pca: Principal Component Analysis (PCA) is a very powerful...

In PCAtools: PCAtools: Everything Principal Components Analysis

Description

Principal Component 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. PCAtools provides functions for data exploration via PCA, and allows the user to generate publication-ready figures. PCA is performed via BiocSingular - users can also identify optimal number of principal component via different metrics, such as elbow method and Horn's parallel analysis, which has relevance for data reduction in single-cell RNA-seq (scRNA-seq) and high dimensional mass cytometry data.

Usage

pca(
  mat,
  metadata = NULL,
  center = TRUE,
  scale = FALSE,
  rank = NULL,
  removeVar = NULL,
  transposed = FALSE,
  BSPARAM = ExactParam()
)

Arguments

mat	A data-matrix or data-frame containing numerical data only. Variables are expected to be in the rows and samples in the columns by default.
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).
center	Center the data before performing PCA? Same as prcomp() 'center' parameter.
scale	Scale the data? Same as prcomp() 'scale' parameter.
rank	An integer scalar specifying the number of PCs to retain. OPTIONAL for an exact SVD, whereby it defaults to all PCs. Otherwise REQUIRED for approximate SVD methods.
removeVar	Remove this % of variables based on low variance.
transposed	Is mat transposed? DEFAULT = FALSE. If set to TRUE, samples are in the rows and variables are in the columns.
BSPARAM	A BiocSingularParam object specifying the algorithm to use for the SVD. Defaults to an exact SVD.

Details

Principal Component 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. PCAtools provides functions for data exploration via PCA, and allows the user to generate publication-ready figures. PCA is performed via BiocSingular - users can also identify optimal number of principal component via different metrics, such as elbow method and Horn's parallel analysis, which has relevance for data reduction in single-cell RNA-seq (scRNA-seq) and high dimensional mass cytometry data.

Value

A pca object, containing:

• rotated, a data frame of the rotated data, i.e., the centred and scaled ( if either or both are requested) input data multiplied by the variable loadings ('loadings'). This is the same as the 'x' variable returned by prcomp().

• loadings, a data frame of variable loadings ('rotation' variable returned by prcomp()).

• variance, a numeric vector of the explained variation for each principal component.

• sdev, the standard deviations of the principal components.

• metadata, the original metadata

• xvars, a character vector of rownames from the input data.

• yvars, a character vector of colnames from the input data.

• components, a character vector of principal component / eigenvector names.

Author(s)

Kevin Blighe <[e-mail]>

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'))

PCAtools documentation built on Nov. 8, 2020, 8:17 p.m.