TCGAbiolinks (version 1.2.5)

TCGAvisualize_PCA: Principal components analysis (PCA) plot

Description

TCGAvisualize_PCA performs a principal components analysis (PCA) on the given data matrix and returns the results as an object of class prcomp, and shows results in PCA level.

Usage

TCGAvisualize_PCA(dataFilt, dataDEGsFiltLevel, ntopgenes)

Arguments

dataFilt

A filtered dataframe or numeric matrix where each row represents a gene, each column represents a sample from function TCGAanalyze_Filtering

dataDEGsFiltLevel

table with DEGs, log Fold Change (FC), false discovery rate (FDR), the gene expression level, etc, from function TCGAanalyze_LevelTab.

ntopgenes

number of DEGs genes to plot in PCA

Value

principal components analysis (PCA) plot of PC1 and PC2

Examples

# normalization of genes
dataNorm <- TCGAbiolinks::TCGAanalyze_Normalization(tabDF = dataBRCA, geneInfo = geneInfo,
method = "geneLength")
# quantile filter of genes
dataFilt <- TCGAanalyze_Filtering(tabDF = dataBRCA, method = "quantile", qnt.cut =  0.25)
# Principal Component Analysis plot for ntop selected DEGs
pca <- TCGAvisualize_PCA(dataFilt,dataDEGsFiltLevel, ntopgenes = 200)
if (!(is.null(dev.list()["RStudioGD"]))){dev.off()}