TCGAvisualize_PCA {TCGAbiolinks} | R Documentation

## 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, 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)

[Package TCGAbiolinks version 2.22.4 Index]