qqman/ 0000755 0001762 0000144 00000000000 12411075505 011367 5 ustar ligges users qqman/inst/ 0000755 0001762 0000144 00000000000 12350120072 012334 5 ustar ligges users qqman/inst/doc/ 0000755 0001762 0000144 00000000000 12411042053 013101 5 ustar ligges users qqman/inst/doc/qqman.html 0000644 0001762 0000144 00004735401 12411044123 015122 0 ustar ligges users
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 0.9148 0.10698
2 rs2 1 2 0.9371 0.07895
3 rs3 1 3 0.2861 1.06663
4 rs4 1 4 0.8304 0.21413
5 rs5 1 5 0.6417 0.46526
6 rs6 1 6 0.5191 0.64474
tail(gwasResults)
SNP CHR BP P zscore
16465 rs16465 22 530 0.5644 0.5764
16466 rs16466 22 531 0.1383 1.4822
16467 rs16467 22 532 0.3937 0.8529
16468 rs16468 22 533 0.1779 1.3473
16469 rs16469 22 534 0.2393 1.1767
16470 rs16470 22 535 0.2630 1.1192
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)
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"))
Now, let's look at a single chromosome:
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.
str(snpsOfInterest)
chr [1:100] "rs3001" "rs3002" "rs3003" "rs3004" "rs3005" ...
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):
manhattan(subset(gwasResults, CHR == 3), highlight = snpsOfInterest, xlim = c(200,
500), main = "Chr 3")
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 0.9148 0.10698
2 rs2 1 2 0.9371 0.07895
3 rs3 1 3 0.2861 1.06663
4 rs4 1 4 0.8304 0.21413
5 rs5 1 5 0.6417 0.46526
6 rs6 1 6 0.5191 0.64474
# 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.
qq(gwasResults$P)
We can otionally supply many other graphical parameters.
qq(gwasResults$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)
qqman/inst/doc/qqman.Rmd 0000644 0001762 0000144 00000012067 12411044123 014670 0 ustar ligges users ---
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")
```
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.R 0000644 0001762 0000144 00000005563 12411044123 014352 0 ustar ligges users ## ----, 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$CHR))
## ------------------------------------------------------------------------
manhattan(gwasResults)
## ------------------------------------------------------------------------
manhattan(gwasResults, main="Manhattan Plot", ylim=c(0,10), cex=0.6, cex.axis=0.9, col=c("blue4", "orange3"), suggestiveline=F, genomewideline=F, chrlabs=c(1:20, "P", "Q"))
## ------------------------------------------------------------------------
manhattan(subset(gwasResults, CHR==1))
## ------------------------------------------------------------------------
str(snpsOfInterest)
manhattan(gwasResults, highlight=snpsOfInterest)
## ------------------------------------------------------------------------
manhattan(subset(gwasResults, CHR==3), highlight=snpsOfInterest, xlim=c(200, 500), main="Chr 3")
## ------------------------------------------------------------------------
# Add test statistics
gwasResults <- transform(gwasResults, zscore=qnorm(P/2, lower.tail=FALSE))
head(gwasResults)
# Make the new plot
manhattan(gwasResults, p="zscore", logp=FALSE, ylab="Z-score", genomewideline=FALSE, suggestiveline=FALSE, main="Manhattan plot of Z-scores")
## ------------------------------------------------------------------------
qq(gwasResults$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/NAMESPACE 0000644 0001762 0000144 00000000123 12341411011 012566 0 ustar ligges users # Generated by roxygen2 (4.0.1): do not edit by hand
export(manhattan)
export(qq)
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o = -log10(sort(pvector,decreasing=FALSE))
e = -log10( ppoints(length(pvector) ))
# # The old way
# plot(e, o, pch=20,
# xlab=expression(Expected~~-log[10](italic(p))),
# ylab=expression(Observed~~-log[10](italic(p))),
# ...)
# The new way to initialize the plot.
## See http://stackoverflow.com/q/23922130/654296
## First, define your default arguments
def_args <- list(pch=20, xlim=c(0, max(e)), ylim=c(0, max(o)),
xlab=expression(Expected~~-log[10](italic(p))),
ylab=expression(Observed~~-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.
tryCatch(do.call("plot", c(list(x=e, y=o), def_args[!names(def_args) %in% names(dotargs)], dotargs)), warn=stop)
# Add diagonal
abline(0,1,col="red")
}
qqman/R/manhattan.R 0000644 0001762 0000144 00000020052 12411044012 013652 0 ustar ligges users #' Creates a manhattan plot
#'
#' Creates a manhattan plot from PLINK assoc output (or any data frame with
#' chromosome, position, and p-value).
