Detection of correlated hidden factors from single cell transcriptomes using Iteratively Adjusted-SVA (IA-SVA).

ABSTRACT: Single cell RNA-sequencing (scRNA-seq) precisely characterizes gene expression levels and dissects variation in expression associated with the state (technical or biological) and the type of the cell, which is averaged out in bulk measurements. Multiple and correlated sources contribute to gene expression variation in single cells, which makes their estimation difficult with the existing methods developed for batch correction (e.g., surrogate variable analysis (SVA)) that estimate orthogonal transformations of these sources. We developed iteratively adjusted surrogate variable analysis (IA-SVA) that can estimate hidden factors even when they are correlated with other sources of variation by identifying a set of genes associated with each hidden factor in an iterative manner. Analysis of sc

SUBMITTER: Lee D 

PROVIDER: S-EPMC6242813 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature