pca2go: Functional interpretation of the principal components

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

Functional interpretation of the principal components

Description

Extracts the genes with the highest loadings for each principal component, and performs functional enrichment analysis on them using routines and algorithms from the topGO package

Usage

pca2go(
  se,
  pca_ngenes = 10000,
  annotation = NULL,
  inputType = "geneSymbol",
  organism = "Mm",
  ensToGeneSymbol = FALSE,
  loadings_ngenes = 500,
  background_genes = NULL,
  scale = FALSE,
  return_ranked_gene_loadings = FALSE,
  annopkg = NULL,
  ...
)

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
annotation	A data.frame object, with row.names as gene identifiers (e.g. ENSEMBL ids) and a column, gene_name, containing e.g. HGNC-based gene symbols
inputType	Input format type of the gene identifiers. Will be used by the routines of topGO
organism	Character abbreviation for the species, using org.XX.eg.db for annotation
ensToGeneSymbol	Logical, whether to expect ENSEMBL gene identifiers, to convert to gene symbols with the annotation provided
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
return_ranked_gene_loadings	Logical, defaults to FALSE. If TRUE, simply returns a list containing the top ranked genes with hi loadings in each PC and in each direction
annopkg	String containing the name of the organism annotation package. Can be used to override the organism parameter, e.g. in case of alternative identifiers used in the annotation package (Arabidopsis with TAIR)
...	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")
data("airway", package = "airway")
airway
dds_airway <- DESeqDataSet(airway, design= ~ cell + dex)
## Not run: 
rld_airway <- rlogTransformation(dds_airway)
# constructing the annotation object
anno_df <- data.frame(gene_id = rownames(dds_airway),
                      stringsAsFactors = FALSE)
library("AnnotationDbi")
library("org.Hs.eg.db")
anno_df$gene_name <- mapIds(org.Hs.eg.db,
                            keys = anno_df$gene_id,
                            column = "SYMBOL",
                            keytype = "ENSEMBL",
                            multiVals = "first")
rownames(anno_df) <- anno_df$gene_id
bg_ids <- rownames(dds_airway)[rowSums(counts(dds_airway)) > 0]
library(topGO)
pca2go_airway <- pca2go(rld_airway,
                        annotation = anno_df,
                        organism = "Hs",
                        ensToGeneSymbol = TRUE,
                        background_genes = bg_ids)

## End(Not run)

federicomarini/pcaExplorer documentation built on Sept. 29, 2024, 9:31 p.m.