Author: Donghyung Lee
Note: The function is written based on the 'pvcaBatchAssess' function of the PVCA R package and slightly changed to make it more efficient and flexible. (http://watson.nci.nih.gov/bioc_mirror/packages/release/bioc/manuals/pvca/man/pvca.pdf)
Description: Function for Principal Variance Component Analysis
Input:
1. counts: normalized(e.g. TMM) or log-transformed reads count matrix from sequencing data (row:gene/feature, col:sample)
2. meta: meta data matrix containing predictor variables (row:sample, col:predictor)
3. threshold: proportion of the variation in read counts explained by k top PCs. This value determines the number of PCs to be used in pvca.
4. inter: TRUE/FALSE - include/do not include pairwise interactions of predictors
Output:
a vector of proportions of the variation in read counts data explained by each predictor.