qqman/0000755000177500001440000000000013062373042011707 5ustar deepayanusersqqman/inst/0000755000177500001440000000000013017117002012654 5ustar deepayanusersqqman/inst/doc/0000755000177500001440000000000013017117634013434 5ustar deepayanusersqqman/inst/doc/qqman.html0000644000177500001440000663654312536016436015471 0ustar deepayanusers Intro to the <strong>qqman</strong> package

Intro to the qqman package

The qqman package includes functions for creating manhattan plots and q-q plots from GWAS results. The gwasResults data.frame included with the package has simulated results for 16,470 SNPs on 22 chromosomes. Take a look at the data:

str(gwasResults)

'data.frame':   16470 obs. of  5 variables:
 $ SNP   : chr  "rs1" "rs2" "rs3" "rs4" ...
 $ CHR   : int  1 1 1 1 1 1 1 1 1 1 ...
 $ BP    : int  1 2 3 4 5 6 7 8 9 10 ...
 $ P     : num  0.915 0.937 0.286 0.83 0.642 ...
 $ zscore: num  0.107 0.0789 1.0666 0.2141 0.4653 ...

head(gwasResults)

  SNP CHR BP         P    zscore
1 rs1   1  1 0.9148060 0.1069785
2 rs2   1  2 0.9370754 0.0789462
3 rs3   1  3 0.2861395 1.0666287
4 rs4   1  4 0.8304476 0.2141275
5 rs5   1  5 0.6417455 0.4652597
6 rs6   1  6 0.5190959 0.6447396

tail(gwasResults)

          SNP CHR  BP         P    zscore
16465 rs16465  22 530 0.5643702 0.5763624
16466 rs16466  22 531 0.1382863 1.4822028
16467 rs16467  22 532 0.3936999 0.8529268
16468 rs16468  22 533 0.1778749 1.3473271
16469 rs16469  22 534 0.2393020 1.1767332
16470 rs16470  22 535 0.2630441 1.1192251

How many SNPs on each chromosome?

as.data.frame(table(gwasResults$CHR))

   Var1 Freq
1     1 1500
2     2 1191
3     3 1040
4     4  945
5     5  877
6     6  825
7     7  784
8     8  750
9     9  721
10   10  696
11   11  674
12   12  655
13   13  638
14   14  622
15   15  608
16   16  595
17   17  583
18   18  572
19   19  562
20   20  553
21   21  544
22   22  535

Creating manhattan plots

Now, let's make a basic manhattan plot. 

manhattan(gwasResults)

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We can also pass in other graphical parameters. Let's add a title (main=), increase the y-axis limit (ylim=), reduce the point size to 60% (cex=), and reduce the font size of the axis labels to 90% (cex.axis=). While we're at it, let's change the colors (col=), remove the suggestive and genome-wide significance lines, and supply our own labels for the chromosomes:

manhattan(gwasResults, main = "Manhattan Plot", ylim = c(0, 10), cex = 0.6, 
    cex.axis = 0.9, col = c("blue4", "orange3"), suggestiveline = F, genomewideline = F, 
    chrlabs = c(1:20, "P", "Q"))

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Now, let's look at a single chromosome:

manhattan(subset(gwasResults, CHR == 1))

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Let's highlight some SNPs of interest on chromosome 3. The 100 SNPs we're highlighting here are in a character vector called snpsOfInterest. You'll get a warning if you try to highlight SNPs that don't exist.

str(snpsOfInterest)

 chr [1:100] "rs3001" "rs3002" "rs3003" "rs3004" "rs3005" ...

manhattan(gwasResults, highlight = snpsOfInterest)

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We can combine highlighting and limiting to a single chromosome, and use the xlim graphical parameter to zoom in on a region of interest (between position 200-500):

manhattan(subset(gwasResults, CHR == 3), highlight = snpsOfInterest, xlim = c(200, 
    500), main = "Chr 3")

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We can also annotate SNPs based on their p-value. By default, this only annotates the top SNP per chromosome that exceeds the annotatePval threshold.

manhattan(gwasResults, annotatePval = 0.01)

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We can also annotate all SNPs that meet a threshold:

manhattan(gwasResults, annotatePval = 0.005, annotateTop = FALSE)

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Finally, the manhattan function can be used to plot any value, not just p-values. Here, we'll simply call the function passing to the p= argument the name of the column we want to plot instead of the default “P” column. In this example, let's create a test statistic (“zscore”), plot that instead of p-values, change the y-axis label, and remove the default log transformation. We'll also remove the genomewide and suggestive lines because these are only meaningful if you're plotting -log10(p-values).

# Add test statistics
gwasResults <- transform(gwasResults, zscore = qnorm(P/2, lower.tail = FALSE))
head(gwasResults)

  SNP CHR BP         P    zscore
1 rs1   1  1 0.9148060 0.1069785
2 rs2   1  2 0.9370754 0.0789462
3 rs3   1  3 0.2861395 1.0666287
4 rs4   1  4 0.8304476 0.2141275
5 rs5   1  5 0.6417455 0.4652597
6 rs6   1  6 0.5190959 0.6447396

# Make the new plot
manhattan(gwasResults, p = "zscore", logp = FALSE, ylab = "Z-score", genomewideline = FALSE, 
    suggestiveline = FALSE, main = "Manhattan plot of Z-scores")

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A few notes on creating manhattan plots:

Creating Q-Q plots

Creating Q-Q plots is straightforward - simply supply a vector of p-values to the qq() function. 

qq(gwasResults$P)

plot of chunk unnamed-chunk-12

We can otionally supply many other graphical parameters.

qq(gwasResults$P, main = "Q-Q plot of GWAS p-values", xlim = c(0, 7), ylim = c(0, 
    12), pch = 18, col = "blue4", cex = 1.5, las = 1)

plot of chunk unnamed-chunk-13

qqman/inst/doc/qqman.Rmd0000644000177500001440000001260612536016436015225 0ustar deepayanusers--- title: "Intro to the qqman package" author: "Stephen D. Turner" date: '`r Sys.Date()`' output: html_document: toc: yes --- ```{r, include=FALSE} library(qqman) library(knitr) opts_chunk$set(comment=NA, fig.width=12, fig.height=9, message=FALSE, tidy=TRUE, dpi=75) ``` # Intro to the **qqman** package ```{r generatedata, eval=FALSE, echo=FALSE} # This code used to generate the test data. Runs slow, but does the job. chrstats <- data.frame(chr=1:22, nsnps=1500) chrstats$nsnps <- with(chrstats, round(nsnps/chr^(1/3))) chrstats d <- data.frame(SNP=rep(NA, sum(chrstats$nsnps)), CHR=rep(NA, sum(chrstats$nsnps)), BP=rep(NA, sum(chrstats$nsnps)), P=rep(NA, sum(chrstats$nsnps))) snpi <- 1 set.seed(42) for (i in chrstats$chr) { for (j in 1:chrstats[i, 2]) { d[snpi, ]$SNP=paste0("rs", snpi) d[snpi, ]$CHR=i d[snpi, ]$BP=j d[snpi, ]$P=runif(1) snpi <- snpi+1 } } divisor <- c(seq(2,50,2), seq(50,2,-2)) divisor <- divisor^4 length(divisor) d[3026:3075, ]$P <- d[3026:3075, ]$P/divisor snpsOfInterest <- paste0("rs", 3001:3100) qq(d$P) manhattan(d, highlight=snpsOfInterest) gwasResults <- d save(gwasResults, file="data/gwasResults.RData") ``` The **qqman** package includes functions for creating manhattan plots and q-q plots from GWAS results. The `gwasResults` data.frame included with the package has simulated results for 16,470 SNPs on 22 chromosomes. Take a look at the data: ```{r} str(gwasResults) head(gwasResults) tail(gwasResults) ``` How many SNPs on each chromosome? ```{r} as.data.frame(table(gwasResults$CHR)) ``` ## Creating manhattan plots Now, let's make a basic manhattan plot. ```{r} manhattan(gwasResults) ``` We can also pass in other graphical parameters. Let's add a title (`main=`), increase the y-axis limit (`ylim=`), reduce the point size to 60% (`cex=`), and reduce the font size of the axis labels to 90% (`cex.axis=`). While we're at it, let's change the colors (`col=`), remove the suggestive and genome-wide significance lines, and supply our own labels for the chromosomes: ```{r} manhattan(gwasResults, main="Manhattan Plot", ylim=c(0,10), cex=0.6, cex.axis=0.9, col=c("blue4", "orange3"), suggestiveline=F, genomewideline=F, chrlabs=c(1:20, "P", "Q")) ``` Now, let's look at a single chromosome: ```{r} manhattan(subset(gwasResults, CHR==1)) ``` Let's highlight some SNPs of interest on chromosome 3. The 100 SNPs we're highlighting here are in a character vector called `snpsOfInterest`. You'll get a warning if you try to highlight SNPs that don't exist. ```{r} str(snpsOfInterest) manhattan(gwasResults, highlight=snpsOfInterest) ``` We can combine highlighting and limiting to a single chromosome, and use the `xlim` graphical parameter to zoom in on a region of interest (between position 200-500): ```{r} manhattan(subset(gwasResults, CHR==3), highlight=snpsOfInterest, xlim=c(200, 500), main="Chr 3") ``` We can also annotate SNPs based on their p-value. By default, this only annotates the top SNP per chromosome that exceeds the `annotatePval` threshold. ```{r} manhattan(gwasResults, annotatePval=0.01) ``` We can also annotate all SNPs that meet a threshold: ```{r} manhattan(gwasResults, annotatePval=0.005, annotateTop=FALSE) ``` Finally, the `manhattan` function can be used to plot any value, not just p-values. Here, we'll simply call the function passing to the `p=` argument the name of the column we want to plot instead of the default "P" column. In this example, let's create a test statistic ("zscore"), plot that instead of p-values, change the y-axis label, and remove the default log transformation. We'll also remove the genomewide and suggestive lines because these are only meaningful if you're plotting -log10(p-values). ```{r} # Add test statistics