TCGAvisualize_PCA {TCGAbiolinks} | R Documentation |
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.
TCGAvisualize_PCA(dataFilt, dataDEGsFiltLevel, ntopgenes, group1, group2)
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 |
principal components analysis (PCA) plot of PC1 and PC2
# 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)