correlatePCs: Principal components (cor)relation with experimental...
In pcaExplorer: Interactive Visualization of RNA-seq Data Using a Principal Components Approach
Description
Usage
Arguments
Value
Examples
Description
Computes the significance of (cor)relations between PCA scores and the sample
experimental covariates, using Kruskal-Wallis test for categorial variables
and the cor.test based on Spearman's correlation for continuous
variables
Usage
1
correlatePCs(pcaobj, coldata, pcs = 1:4)
Arguments
pcaobj
A prcomp object
coldata
A data.frame object containing the experimental
covariates
pcs
A numeric vector, containing the corresponding PC number
Value
A data.frame object with computed p values for each covariate
and for each principal component
Examples
1
2
3
4
5
library(DESeq2)
dds <- makeExampleDESeqDataSet_multifac(betaSD_condition = 3, betaSD_tissue = 1)
rlt <- DESeq2::rlogTransformation(dds)
pcaobj <- prcomp(t(assay(rlt)))
correlatePCs(pcaobj, colData(dds))
pcaExplorer documentation built on Nov. 8, 2020, 5:29 p.m.
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Description Usage Arguments Value Examples
Description
Computes the significance of (cor)relations between PCA scores and the sample
experimental covariates, using Kruskal-Wallis test for categorial variables
and the cor.test based on Spearman's correlation for continuous
variables
Usage
1 | correlatePCs(pcaobj, coldata, pcs = 1:4)
|
Arguments
pcaobj |
A |
coldata |
A |
pcs |
A numeric vector, containing the corresponding PC number |
Value
A data.frame object with computed p values for each covariate
and for each principal component
Examples
1 2 3 4 5 | library(DESeq2)
dds <- makeExampleDESeqDataSet_multifac(betaSD_condition = 3, betaSD_tissue = 1)
rlt <- DESeq2::rlogTransformation(dds)
pcaobj <- prcomp(t(assay(rlt)))
correlatePCs(pcaobj, colData(dds))
|
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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