#'
#' @param x A data.frame with columns "BP," "CHR," "P," and optionally, "SNP."
#' @param 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.
#' @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 ... Arguments passed on to other plot/points functions
#'
#' @return A manhattan plot.
#'
#' @keywords visualization manhattan
#'
#' @examples
#' manhattan(gwasResults)
#'
#' @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, ...) {
# 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
options(scipen=999)
d$pos=d$BP/1e6
ticks=floor(length(d$pos))/2+1
xlabel = paste('Chromosome',unique(d$CHR),'position(Mb)')
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$CHR == i,]$pos) + max(d[d$CHR == i,]$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$SNP %in% highlight), ]
with(d.highlight, points(pos, logp, col="green3", pch=20, ...))
}
}
qqman/R/package.R 0000644 0001762 0000144 00000000447 12335152166 013317 0 ustar ligges users #' Create Q-Q and manhattan plots for GWAS data.
#'
#' A package for creating Q-Q and manhattan plots for GWAS data. See the package
#' vignette for details: \cr\cr
#' \code{vignette("qqman")}
#'
#' @name qqman
#' @docType package
#' @author Stephen Turner <\url{http://stephenturner.us}>
NULL qqman/R/zzz.R 0000644 0001762 0000144 00000000640 12364505140 012550 0 ustar ligges users .onAttach <- function(...){
packageStartupMessage()
packageStartupMessage("For example usage please run: vignette('qqman')")
packageStartupMessage()
packageStartupMessage("Citation appreciated but not required:")
packageStartupMessage("Turner, S.D. qqman: an R package for visualizing GWAS results using Q-Q and manhattan plots. biorXiv DOI: 10.1101/005165 (2014).")
packageStartupMessage()
} qqman/vignettes/ 0000755 0001762 0000144 00000000000 12411044266 013377 5 ustar ligges users qqman/vignettes/qqman.Rmd 0000644 0001762 0000144 00000012067 12370515235 015171 0 ustar ligges users ---
title: "Intro to the qqman package"
author: "Stephen D. Turner"
date: '`r Sys.Date()`'
output:
html_document:
toc: yes
---
```{r, include=FALSE}
library(qqman)
library(knitr)
opts_chunk$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")
```
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$P, main="Q-Q plot of GWAS p-values",
xlim=c(0,7), ylim=c(0,12), pch=18, col="blue4", cex=1.5, las=1)
``` qqman/README.md 0000644 0001762 0000144 00000003357 12364505372 012665 0 ustar ligges users # qqman: An R package for creating Q-Q and manhattan plots from GWAS results.
## Citation
If you'd like to cite qqman (appreciated but not required), please cite the pre-print below:
Turner, S.D. qqman: an R package for visualizing GWAS results using Q-Q and manhattan plots. [*biorXiv* DOI: 10.1101/005165](http://biorxiv.org/content/early/2014/05/14/005165).
