voom.controlPseudocount: limma + voom
In jhsiao999/ashbun: Methods and evaluation tools for single cell RNA-seq count data
Description
Usage
Arguments
Value
Author(s)
Description
Implement limma with voom and apply the empirical Bayes method
in limma. This is a modified version of voom which allows to specify pseudocount and
pseudo library size.
Hence, voom: y <- t(log2(t(counts + pseudocount)/(lib.size + pseudo_libsizes) * 1e+06)).
Usage
1
2
voom.controlPseudocount(counts, design, pseudocount = 0.5,
pseudo_libsizes = 1, span = 0.5)
Arguments
counts
gene by sample expression count matrix.
design
design matrix, generated by R function model.matrix()
pseudocount
default .5
pseudo_libsizes
default 1.
Value
w Weights of dimension G by N.
Author(s)
Chiaowen Joyce Hsiao
jhsiao999/ashbun documentation built on May 8, 2019, 11:17 p.m.
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Description Usage Arguments Value Author(s)
Description
Implement limma with voom and apply the empirical Bayes method in limma. This is a modified version of voom which allows to specify pseudocount and pseudo library size. Hence, voom: y <- t(log2(t(counts + pseudocount)/(lib.size + pseudo_libsizes) * 1e+06)).
Usage
1 2 | voom.controlPseudocount(counts, design, pseudocount = 0.5,
pseudo_libsizes = 1, span = 0.5)
|
Arguments
counts |
gene by sample expression count matrix. |
design |
design matrix, generated by R function model.matrix() |
pseudocount |
default .5 |
pseudo_libsizes |
default 1. |
Value
w Weights of dimension G by N.
Author(s)
Chiaowen Joyce Hsiao
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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