TCGAvisualize_PCA: Principal components analysis (PCA) plot

In TCGAbiolinks: TCGAbiolinks: An R/Bioconductor package for integrative analysis with GDC data

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, group1, group2)

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
group1	a string containing the barcode list of the samples in in control group
group2	a string containing the barcode list of the samples in in disease group the name of the group

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
    # selection of normal samples "NT"
    group1 <- TCGAquery_SampleTypes(colnames(dataFilt), typesample = c("NT"))
    # selection of normal samples "TP"
    group2 <- TCGAquery_SampleTypes(colnames(dataFilt), typesample = c("TP"))
pca <- TCGAvisualize_PCA(dataFilt,dataDEGsFiltLevel, ntopgenes = 200, group1, group2)

TCGAbiolinks documentation built on Nov. 8, 2020, 5:37 p.m.