PT  - JOURNAL ARTICLE
AU  - Donghyung Lee
AU  - Anthony Cheng
AU  - Duygu Ucar
TI  - A robust statistical framework to detect multiple sources of hidden variation in single-cell transcriptomes
AID  - 10.1101/151217
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 151217
4099  - http://biorxiv.org/content/early/2017/06/18/151217.short
4100  - http://biorxiv.org/content/early/2017/06/18/151217.full
AB  - Single-cell RNA-Sequencing data often harbor variation from multiple correlated sources, which cannot be accurately detected by existing methods. Here we present a novel and robust statistical framework that can capture correlated sources of variation in an iterative fashion: iteratively adjusted surrogate variable analysis (IA-SVA). We demonstrate that IA-SVA accurately captures hidden variation in single cell RNA-Sequencing data arising from cell contamination, cell-cycle stage, and differences in cell types along with the marker genes associated with the source.