gwasResults <- transform(gwasResults, zscore=qnorm(P/2, lower.tail=FALSE)) head(gwasResults) # Make the new plot manhattan(gwasResults, p="zscore", logp=FALSE, ylab="Z-score", genomewideline=FALSE, suggestiveline=FALSE, main="Manhattan plot of Z-scores") ``` A few notes on creating manhattan plots: * Run `str(gwasResults)`. Notice that the `gwasResults` data.frame has SNP, chromosome, position, and p-value columns named `SNP`, `CHR`, `BP`, and `P`. If you're creating a manhattan plot and your column names are different, you'll have to pass the column names to the `chr=`, `bp=`, `p=`, and `snp=` arguments. See `help(manhattan)` for details. * The chromosome column must be numeric. If you have "X," "Y," or "MT" chromosomes, you'll need to rename these 23, 24, 25, etc. You can modify the source code (e.g., `fix(manhattan)`) to change the line designating the axis tick labels (`labs <- unique(d$CHR)`) to set this to whatever you'd like it to be. * If you'd like to change the color of the highlight or the suggestive/genomewide lines, you'll need to modify the source code. Search for `col="blue"`, `col="red"`, or `col="green3"` to modify the suggestive line, genomewide line, and highlight colors, respectively. ## Creating Q-Q plots Creating Q-Q plots is straightforward - simply supply a vector of p-values to the `qq()` function. ```{r} qq(gwasResults$P) ``` We can otionally supply many other graphical parameters. ```{r} qq(gwasResults$P, main="Q-Q plot of GWAS p-values", xlim=c(0,7), ylim=c(0,12), pch=18, col="blue4", cex=1.5, las=1) ```qqman/inst/doc/qqman.R0000644000177500001440000000616512536016436014707 0ustar deepayanusers## ----, include=FALSE----------------------------------------------------- library(qqman) library(knitr) opts_chunk$set(comment=NA, fig.width=12, fig.height=9, message=FALSE, tidy=TRUE, dpi=75) ## ----generatedata, eval=FALSE, echo=FALSE-------------------------------- # # This code used to generate the test data. Runs slow, but does the job. # chrstats <- data.frame(chr=1:22, nsnps=1500) # chrstats$nsnps <- with(chrstats, round(nsnps/chr^(1/3))) # chrstats # # d <- data.frame(SNP=rep(NA, sum(chrstats$nsnps)), # CHR=rep(NA, sum(chrstats$nsnps)), # BP=rep(NA, sum(chrstats$nsnps)), # P=rep(NA, sum(chrstats$nsnps))) # snpi <- 1 # set.seed(42) # for (i in chrstats$chr) { # for (j in 1:chrstats[i, 2]) { # d[snpi, ]$SNP=paste0("rs", snpi) # d[snpi, ]$CHR=i # d[snpi, ]$BP=j # d[snpi, ]$P=runif(1) # snpi <- snpi+1 # } # } # # divisor <- c(seq(2,50,2), seq(50,2,-2)) # divisor <- divisor^4 # length(divisor) # d[3026:3075, ]$P <- d[3026:3075, ]$P/divisor # snpsOfInterest <- paste0("rs", 3001:3100) # qq(d$P) # manhattan(d, highlight=snpsOfInterest) # gwasResults <- d # save(gwasResults, file="data/gwasResults.RData") ## ------------------------------------------------------------------------ str(gwasResults) head(gwasResults) tail(gwasResults) ## ------------------------------------------------------------------------ as.data.frame(table(gwasResults$CHR)) ## ------------------------------------------------------------------------ manhattan(gwasResults) ## ------------------------------------------------------------------------ manhattan(gwasResults, main="Manhattan Plot", ylim=c(0,10), cex=0.6, cex.axis=0.9, col=c("blue4", "orange3"), suggestiveline=F, genomewideline=F, chrlabs=c(1:20, "P", "Q")) ## ------------------------------------------------------------------------ manhattan(subset(gwasResults, CHR==1)) ## ------------------------------------------------------------------------ str(snpsOfInterest) manhattan(gwasResults, highlight=snpsOfInterest) ## ------------------------------------------------------------------------ manhattan(subset(gwasResults, CHR==3), highlight=snpsOfInterest, xlim=c(200, 500), main="Chr 3") ## ------------------------------------------------------------------------ manhattan(gwasResults, annotatePval=0.01) ## ------------------------------------------------------------------------ manhattan(gwasResults, annotatePval=0.005, annotateTop=FALSE) ## ------------------------------------------------------------------------ # Add test statistics gwasResults <- transform(gwasResults, zscore=qnorm(P/2, lower.tail=FALSE)) head(gwasResults) # Make the new plot manhattan(gwasResults, p="zscore", logp=FALSE, ylab="Z-score", genomewideline=FALSE, suggestiveline=FALSE, main="Manhattan plot of Z-scores") ## ------------------------------------------------------------------------ qq(gwasResults$P) ## ------------------------------------------------------------------------ qq(gwasResults$P, main="Q-Q plot of GWAS p-values", xlim=c(0,7), ylim=c(0,12), pch=18, col="blue4", cex=1.5, las=1) qqman/NAMESPACE0000644000177500001440000000024113062271472013127 0ustar deepayanusers# Generated by roxygen2: do not edit by hand export(manhattan) export(qq) import(graphics) import(utils) importFrom(calibrate,textxy) importFrom(stats,ppoints) qqman/NEWS.md0000644000177500001440000000156213062270762013016 0ustar deepayanusers# qqman 0.1.4 * Minor fix to location to referenced image for pandoc self-contained README.html generation. # qqman 0.1.3 * Annotate SNPs below a p-value threshold with the `annotatePval=` option. See vignette for details. * Annotate the top SNP on each chromosome with the `annotateTop=` option. See vignette for details. # qqman 0.1.2 * Does not assume that SNPs are evenly distributed across chromosomes when deciding where to place the tick in the center of the chromosome. * Changed single chromosome x-axis notation to use Mb instead of raw pos * `qq()` accepts graphical parameters the same way as `manhattan()` * Removed default `xlim` * Citation details on package load * Added axis label options * Removed `ymax` argument in favor of allowing user to set `ylim` in `...` * Option to *not* take log of p-value # qqman 0.1.1 * Fixed a bunch of typos in the vignette qqman/data/0000755000177500001440000000000013017120026012610 5ustar deepayanusersqqman/data/gwasResults.RData0000644000177500001440000040511412335152166016071 0ustar deepayanusersT۽`]vrwN^ẃrJH}QCT%7zTAן~O˟~~_~_ןʟ3~ٿ??>s⯟w~ݟ|~?ws7>ws7>ws7>ws7>ws7>ws7?ws7?ws7?ws7?ws7?ws>ws>ws>ws>ws>ws?ws?ws?ws?ws?wsw>wsw>wsw>wsw>wsw>wsw?wsw?wsw?wsw?wsw?wܽs~{?w}s}>w}w֍~#>c}|]p w.8|]]]]]]]]]]]P]P]P]P]P]]]]]]0]0]0]0]0]]]]]]p w.~]p w.x}] 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Defaults to #' PLINK's "CHR." Said column must be numeric. If you have X, Y, or MT #' chromosomes, be sure to renumber these 23, 24, 25, etc. #' @param bp A string denoting the column name for the chromosomal position. #' Defaults to PLINK's "BP." Said column must be numeric. #' @param p A string denoting the column name for the p-value. Defaults to #' PLINK's "P." Said column must be numeric. #' @param snp A string denoting the column name for the SNP name (rs number). #' Defaults to PLINK's "SNP." Said column should be a character. #' @param col A character vector indicating which colors to alternate. #' @param chrlabs A character vector equal to the number of chromosomes #' specifying the chromosome labels (e.g., \code{c(1:22, "X", "Y", "MT")}). #' @param suggestiveline Where to draw a "suggestive" line. Default #' -log10(1e-5). Set to FALSE to disable. #' @param genomewideline Where to draw a "genome-wide sigificant" line. Default #' -log10(5e-8). Set to FALSE to disable. #' @param highlight A character vector of SNPs in your dataset to highlight. #' These SNPs should all be in your dataset. #' @param logp If TRUE, the -log10 of the p-value is plotted. It isn't very #' useful to plot raw p-values, but plotting the raw value could be useful for #' other genome-wide plots, for example, peak heights, bayes factors, test #' statistics, other "scores," etc. #' @param annotatePval If set, SNPs below this p-value will be annotated on the plot. #' @param annotateTop If TRUE, only annotates the top hit on each chromosome that is below the annotatePval threshold. #' @param ... Arguments passed on to other plot/points functions #' #' @return A manhattan plot. #' #' @keywords visualization manhattan #' #' @import utils #' @import graphics #' #' @examples #' manhattan(gwasResults) #' #' @importFrom calibrate textxy #' #' @export manhattan <- function(x, chr="CHR", bp="BP", p="P", snp="SNP", col=c("gray10", "gray60"), chrlabs=NULL, suggestiveline=-log10(1e-5), genomewideline=-log10(5e-8), highlight=NULL, logp=TRUE, annotatePval = NULL, annotateTop = TRUE, ...) { # Not sure why, but package check will warn without this. CHR=BP=P=index=NULL # Check for sensible dataset ## Make sure you have chr, bp and p columns. if (!(chr %in% names(x))) stop(paste("Column", chr, "not found!")) if (!(bp %in% names(x))) stop(paste("Column", bp, "not found!")) if (!(p %in% names(x))) stop(paste("Column", p, "not found!")) ## warn if you don't have a snp column if (!(snp %in% names(x))) warning(paste("No SNP column found. OK unless you're trying to highlight.")) ## make sure chr, bp, and p columns are numeric. if (!is.numeric(x[[chr]])) stop(paste(chr, "column should be numeric. Do you have 'X', 'Y', 'MT', etc? If so change to numbers and try again.")) if (!is.numeric(x[[bp]])) stop(paste(bp, "column should be numeric.")) if (!is.numeric(x[[p]])) stop(paste(p, "column should be numeric.")) # Create a new data.frame with columns called CHR, BP, and P. d=data.frame(CHR=x[[chr]], BP=x[[bp]], P=x[[p]]) # If the input data frame has a SNP column, add it to the new data frame you're creating. if (!is.null(x[[snp]])) d=transform(d, SNP=x[[snp]]) # Set positions, ticks, and labels for plotting ## Sort and keep only values where is numeric. #d <- subset(d[order(d$CHR, d$BP), ], (P>0 & P<=1 & is.numeric(P))) d <- subset(d, (is.numeric(CHR) & is.numeric(BP) & is.numeric(P))) d <- d[order(d$CHR, d$BP), ] #d$logp <- ifelse(logp, yes=-log10(d$P), no=d$P) if (logp) { d$logp <- -log10(d$P) } else { d$logp <- d$P } d$pos=NA # Fixes the bug where one chromosome is missing by adding a sequential index column. d$index=NA ind = 0 for (i in unique(d$CHR)){ ind = ind + 1 d[d$CHR==i,]$index = ind } # This section sets up positions and ticks. Ticks should be placed in the # middle of a chromosome. The a new pos column is added that keeps a running # sum of the positions of each successive chromsome. For example: # chr bp pos # 1 1 1 # 1 2 2 # 2 1 3 # 2 2 4 # 3 1 5 nchr = length(unique(d$CHR)) if (nchr==1) { ## For a single chromosome ## Uncomment the next two linex to plot single chr results in Mb #options(scipen=999) #d$pos=d$BP/1e6 d$pos=d$BP ticks=floor(length(d$pos))/2+1 xlabel = paste('Chromosome',unique(d$CHR),'position') labs = ticks } else { ## For multiple chromosomes lastbase=0 ticks=NULL for (i in unique(d$index)) { if (i==1) { d[d$index==i, ]$pos=d[d$index==i, ]$BP } else { lastbase=lastbase+tail(subset(d,index==i-1)$BP, 1) d[d$index==i, ]$pos=d[d$index==i, ]$BP+lastbase } # Old way: assumes SNPs evenly distributed # ticks=c(ticks, d[d$index==i, ]$pos[floor(length(d[d$index==i, ]$pos)/2)+1]) # New way: doesn't make that assumption ticks = c(ticks, (min(d[d$index == i,]$pos) + max(d[d$index == i,]$pos))/2 + 1) } xlabel = 'Chromosome' #labs = append(unique(d$CHR),'') ## I forgot what this was here for... if seems to work, remove. labs <- unique(d$CHR) } # Initialize plot xmax = ceiling(max(d$pos) * 1.03) xmin = floor(max(d$pos) * -0.03) # The old way to initialize the plot # plot(NULL, xaxt='n', bty='n', xaxs='i', yaxs='i', xlim=c(xmin,xmax), ylim=c(ymin,ymax), # xlab=xlabel, ylab=expression(-log[10](italic(p))), las=1, pch=20, ...) # The new way to initialize the plot. ## See http://stackoverflow.com/q/23922130/654296 ## First, define your default arguments def_args <- list(xaxt='n', bty='n', xaxs='i', yaxs='i', las=1, pch=20, xlim=c(xmin,xmax), ylim=c(0,ceiling(max(d$logp))), xlab=xlabel, ylab=expression(-log[10](italic(p)))) ## Next, get a list of ... arguments #dotargs <- as.list(match.call())[-1L] dotargs <- list(...) ## And call the plot function passing NA, your ... arguments, and the default ## arguments that were not defined in the ... arguments. do.call("plot", c(NA, dotargs, def_args[!names(def_args) %in% names(dotargs)])) # If manually specifying chromosome labels, ensure a character vector and number of labels matches number chrs. if (!is.null(chrlabs)) { if (is.character(chrlabs)) { if (length(chrlabs)==length(labs)) { labs <- chrlabs } else { warning("You're trying to specify chromosome labels but the number of labels != number of chromosomes.") } } else { warning("If you're trying to specify chromosome labels, chrlabs must be a character vector") } } # Add an axis. if (nchr==1) { #If single chromosome, ticks and labels automatic. axis(1, ...) } else { # if multiple chrs, use the ticks and labels you created above. axis(1, at=ticks, labels=labs, ...) } # Create a vector of alternatiting colors col=rep(col, max(d$CHR)) # Add points to the plot if (nchr==1) { with(d, points(pos, logp, pch=20, col=col[1], ...)) } else { # if multiple chromosomes, need to alternate colors and increase the color index (icol) each chr. icol=1 for (i in unique(d$index)) { with(d[d$index==unique(d$index)[i], ], points(pos, logp, col=col[icol], pch=20, ...)) icol=icol+1 } } # Add suggestive and genomewide lines if (suggestiveline) abline(h=suggestiveline, col="blue") if (genomewideline) abline(h=genomewideline, col="red") # Highlight snps from a character vector if (!is.null(highlight)) { if (any(!(highlight %in% d$SNP))) warning("You're trying to highlight SNPs that don't exist in your results.") d.highlight=d[which(d$SNP %in% highlight), ] with(d.highlight, points(pos, logp, col="green3", pch=20, ...)) } # Highlight top SNPs if (!is.null(annotatePval)) { # extract top SNPs at given p-val topHits = subset(d, P <= annotatePval) par(xpd = TRUE) # annotate these SNPs if (annotateTop == FALSE) { with(subset(d, P <= annotatePval), textxy(pos, -log10(P), offset = 0.625, labs = topHits$SNP, cex = 0.45), ...) } else { # could try alternative, annotate top SNP of each sig chr topHits <- topHits[order(topHits$P),] topSNPs <- NULL for (i in unique(topHits$CHR)) { chrSNPs <- topHits[topHits$CHR == i,] topSNPs <- rbind(topSNPs, chrSNPs[1,]) } textxy(topSNPs$pos, -log10(topSNPs$P), offset = 0.625, labs = topSNPs$SNP, cex = 0.5, ...) } } par(xpd = FALSE) } qqman/R/package.R0000644000177500001440000000044712335152166013637 0ustar deepayanusers#' Create Q-Q and manhattan plots for GWAS data. #' #' A package for creating Q-Q and manhattan plots for GWAS