BASiCS: Bayesian Analysis of Single-Cell Sequencing Data

• Published in: PLoS computational biology Vol. 11; no. 6; p. e1004333
• Main Authors: Vallejos, Catalina A., Marioni, John C., Richardson, Sylvia
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.06.2015; Public Library of Science (PLoS)
• Subjects: Algorithms; Animals; Bayes Theorem; Bayesian analysis; Computational Biology - methods; Datasets; Embryonic Stem Cells - metabolism; Embryonic Stem Cells - physiology; Experiments; Funding; Gene expression; Gene Expression Profiling - methods; Mice; Noise; Oligonucleotide Array Sequence Analysis; Ontology; Population; Reproducibility of Results; RNA, Messenger - analysis; RNA, Messenger - genetics; RNA, Messenger - metabolism; Single-Cell Analysis - methods; Stem cells
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1004333

Single-cell mRNA sequencing can uncover novel cell-to-cell heterogeneity in gene expression levels in seemingly homogeneous populations of cells. However, these experiments are prone to high levels of unexplained technical noise, creating new challenges for identifying genes that show genuine heterogeneous expression within the population of cells under study. BASiCS (Bayesian Analysis of Single-Cell Sequencing data) is an integrated Bayesian hierarchical model where: (i) cell-specific normalisation constants are estimated as part of the model parameters, (ii) technical variability is quantified based on spike-in genes that are artificially introduced to each analysed cell's lysate and (iii) the total variability of the expression counts is decomposed into technical and biological components. BASiCS also provides an intuitive detection criterion for highly (or lowly) variable genes within the population of cells under study. This is formalised by means of tail posterior probabilities associated to high (or low) biological cell-to-cell variance contributions, quantities that can be easily interpreted by users. We demonstrate our method using gene expression measurements from mouse Embryonic Stem Cells. Cross-validation and meaningful enrichment of gene ontology categories within genes classified as highly (or lowly) variable supports the efficacy of our approach.