pairs-DEDS
produces pairs plots of statistics or p
values for DEDS-class
objects.
"pairs"(x, subset=c(1:nrow(x$stats)), labels =
colnames(x$stats[,-1]), logit = FALSE,
diagonal = c("qqnorm", "boxplot", "density", "histogram", "none"),
lower = c("cor", "none"), groups.by.deds = TRUE, thresh = 0.05, reg.line
= NULL, smooth = FALSE, line.by.group = FALSE, diag.by.group = TRUE, lower.by.group =
FALSE, col = palette(), pch = 1:n.groups, lwd = 1, legend.plot =
length(levels(groups)) > 1, ...)
DEDS
.TRUE
the variables are
logged, useful when plotting p values.
lower="cor" : |
absolute correlation will be put on the lower panel |
TRUE
, points will
be separated into groups according to their magnitude of q- or p-values
by DEDS. thresh
<1, it="" specifies="" the="" threshold="" of="" significance="" in="" differential="" expression="" (de)="" for="" q-="" or="" p-values="" deds="" object;="" default is="" set="" at="" 0.05.="" if="" thresh>1, it specifies the number of top DE genes to be
highlighted. 1,>
reg.line=lm
,
linear regression line will be plotted; If reg.line=NULL
, no
regression line will be plotted in the scatter plot.smooth=TRUE
, a
lowess
line will be applied.lower.by.group=TRUE
and lower="cor"
, correlation
coefficients will be calculated and printed separated according to
groups in the lower panels.par
.par
.par
.pairs.DEDS
implements a S3 method of
pairs
for DEDS
. The DEDS
class is a simple list-based class to store DEDS results and it is
usually created by functions deds.pval
,
deds.stat
, deds.stat.linkC
. The list
contains a "stat" component, which stores statistics or p values from
various statistical tests. The function pairs.DEDS
extracts the
"stat" component and produces a matrix of scatterplot. pairs.DEDS
as a default highlights points (corresponding to
genes) with adjusted p- or q-values less than a user defined
threshold. The user can select among a series of options a plot for
the diagonal panel; as a default, it produces a qqnorm
for each column in the "stat" matrix. Both the diagonal and lower
panels can be stratified by specifying the diag.by.group
or
lower.by.group
arguments.
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
d <- deds.stat.linkC(X, L, B=200)
# pairs plot
pairs(d)
# plot regression line
pairs(d, reg.line=lm, lwd=2)
# histogram in the diagonal panel
pairs(d, diagonal="hist")
# boxplot on the diagonal panel and stratified
pairs(d, diagonal="boxplot", diag.by.group=TRUE)
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