data. See the package #' vignette for details: \cr\cr #' \code{vignette("qqman")} #' #' @name qqman #' @docType package #' @author Stephen Turner <\url{http://stephenturner.us}> NULLqqman/R/zzz.R0000644000177500001440000000064012411044403013061 0ustar deepayanusers.onAttach <- function(...){ packageStartupMessage() packageStartupMessage("For example usage please run: vignette('qqman')") packageStartupMessage() packageStartupMessage("Citation appreciated but not required:") packageStartupMessage("Turner, S.D. qqman: an R package for visualizing GWAS results using Q-Q and manhattan plots. biorXiv DOI: 10.1101/005165 (2014).") packageStartupMessage() }qqman/vignettes/0000755000177500001440000000000013062276412013722 5ustar deepayanusersqqman/vignettes/qqman.Rmd0000644000177500001440000001260612536016436015513 0ustar deepayanusers--- title: "Intro to the qqman package" author: "Stephen D. Turner" date: '`r Sys.Date()`' output: html_document: toc: yes --- ```{r, include=FALSE} library(qqman) library(knitr) opts_chunk$set(comment=NA, fig.width=12, fig.height=9, message=FALSE, tidy=TRUE, dpi=75) ``` # Intro to the **qqman** package ```{r generatedata, eval=FALSE, echo=FALSE} # This code used to generate the test data. Runs slow, but does the job. chrstats <- data.frame(chr=1:22, nsnps=1500) chrstats$nsnps <- with(chrstats, round(nsnps/chr^(1/3))) chrstats d <- data.frame(SNP=rep(NA, sum(chrstats$nsnps)), CHR=rep(NA, sum(chrstats$nsnps)), BP=rep(NA, sum(chrstats$nsnps)), P=rep(NA, sum(chrstats$nsnps))) snpi <- 1 set.seed(42) for (i in chrstats$chr) { for (j in 1:chrstats[i, 2]) { d[snpi, ]$SNP=paste0("rs", snpi) d[snpi, ]$CHR=i d[snpi, ]$BP=j d[snpi, ]$P=runif(1) snpi <- snpi+1 } } divisor <- c(seq(2,50,2), seq(50,2,-2)) divisor <- divisor^4 length(divisor) d[3026:3075, ]$P <- d[3026:3075, ]$P/divisor snpsOfInterest <- paste0("rs", 3001:3100) qq(d$P) manhattan(d, highlight=snpsOfInterest) gwasResults <- d save(gwasResults, file="data/gwasResults.RData") ``` The **qqman** package includes functions for creating manhattan plots and q-q plots from GWAS results. The `gwasResults` data.frame included with the package has simulated results for 16,470 SNPs on 22 chromosomes. Take a look at the data: ```{r} str(gwasResults) head(gwasResults) tail(gwasResults) ``` How many SNPs on each chromosome? ```{r} as.data.frame(table(gwasResults$CHR)) ``` ## Creating manhattan plots Now, let's make a basic manhattan plot. ```{r} manhattan(gwasResults) ``` We can also pass in other graphical parameters. Let's add a title (`main=`), increase the y-axis limit (`ylim=`), reduce the point size to 60% (`cex=`), and reduce the font size of the axis labels to 90% (`cex.axis=`). While we're at it, let's change the colors (`col=`), remove the suggestive and genome-wide significance lines, and supply our own labels for the chromosomes: ```{r} manhattan(gwasResults, main="Manhattan Plot", ylim=c(0,10), cex=0.6, cex.axis=0.9, col=c("blue4", "orange3"), suggestiveline=F, genomewideline=F, chrlabs=c(1:20, "P", "Q")) ``` Now, let's look at a single chromosome: ```{r} manhattan(subset(gwasResults, CHR==1)) ``` Let's highlight some SNPs of interest on chromosome 3. The 100 SNPs we're highlighting here are in a character vector called `snpsOfInterest`. You'll get a warning if you try to highlight SNPs that don't exist. ```{r} str(snpsOfInterest) manhattan(gwasResults, highlight=snpsOfInterest) ``` We can combine highlighting and limiting to a single chromosome, and use the `xlim` graphical parameter to zoom in on a region of interest (between position 200-500): ```{r} manhattan(subset(gwasResults, CHR==3), highlight=snpsOfInterest, xlim=c(200, 500), main="Chr 3") ``` We can also annotate SNPs based on their p-value. By default, this only annotates the top SNP per chromosome that exceeds the `annotatePval` threshold. ```{r} manhattan(gwasResults, annotatePval=0.01) ``` We can also annotate all SNPs that meet a threshold: ```{r} manhattan(gwasResults, annotatePval=0.005, annotateTop=FALSE) ``` Finally, the `manhattan` function can be used to plot any value, not just p-values. Here, we'll simply call the function passing to the `p=` argument the name of the column we want to plot instead of the default "P" column. In this example, let's create a test statistic ("zscore"), plot that instead of p-values, change the y-axis label, and remove the default log transformation. We'll also remove the genomewide and suggestive lines because these are only meaningful if you're plotting -log10(p-values). ```{r} # Add test statistics gwasResults <- transform(gwasResults, zscore=qnorm(P/2, lower.tail=FALSE)) head(gwasResults) # Make the new plot manhattan(gwasResults, p="zscore", logp=FALSE, ylab="Z-score", genomewideline=FALSE, suggestiveline=FALSE, main="Manhattan plot of Z-scores") ``` A few notes on