qqnorm-methods {DEDS} | R Documentation |
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.
## S3 method for class 'DEDS': qqnorm(y, subset=c(1:nrow(y$stats)), xlab = "Quantiles of standard normal", thresh = 0.05, col = palette(), pch, ...)
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 specificatio 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 threshould (group I) and the second for points with q- or adjusted p-values bigger than the threshould (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. |
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
.
Yuanyuan Xiao, yxiao@itsa.ucsf.edu,
Jean Yee Hwa Yang, jean@biostat.ucsf.edu.
deds.stat
, deds.pval
,
deds.stat.linkC
, hist.DEDS
,
qqnorm.DEDS
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))