PT  - JOURNAL ARTICLE
AU  - Tallulah S. Andrews
AU  - Martin Hemberg
TI  - Modelling dropouts improves feature selection in scRNASeq experiments
AID  - 10.1101/065094
DP  - 2016 Jan 01
TA  - bioRxiv
PG  - 065094
4099  - http://biorxiv.org/content/early/2016/10/20/065094.short
4100  - http://biorxiv.org/content/early/2016/10/20/065094.full
AB  - A key challenge of single-cell RNASeq (scRNASeq) is the many genes with zero reads in some cells, but high expression in others. Modelling zeros using the Michaelis-Menten equation provides a superior fit to existing scRNASeq datasets compared to other approaches and enables fast and accurate identification of features corresponding to differentially expressed genes without prior identification of cell subpopulations. Applying our method to mouse preimplantation embryos revealed clusters corresponding to the inner cell mass and trophectoderm of the blastocyst. Our feature selection method overcomes batch effects to cluster cells from five different datasets by developmental stage rather than experimental origin.