RT Journal Article
SR Electronic
T1 A gene filter for comparative analysis of single-cell RNA-sequencing trajectory datasets
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 637488
DO 10.1101/637488
A1 Wang, Yutong
A1 Thong, Tasha
A1 Saligrama, Venkatesh
A1 Colacino, Justin
A1 Balzano, Laura
A1 Scott, Clayton
YR 2019
UL http://biorxiv.org/content/early/2019/05/17/637488.abstract
AB Unsupervised feature selection, or gene filtering, is a common preprocessing step to reduce the dimensionality of single-cell RNA sequencing (scRNAseq) data sets. Existing gene filters operate on scRNAseq datasets in isolation from other datasets. When jointly analyzing multiple datasets, however, there is a need for gene filters that are tailored to comparative analysis. In this work, we present a method for ranking the relevance of genes for comparing trajectory datasets. Our method is unsupervised, i.e., the cell metadata are not assumed to be known. Using the top-ranking genes significantly improves performance compared to methods not tailored to comparative analysis. We demonstrate the effectiveness of our algorithm on previously published datasets from studies on preimplantation embryo development, neurogenesis and cardiogenesis.