eigenDecompose: Eigendecompose gene expression matrix

In IMB-Computational-Genomics-Lab/scPred: scPred - Single cell prediction using singular value decomposition and machine learning classification

Description

Performs principal component analysis from a gene expression matrix. Data is centered and scaled by default.

Usage

eigenDecompose(expData, n = 10, pseudo = TRUE, returnData = TRUE,
  seed = 66)

Arguments

expData	A matrix object with genes as rows and cells as columns. Unique row names for genes must be provided
n	Number of principal components to be computed
pseudo	Whether to perform a log2(data + 1) transformation on expression data
returnData	Return training data?
seed	Numeric seed for computing the eigenvalues and eigenvectors using the Lanczos algorithm

Value

A scPred object with three or four filled slots

• svd: results from prcomp_irlba function

• expVar: explained variance by each principal component

• pseudo: TRUE if a pseudo-log2 transformation was performed

• trainData: if returnData is TRUE, the training data is returned

Author(s)

Jos<c3><a9> Alquicira Hern<c3><a1>ndez

Examples

# Eigendecompose gene expression matrix

# Simulate gene expression data for two groups

class1 <- matrix(rnbinom(10000, 1, 0.1),  ncol = 100)
class2 <- matrix(rnbinom(10000, 1, 0.15),  ncol = 100)

# Create gene expression matrix (rows = cells, colums = genes)

expTrain <- cbind(class1, class2)

# Eigendecompose gene expression matrix

object <- eigenDecompose(expTrain, n = 25)
plotEigen(object)

IMB-Computational-Genomics-Lab/scPred documentation built on Jan. 11, 2020, 7:37 a.m.