run.limma.matrix | R Documentation |
run limma, given an expression matrix
run.limma.matrix(
xdata,
xlabel,
batch = NULL,
out.prefix = NULL,
ncell.downsample = NULL,
T.fdr = 0.05,
T.logFC = 1,
T.expr = 0.3,
T.bin.useZ = T,
verbose = 0,
n.cores = NULL,
group = NULL,
gid.mapping = NULL,
do.voom = F,
rn.seed = 9999
)
xdata |
data frame or matrix; rows for genes and columns for samples |
xlabel |
factor; cluster label of the samples, with length equal to the number of columns in xdata |
batch |
factor; covariate. (default: NULL) |
out.prefix |
character; if not NULL, write the result to the file(s). (default: NULL) |
ncell.downsample |
integer; for each group, number of cells downsample to. (default: NULL) |
T.fdr |
numeric; threshold of the adjusted p value of moderated t-test (default: 0.05) |
T.logFC |
numeric; threshold of the absoute diff (default: 1) |
T.expr |
numeric; threshold for binarizing exprs (default: 0.3) |
T.bin.useZ |
logical; wheter use the z-score version of assay.namme for binarizing exprs (default: T) |
verbose |
integer; verbose (default: 0) |
n.cores |
integer; number of cores used, if NULL it will be determined automatically (default: NULL) |
group |
character; group of interest, if NULL the last group will be used (default: NULL) |
gid.mapping |
named character; gene id to gene symbol mapping. (default: NULL) |
do.voom |
logical; perform voom transfromation (default: FALSE) |
rn.seed |
integer; random number seed (default: 9999) |
diffeerentially expressed genes dectection using limma
a matrix with dimention as input ( samples in rows and variables in columns)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.