Modelling dropouts for feature selection in scRNASeq experiments

By Tallulah S. Andrews, Martin Hemberg. 2016

38 citations recorded by the source corpus.

A key challenge of single-cell RNASeq (scRNASeq) is the many genes with zero reads in some cells, but high expression in others. In full-transcript datasets modelling zeros using the Michaelis-Menten equation provides an equal or 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. For datasets tagged with unique molecular identifiers we introduce a depth adjusted negative binomial (DANB) to perform dropout-rate based feature selection. 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. Author Summary Feature selection is a powerful approach for improving the signal to noise ratio in high dimensional datasets. We present two unsupervised feature selection methods for single-cell RNASeq data which unlike all previous methods are based on dropout rate rather than variance: M3Drop, tailored to full-transcript scRNASeq protocols, and DANB, tailored to data tagged with unique molecular identifiers (UMI). Using differentially expressed genes defined from bulk RNASeq, we perform the first comparison of feature selection quality for both full-transcript and UMI-tagged scRNASeq data. We show that dropout based methods outperform their variance-based counterparts on both real and simulated data due to lower sampling errors. Finally we demonstrate the ability to merge mouse embryo datasets produced using different protocols by different research groups using only the combination of feature selection and library size normalization.

Subfields: Cancer Research, Molecular Biology

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Citations in the corpus

Challenges in unsupervised clustering of single-cell RNA-seq data (2019, background): “It is not uncommon to have >50% of the entries in a count matrix equal to zero 46 .”
A systematic performance evaluation of clustering methods for single-cell RNA-seq data (2018, methodology): “Again, the stability was lower for the simulated data sets after gene filtering by M3Drop (note that the same genes were used in all five runs), indicating that the selection of genes may be suboptimal.”
Probabilistic harmonization and annotation of single‐cell transcriptomics data with deep generative models (2019, unknown): “We detail the case for a depth of level two in Appendix Note D though our approach can in principle be adapted to arbitrary depths.”
Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model (2019, background): “Alternatively, highly dropout genes may be retained [17].”