scPCA: A toolbox for sparse contrastive principal component analysis in R

Abstract

scPCA is a toolbox for sparse contrastive principal component analysis of high-dimensional biological data. scPCA combines the stability and interpretability of sparse PCA with contrastive PCA’s ability to disentangle biological signal from techical noise through the use of control data. cPCA is also implemented and extended.

Publication
In Journal of Open Source Software
Nima Hejazi
Nima Hejazi
PhD Candidate in Biostatistics

My research interests lie at the intersection of causal inference and machine learning, especially as applied to the statistical analysis of complex data from observational studies and experiments in the biomedical and health sciences.