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.
Type
Publication
In Journal of Open Source Software
Nima Hejazi
Assistant Professor of Biostatistics
My research lies at the intersection of causal inference and machine learning, developing flexible methodology for statistical inference tailored to modern experiments and observational studies in the biomedical and public health sciences.