creating manhattan plots: * Run `str(gwasResults)`. Notice that the `gwasResults` data.frame has SNP, chromosome, position, and p-value columns named `SNP`, `CHR`, `BP`, and `P`. If you're creating a manhattan plot and your column names are different, you'll have to pass the column names to the `chr=`, `bp=`, `p=`, and `snp=` arguments. See `help(manhattan)` for details. * The chromosome column must be numeric. If you have "X," "Y," or "MT" chromosomes, you'll need to rename these 23, 24, 25, etc. You can modify the source code (e.g., `fix(manhattan)`) to change the line designating the axis tick labels (`labs <- unique(d$CHR)`) to set this to whatever you'd like it to be. * If you'd like to change the color of the highlight or the suggestive/genomewide lines, you'll need to modify the source code. Search for `col="blue"`, `col="red"`, or `col="green3"` to modify the suggestive line, genomewide line, and highlight colors, respectively. ## Creating Q-Q plots Creating Q-Q plots is straightforward - simply supply a vector of p-values to the `qq()` function. ```{r} qq(gwasResults$P) ``` We can otionally supply many other graphical parameters. ```{r} qq(gwasResults$P, main="Q-Q plot of GWAS p-values", xlim=c(0,7), ylim=c(0,12), pch=18, col="blue4", cex=1.5, las=1) ```qqman/README.md0000644000177500001440000000353213062270626013175 0ustar deepayanusers# qqman: An R package for creating Q-Q and manhattan plots from GWAS results. [![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/qqman)](http://cran.r-project.org/package=qqman) ![qqman.gif](tools/qqman.gif) ## Citation If you'd like to cite qqman (appreciated but not required), please cite the pre-print below: Turner, S.D. qqman: an R package for visualizing GWAS results using Q-Q and manhattan plots. [*biorXiv* DOI: 10.1101/005165](http://biorxiv.org/content/early/2014/05/14/005165). ## Installation Install the stable release from CRAN: ```coffee install.packages("qqman") ``` Or install directly from github using devtools ```coffee library(devtools) install_github("stephenturner/qqman") ``` Or install the most recent development release with devtools (note, there be dragons here): ```coffee library(devtools) install_github("stephenturner/qqman", ref="dev") ``` Load the package each time you use it: ```coffee library(qqman) ``` ## Usage See the [online package vignette](http://cran.r-project.org/web/packages/qqman/vignettes/qqman.html) for more examples: ```coffee vignette("manhattan") ``` Take a look at the built-in data: ```coffee head(gwasResults) ``` Basic manhattan plot using built-in data: ```coffee manhattan(gwasResults) ``` Basic Q-Q plot using built-in data: ```coffee qq(gwasResults$P) ``` Get help: ```coffee ?manhattan ?qq ``` ## Notes * This release is substantially simplified for the sake of maintainability and creating an R package. The old code that allows confidence intervals on the Q-Q plot and allows more flexible annotation and highlighting is still available at the [version 0.0.0 tag](https://github.com/stephenturner/qqman/tree/v0.0.0). * Special thanks to Dan Capurso and Tim Knutsen for useful contributions and bugfixes. * Thanks to all the blog commenters for pointing out bugs and other issues. qqman/MD50000644000177500001440000000217513062373042012224 0ustar deepayanusersf022a46b2403ef302c08a258781b4171 *DESCRIPTION 6d818b3191e430b4dc5e64be2f7a0f77 *NAMESPACE 9b09f7b747f86fe5ee2bababd94ff8da *NEWS.md 4703c59c0422edc311c09f34a79425c2 *R/gwasResults-data.R 16373184eb5c5cd7359af8e458903eac *R/manhattan.R 6f2bc34e71e66aef115d0a9dff9c2c78 *R/package.R ba406a08010ce9f456757cec1c391170 *R/qq.R 1d8183045f8520b6f1f7a05b8e8eddd4 *R/snpsOfInterest-data.R 4e7b578263eee2e20de8b161e4c08af4 *R/zzz.R 0c5de2132b11035c80fad042ba49fa63 *README.md 163dfff68fd924f44e30f4ba1dab5d9f *build/vignette.rds ff43bc38623980d5b64e423362075c10 *data/gwasResults.RData a10481df0b1192bc3f599e14414e0a55 *data/snpsOfInterest.RData f79658ca04fe49041faee56fd1b973e5 *inst/doc/qqman.R 3c08b1a29af86383f51c9f039bcd10f7 *inst/doc/qqman.Rmd c3ffd9e4c8e62c22b5b05338cbdf0adc *inst/doc/qqman.html 5fcd5f195193e0c06c67306869eb5be0 *man/gwasResults.Rd 1e5453a5e9abaa2d08bb4ea3e10e79da *man/manhattan.Rd 84d4a5c8877bcff6b205d90cf45a9afe *man/qq.Rd 5280e7e7b388f7c3a6b5c431499a29aa *man/qqman.Rd df5c691e8de49e89eb05316e6aacce0d *man/snpsOfInterest.Rd 3f6ead1f8902b40ec314a33eccfc1b32 *tools/qqman.gif 3c08b1a29af86383f51c9f039bcd10f7 *vignettes/qqman.Rmd qqman/build/0000755000177500001440000000000013062276412013011 5ustar deepayanusersqqman/build/vignette.rds0000644000177500001440000000032413062276412015347 0ustar deepayanusersb```b`ffb`b2 1# ',,M MA+)W(T*T4\#2Jrsdء! H 2ּbt.y) 3GZY_Ӄ -3'foHf e2|s  =XQĒD"~Wqqman/DESCRIPTION0000644000177500001440000000075613062373042013425 0ustar deepayanusersPackage: qqman Title: Q-Q and Manhattan Plots