PVCA: A function for principal variance component analysis

In madhulika-EBI/Batchevaluation: Toolkit for batch correction

A function for principal variance component analysis

Description

The function is written based on the 'pvcaBatchAssess' function of the PVCA R package and slightly changed to make it more efficient and flexible for sequencing read counts data. From https://github.com/dleelab/pvca #' @import lme4

Usage

PVCA(counts, meta, threshold, inter)

Arguments

counts	The Normalized(e.g. TMM)/ log-transformed reads count matrix from sequencing data (row:gene/feature, col:sample)
meta	The Meta data matrix containing predictor variables (row:sample, col:predictor)
threshold	The proportion of the variation in read counts explained by top k PCs. This value determines the number of top PCs to be used in pvca.
inter	TRUE/FALSE - include/do not include pairwise interactions of predictors

Value

std.prop.val The vector of proportions of variation explained by each predictor.

madhulika-EBI/Batchevaluation documentation built on Jan. 27, 2023, 5:27 p.m.