXs<A{.fd!(:l7 A8t9[VTL8hy4І+)LoIa;#SR6ܐtz$e1?e::xk`i1K+]ݲ WXBnfG%Zg,I70OK]֍^ Qi1{yj;S;mN($[j'Zxt׫MWzLcwEjz5fZO, .FB'܍z tCAt 5c knDw(56Y@`&|P]Rbn:56PL#kaPilw^C5<#y4 a=*J7MT#lS @>T^Kd"bLct'o#7ɝ&:셋\(o=Ff}hб2봣TkqbYb:h(Qݫ02UA, ר+1_y4'\L(jaA͢N;'psģBʦKâVZilq@w^#(z9E$g 9n`G6k.:tdAX46Y3)D9@,OKtۥA6s€seE'h6h(wV=c2Y5y ?2.)9MI-s fQE^T<U齊ps<*r}p<*rxܼ`T8 o-Vjb!e`:w(4܈ V~7n8,oCoO$m ,3AwUD%6өڽ qΫgMG$.0]+H O,8O86 CE ZEؚѝ'Gwԝj֛yۀy7~a;Nzn`Mܽ P3ݧHwa54) *ƽcgTbRTB-wP{7.!_Hw#jq`H ,pA.):h5b ;+NR֙֙zG9f`ō^דXe4THIxs-UΫ` m~jHtٻWz>^C=N%D {#\x`ƒ.e+)ucLc#*IvaA*wu4 9ll%HxY4И%76 N^}JQx> &Q4*(wM VxtخCU 资c]L/MbٴòeӿX6la7;abmò_,[PR@3* 6N;n2w^Q2~/=ɗПT^PNoeYӅ||a^#KN||!6ˑq?ظR5tԥ;E[}[+̊LicϺNzգjP+fI9^ w=ѝըݧ:/9unl:DvwPoOrx;s%|G8 n9Vj@Ve;Э7]V֌z<15vNghݖ؏Jw6{!q׉n7*@cknDwE< ~qECG{=~ Q4LCIg) f@[InƪNt9JR<2ܡ9ئkDt8juoKCv1Ȳ%r!s)s7@֠{G9f`MaQ6EXEX-NYi.Ѻva9k}üzkQtUQb>*稢{\88UQEa9x{Й?VB_vkBXucU4.!t[DpfnnLw}z:"05$2CG#|ϰ́|W=݈':U嘁aw^߭)犗Xa}Xa#"Aa k²PӶˏmW\9 gLwhՋ 1V"\uP>8 \\ :Wh ܮ-WU}vqnnLw빺T7N7K:xE8% "規F [o*@V'X-.?&׫ UE8.}uQi{y.VZ}u^Hw mh1SV܍w' 0b.$m:mkGGײ:Q?},ʳl0{+3g:vhsqH9iUC_m~3tKg/Pb4L\^XqcYO''E+BWY6[W pZrDؖ}ntJxKѐnĽ PΚcvt'a%_&7zgz G9+7͚{F j+x W17;x }T]^w zTw{nJR6" #3kSi1kw,7cƑqXؐœ yb yux@9PGێ1M6IwK 70:O c3&5@^c"i;x s9en(,/4Q3Q0(OdaR \17;y}ϾSh0qtyݑ봁ty> : /2{F-gt>2xwFCjk8j2\s{mN+F(ڋG xysc/wJ3gBx< -Semópdgf}l&3fT'Ϲhr)x{4܂Q Ī6EkaYģj&t<F1Ft'on#ípҞ=s^;rؤwţ3ܔ<~|羽gzNa+K!k߾zh~ױtLg {ՀHo :,ݫ9J+nLwO$x@PBaq*,;xJVX@tTIVX1Awse ~G×]ӷA;_' 79~6SUoLĺR41˽o/]`-bhƷ om s-O i%^ >Z «sL(%N3,Ͻ5_?}5k8w@yPob& UN; 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