pca {PCAtools} | R Documentation

## pca

### Description

Principal Components Analysis (PCA) is a very powerful technique that has wide applicability in data science, bioinformatics, and further afield. It was initially developed to analyse large volumes of data in order to tease out the differences/relationships between the logical entities being analysed. It extracts the fundamental structure of the data without the need to build any model to represent it. This 'summary' of the data is arrived at through a process of reduction that can transform the large number of variables into a lesser number that are uncorrelated, i.e., the principal components', whilst at the same time being capable of easy interpretation on the original data.

### Usage

    pca(
      mat,
      metadata = NULL,
      center = TRUE,
      scale = FALSE,
      removeVar = NULL)

### Arguments

`mat` | A data-matrix or data-frame containing numerical data only. REQUIRED.
`metadata` | A data-matrix or data-frame containing metadata. This will be stored in the resulting pca object. Strictly enforced that rownames(metadata) == colnames(mat). DEFAULT = NULL. OPTIONAL.
`center` | Center the data before performing PCA? Same as prcomp() 'center' parameter. DEFAULT = TRUE. OPTIONAL.
`scale` | Scale the data? Same as prcomp() 'scale' parameter. DEFAULT = FALSE. OPTIONAL.
`removeVar` | Remove this DEFAULT = NULL. OPTIONAL.

### Value

A `pca` object.

### Author(s)

Kevin Blighe <kevin@clinicalbioinformatics.co.uk>, Myles Lewis <myles.lewis@qmul.ac.uk>

### Examples

      options(scipen=10)
      options(digits=6)

      col <- 20
      row <- 20000
      mat1 <- matrix(
        rexp(col*row, rate = 0.1),
        ncol = col)
      rownames(mat1) <- paste0('gene', 1:nrow(mat1))
      colnames(mat1) <- paste0('sample', 1:ncol(mat1))

      mat2 <- matrix(
        rexp(col*row, rate = 0.1),
        ncol = col)
      rownames(mat2) <- paste0('gene', 1:nrow(mat2))
      colnames(mat2) <- paste0('sample', (ncol(mat1)+1):(ncol(mat1)+ncol(mat2)))

      mat <- cbind(mat1, mat2)

      metadata <- data.frame(row.names = colnames(mat))
      metadata$Group <- rep(NA, ncol(mat))
      metadata$Group[seq(1,40,2)] <- 'A'
      metadata$Group[seq(2,40,2)] <- 'B'
      metadata$CRP <- sample.int(100, size=ncol(mat), replace=TRUE)
      metadata$ESR <- sample.int(100, size=ncol(mat), replace=TRUE)

      p <- pca(mat, metadata = metadata, removeVar = 0.1)

      getComponents(p)

      getVars(p)

      getLoadings(p)

      screeplot(p)

      screeplot(p, hline = 80)

      biplot(p)

      biplot(p, colby = 'Group', shape = 'Group')

      biplot(p, colby = 'Group', colkey = c(A = 'forestgreen', B = 'gold'),
        legendPosition = 'right')

      biplot(p, colby = 'Group', colkey = c(A='forestgreen', B='gold'),
        shape = 'Group', shapekey = c(A=10, B=21), legendPosition = 'bottom')

      pairsplot(p, triangle = TRUE)

      plotloadings(p, drawConnectors=TRUE)

      eigencorplot(p, components = getComponents(p, 1:10),
        metavars = c('ESR', 'CRP'))

[Package PCAtools version 1.0.0 Index]