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DEDS (version 1.46.0)

qqnorm-methods: Normal Q-Q Plot for DEDS Objects

Description

The function qqnorm.DEDS produces normal Quantile-Quantile plots of statistics for DEDS-class objects. The points corresponding to genes with DEDS q- or adjusted p-values less than a user defined threshold are highlighted.

Usage

"qqnorm"(y, subset=c(1:nrow(y$stats)),
xlab = "Quantiles of standard normal", thresh = 0.05, col = palette(), pch, ...)

Arguments

y
An object of DEDS, produced by deds.stat.linkC or deds.stat.
subset
A numeric vector indicating the subset of points to be plotted.
xlab
A title for the x axis
thresh
A numeric variable specifying the threshold of significance in differential expression (DE) for q- or p-values of the DEDS object.
col
A specification for the colors to be used for plotting. It should have a length bigger than two. The first is used for points with q- or adjusted p-values smaller than the specified threshold (group I) and the second for points with q- or adjusted p-values bigger than the threshold (group II).
pch
A specification for the type of points to be used for plotting. It should have a length bigger than two. The first parameter is used for group I genes, and the second for group II genes.
...
Extra parameters for plotting.

Details

The function qqnorm.DEDS implements a S3 method of qqnorm for DEDS. The DEDS class is a simple list-based class to store DEDS results and qqnorm.DEDS is used for a DEDS object that is created by functions deds.stat, deds.stat.linkC. The list contains a "stat" component, which stores statistics from various statistical tests. The function qqnorm.DEDS extracts the "stat" component and produces a normal QQ plot for each type of statistics. qqnorm.DEDS as a default highlights points (corresponding to genes) with DEDS adjusted p- or q-values less than a user defined threshold.

For DEDS objects that are created by the function deds.pval, the "stat" matrix consists of unadjusted p-values from different statistical models. For graphical display of these p values, the user can use hist.DEDS and pairs.DEDS.

See Also

deds.stat, deds.pval, deds.stat.linkC, hist.DEDS, qqnorm.DEDS

Examples

Run this code
X <- matrix(rnorm(1000,0,0.5), nc=10)
L <- rep(0:1,c(5,5))

# genes 1-10 are differentially expressed
X[1:10,6:10]<-X[1:10,6:10]+1
# DEDS summarizing t, fc and sam
d <- deds.stat.linkC(X, L, B=200)

# qqnorm for t, fc and sam
qqnorm(d)
# change points color
qqnorm(d, col=c(2,3))
# change points type
qqnorm(d, pch=c(1,2))

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