for GWAS Data Version: 0.1.4 Author: Stephen Turner Maintainer: Stephen Turner Description: Create Q-Q and manhattan plots for GWAS data from PLINK results. Depends: R (>= 3.0.0), Imports: calibrate Suggests: knitr License: GPL-3 LazyData: true VignetteBuilder: knitr RoxygenNote: 6.0.1 NeedsCompilation: no Packaged: 2017-03-15 17:42:34 UTC; sdt5z Repository: CRAN Date/Publication: 2017-03-16 03:19:14 qqman/man/0000755000177500001440000000000013017117702012461 5ustar deepayanusersqqman/man/gwasResults.Rd0000644000177500001440000000040413062271057015275 0ustar deepayanusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/gwasResults-data.R \docType{data} \name{gwasResults} \alias{gwasResults} \title{Simulated GWAS results} \description{ Simulated GWAS results as obtained from \code{plink --assoc}. } qqman/man/qqman.Rd0000644000177500001440000000063713062271057014077 0ustar deepayanusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/package.R \docType{package} \name{qqman} \alias{qqman} \alias{qqman-package} \title{Create Q-Q and manhattan plots for GWAS data.} \description{ A package for creating Q-Q and manhattan plots for GWAS data. See the package vignette for details: \cr\cr \code{vignette("qqman")} } \author{ Stephen Turner <\url{http://stephenturner.us}> } qqman/man/manhattan.Rd0000644000177500001440000000457313062271057014740 0ustar deepayanusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/manhattan.R \name{manhattan} \alias{manhattan} \title{Creates a manhattan plot} \usage{ manhattan(x, chr = "CHR", bp = "BP", p = "P", snp = "SNP", col = c("gray10", "gray60"), chrlabs = NULL, suggestiveline = -log10(1e-05), genomewideline = -log10(5e-08), highlight = NULL, logp = TRUE, annotatePval = NULL, annotateTop = TRUE, ...) } \arguments{ \item{x}{A data.frame with columns "BP," "CHR," "P," and optionally, "SNP."} \item{chr}{A string denoting the column name for the chromosome. Defaults to PLINK's "CHR." Said column must be numeric. If you have X, Y, or MT chromosomes, be sure to renumber these 23, 24, 25, etc.} \item{bp}{A string denoting the column name for the chromosomal position. Defaults to PLINK's "BP." Said column must be numeric.} \item{p}{A string denoting the column name for the p-value. Defaults to PLINK's "P." Said column must be numeric.} \item{snp}{A string denoting the column name for the SNP name (rs number). Defaults to PLINK's "SNP." Said column should be a character.} \item{col}{A character vector indicating which colors to alternate.} \item{chrlabs}{A character vector equal to the number of chromosomes specifying the chromosome labels (e.g., \code{c(1:22, "X", "Y", "MT")}).} \item{suggestiveline}{Where to draw a "suggestive" line. Default -log10(1e-5). Set to FALSE to disable.} \item{genomewideline}{Where to draw a "genome-wide sigificant" line. Default -log10(5e-8). Set to FALSE to disable.} \item{highlight}{A character vector of SNPs in your dataset to highlight. These SNPs should all be in your dataset.} \item{logp}{If TRUE, the -log10 of the p-value is plotted. It isn't very useful to plot raw p-values, but plotting the raw value could be useful for other genome-wide plots, for example, peak heights, bayes factors, test statistics, other "scores," etc.} \item{annotatePval}{If set, SNPs below this p-value will be annotated on the plot.} \item{annotateTop}{If TRUE, only annotates the top hit on each chromosome that is below the annotatePval threshold.} \item{...}{Arguments passed on to other plot/points functions} } \value{ A manhattan plot. } \description{ Creates a manhattan plot from PLINK assoc output (or any data frame with chromosome, position, and p-value). } \examples{ manhattan(gwasResults) } \keyword{manhattan} \keyword{visualization} qqman/man/snpsOfInterest.Rd0000644000177500001440000000041013062271627015740 0ustar deepayanusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/snpsOfInterest-data.R \docType{data} \name{snpsOfInterest} \alias{snpsOfInterest} \title{snpsOfInterest} \description{ Example SNPs of interest from simulated \code{gwasResults} data. } qqman/man/qq.Rd0000644000177500001440000000071513062271057013400 0ustar deepayanusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/qq.R \name{qq} \alias{qq} \title{Creates a Q-Q plot} \usage{ qq(pvector, ...) } \arguments{ \item{pvector}{A numeric vector of p-values.} \item{...}{Other arguments passed to \code{plot()}} } \value{ A Q-Q plot. } \description{ Creates a quantile-quantile plot from p-values from a GWAS study. } \examples{ qq(gwasResults$P) } \keyword{qq} \keyword{qqplot} \keyword{visualization} qqman/tools/0000755000177500001440000000000013017117551013050 5ustar deepayanusersqqman/tools/qqman.gif0000644000177500001440000104632312411044403014655 0ustar deepayanusersGIF89ar7w! 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