limmaquickpca2go: Functional interpretation of the principal components, based...

In pcaExplorer: Interactive Visualization of RNA-seq Data Using a Principal Components Approach

Description

Extracts the genes with the highest loadings for each principal component, and performs functional enrichment analysis on them using the simple and quick routine provided by the limma package

Usage

limmaquickpca2go(
  se,
  pca_ngenes = 10000,
  inputType = "ENSEMBL",
  organism = "Mm",
  loadings_ngenes = 500,
  background_genes = NULL,
  scale = FALSE,
  ...
)

Arguments

se	A DESeqTransform object, with data in assay(se), produced for example by either rlog or varianceStabilizingTransformation
pca_ngenes	Number of genes to use for the PCA
inputType	Input format type of the gene identifiers. Deafults to ENSEMBL, that then will be converted to ENTREZ ids. Can assume values such as ENTREZID,GENENAME or SYMBOL, like it is normally used with the select function of AnnotationDbi
organism	Character abbreviation for the species, using org.XX.eg.db for annotation
loadings_ngenes	Number of genes to extract the loadings (in each direction)
background_genes	Which genes to consider as background.
scale	Logical, defaults to FALSE, scale values for the PCA
...	Further parameters to be passed to the topGO routine

Value

A nested list object containing for each principal component the terms enriched in each direction. This object is to be thought in combination with the displaying feature of the main pcaExplorer function

Examples

library(airway)
library(DESeq2)
library(limma)
data(airway)
airway
dds_airway <- DESeqDataSet(airway, design = ~ cell + dex)
## Not run: 
rld_airway <- rlogTransformation(dds_airway)
goquick_airway <- limmaquickpca2go(rld_airway,
                                   pca_ngenes = 10000,
                                   inputType = "ENSEMBL",
                                   organism = "Hs")

## End(Not run)

pcaExplorer documentation built on Nov. 8, 2020, 5:29 p.m.