voom_polar {volcano3D} | R Documentation |
Convert RNA-Seq count data to a volcano3d object using 'limma voom'
Description
This function is used instead of polar_coords
if you have raw
RNA-Seq count data. The function takes a design formula, metadata and raw
RNA-Seq count data and uses 'limma voom' to analyse the data. The results are
converted to a 'volc3d' object ready for plotting a 3d volcano plot or polar
plot.
Usage
voom_polar(
formula,
metadata,
counts,
pcutoff = 0.05,
padj.method = "BH",
filter_pairwise = TRUE,
...
)
Arguments
formula |
Design formula which must be of the form |
metadata |
Matrix or dataframe containing metadata as referenced by
|
counts |
Matrix containing raw gene expression count data |
pcutoff |
Cut-off for p-value significance |
padj.method |
Can be any method available in |
filter_pairwise |
Logical whether adjusted p-value pairwise statistical tests are only conducted on genes which reach significant adjusted p-value cut-off on the group likelihood ratio test |
... |
Optional arguments passed to |
Details
Statistical results for the group and pairwise comparisons are calculated
using the 'limma voom' pipeline and the results passed to
polar_coords
to generate a 'volc3d' object ready for plotting a
3d volcano plot or polar plot.
Value
Calls polar_coords
to return an S4 'volc3d' object
See Also
polar_coords
, deseq_polar
,
voom
in the limma package
Examples
if (requireNamespace("limma", quietly = TRUE) &
requireNamespace("edgeR", quietly = TRUE)) {
library(limma)
library(edgeR)
counts <- matrix(rnbinom(n=1500, mu=100, size=1/0.5), ncol=15)
cond <- factor(rep(1:3, each=5), labels = c('A', 'B', 'C'))
cond <- data.frame(cond)
polar <- voom_polar(~0 + cond, cond, counts)
volcano3D(polar)
radial_ggplot(polar)
}