## Installation
Install the stable release from CRAN:
```coffee
install.packages("qqman")
```
Or install directly from github using devtools
```coffee
library(devtools)
install_github("stephenturner/qqman")
```
Or install the most recent development release with devtools (note, there be dragons here):
```coffee
library(devtools)
install_github("stephenturner/qqman", ref="dev")
```
Load the package each time you use it:
```coffee
library(qqman)
```
## Usage
See the [online package vignette](http://cran.r-project.org/web/packages/qqman/vignettes/qqman.html) for more examples:
```coffee
vignette("manhattan")
```
Take a look at the built-in data:
```coffee
head(gwasResults)
```
Basic manhattan plot using built-in data:
```coffee
manhattan(gwasResults)
```
Basic Q-Q plot using built-in data:
```coffee
qq(gwasResults$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/MD5 0000644 0001762 0000144 00000002041 12411075505 011674 0 ustar ligges users 878f0a46bac21b0a8714378777493948 *DESCRIPTION
e8a3b67a1cfc5de2c87e2c05509ccb86 *NAMESPACE
4703c59c0422edc311c09f34a79425c2 *R/gwasResults-data.R
506bb6bd378ce87b44d1b452c47dac6a *R/manhattan.R
6f2bc34e71e66aef115d0a9dff9c2c78 *R/package.R
32fa6e2f67e18b0f33654b21de1624a6 *R/qq.R
1d2a7fe82e8a5143d495b8f2f7800eca *R/snpsOfInterest-data.R
4e7b578263eee2e20de8b161e4c08af4 *R/zzz.R
7d28ad0c1131dbad19b657df61645cda *README.md
86002107ab6d65a20ca6f0bbc7db8d34 *build/vignette.rds
ff43bc38623980d5b64e423362075c10 *data/gwasResults.RData
a10481df0b1192bc3f599e14414e0a55 *data/snpsOfInterest.RData
9cb0830cb6ce29dbd98ebbb76d770cab *inst/doc/qqman.R
47c06f8e3c96a96fca34f31fad80fd6d *inst/doc/qqman.Rmd
c608b972e60a7ce5b88f47c7ae9055cb *inst/doc/qqman.html
d1bb3943d85241abb665c6235c55825a *man/gwasResults.Rd
516c1f05ddcecd781b1b1e01e16ad68d *man/manhattan.Rd
7b64ebaab0b22df9b3c146614083689b *man/qq.Rd
e7729862ac4cb3540ee65a21600538a4 *man/qqman.Rd
efcb6525c2360e9fbf5acfa005b5cdff *man/snpsOfInterest.Rd
47c06f8e3c96a96fca34f31fad80fd6d *vignettes/qqman.Rmd
qqman/build/ 0000755 0001762 0000144 00000000000 12411044266 012466 5 ustar ligges users qqman/build/vignette.rds 0000644 0001762 0000144 00000000323 12411044266 015023 0 ustar ligges users b```b`f@&0`b fd`aҜyzA)hRy%E
%@ VP bDFIn ;pi `aBRƚZ% 5/$~hZ8S+`zPAհe
,s\ܠL t7`~r= xA $Gs=ʕXVr7 G+" qqman/DESCRIPTION 0000644 0001762 0000144 00000000654 12411075505 013102 0 ustar ligges users Package: qqman
Title: Q-Q and manhattan plots for GWAS data
Version: 0.1.2
Author: Stephen Turner
Maintainer: Stephen Turner
Description: Q-Q and manhattan plots for GWAS data
Depends: R (>= 3.0.0)
Suggests: knitr
License: GPL-3
LazyData: true
VignetteBuilder: knitr
Packaged: 2014-09-25 16:54:14 UTC; sdt5z
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-09-25 22:29:57
qqman/man/ 0000755 0001762 0000144 00000000000 12364504641 012147 5 ustar ligges users qqman/man/gwasResults.Rd 0000644 0001762 0000144 00000000331 12341411011 014735 0 ustar ligges users % Generated by roxygen2 (4.0.1): do not edit by hand
\docType{data}
\name{gwasResults}
\alias{gwasResults}
\title{Simulated GWAS results}
\description{
Simulated GWAS results as obtained from \code{plink --assoc}.
}
qqman/man/qqman.Rd 0000644 0001762 0000144 00000000575 12341411011 013541 0 ustar ligges users % Generated by roxygen2 (4.0.1): do not edit by hand
\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.Rd 0000644 0001762 0000144 00000004123 12364504641 014411 0 ustar ligges users % Generated by roxygen2 (4.0.1): do not edit by hand
\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, ...)
}
\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{...}{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.Rd 0000644 0001762 0000144 00000000250 12341411011 015400 0 ustar ligges users % Generated by roxygen2 (4.0.1): do not edit by hand
\docType{data}
\name{snpsOfInterest}
\alias{snpsOfInterest}
\title{snpsOfInterest}
\description{
snpsOfInterest
}
qqman/man/qq.Rd 0000644 0001762 0000144 00000000657 12341411011 013046 0 ustar ligges users % Generated by roxygen2 (4.0.1): do not edit by hand
\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}