diffExp.
polarPlot(pValues, chromosomeMapping, colors = c("red", "green", "blue", "yellow", "orange", "purple", "tan", "cyan", "gray60", "black"), save = FALSE, verbose = TRUE)data.frame containing p-values for different contrasts in different
columns. Column names are used as contrast names. Maximum number of columns
allowed are ten.
data.frame or similar containing the chromosome mapping. See
details below.
FALSE.
TRUE.
diffExp but can also be used
separately by the user.
The argument chromosomeMapping should be either a data.frame
or a tab delimited text file and include the columns chromosome with the
chromosome name and chromosome location containing the starting position
of each gene. A - sign can be used to denote the antisense strand but this will
be disregarded while plotting. The rownames should be probe IDs or, if
using a text file, the first column with a column header should contain the
probe IDs. If relying on an ArrayData object (called arrayData)
and containing an annotation field, the chromosomeMapping can be
set to arrayData$annotation[,c(2,3)] (see the example below).
The Polar plot sorts the genes according to chromosomal location, for each
chromosome starting with unknown positions followed by increasing number in
the chromosome location column. Genes which do not map to any chromosome
are listed as U for unknown. The radial lines in the Polar plot are -log10 scaled
p-values, so that a longer line means a smaller p-value. This gives an overview
of the magnitude of differential expression for each contrast.
diffExp, radial.plot
# Get path to example data and setup files:
dataPath <- system.file("extdata", package="piano")
# Load normalized data:
myArrayData <- loadMAdata(datadir=dataPath, dataNorm="norm_data.txt.gz", platform="yeast2")
# Perform differential expression analysis:
pfc <- diffExp(myArrayData, plot=FALSE,
contrasts=c("aerobic_Clim - anaerobic_Clim",
"aerobic_Nlim - anaerobic_Nlim"))
# Get chromosome mapping from myArrayData:
chrMap <- myArrayData$annotation[,c(2,3)]
# Get p-values from pfc
pval <- pfc$pValues
# Draw the polar plot:
polarPlot(pval, chromosomeMapping=chrMap)
Run the code above in your browser using DataLab