plotPCcorrs: Plot significance of (cor)relations of covariates VS...
In pcaExplorer: Interactive Visualization of RNA-seq Data Using a Principal Components Approach
Description
Usage
Arguments
Value
Examples
Description
Plots the significance of the (cor)relation of each covariate vs a principal component
Usage
1
plotPCcorrs(pccorrs, pc = 1, logp = TRUE)
Arguments
pccorrs
A data.frame object generated by correlatePCs
pc
An integer number, corresponding to the principal component of
interest
logp
Logical, defaults to TRUE, displays the -log10 of
the pvalue instead of the p value itself
Value
A base plot object
Examples
1
2
3
4
5
6
library(DESeq2)
dds <- makeExampleDESeqDataSet_multifac(betaSD_condition = 3, betaSD_tissue = 1)
rlt <- rlogTransformation(dds)
pcaobj <- prcomp(t(assay(rlt)))
res <- correlatePCs(pcaobj, colData(dds))
plotPCcorrs(res)
pcaExplorer documentation built on Nov. 8, 2020, 5:29 p.m.
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Description Usage Arguments Value Examples
Description
Plots the significance of the (cor)relation of each covariate vs a principal component
Usage
1 | plotPCcorrs(pccorrs, pc = 1, logp = TRUE)
|
Arguments
pccorrs |
A |
pc |
An integer number, corresponding to the principal component of interest |
logp |
Logical, defaults to |
Value
A base plot object
Examples
1 2 3 4 5 6 | library(DESeq2)
dds <- makeExampleDESeqDataSet_multifac(betaSD_condition = 3, betaSD_tissue = 1)
rlt <- rlogTransformation(dds)
pcaobj <- prcomp(t(assay(rlt)))
res <- correlatePCs(pcaobj, colData(dds))
plotPCcorrs(res)
|
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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