BASiCS: Bayesian Analysis of Single-Cell Sequencing Data

Overview

Journal PLoS Comput Biol

Specialty Biology

Date 2015 Jun 25

PMID 26107944

Citations 144

Authors

Catalina A Vallejos

John C Marioni

Sylvia Richardson

Affiliations

Soon will be listed here.

Abstract

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.

Citing Articles

Single-cell RNA sequencing algorithms underestimate changes in transcriptional noise compared to single-molecule RNA imaging.

Khetan N, Zuckerman B, Calia G, Chen X, Garcia Arceo X, Weinberger L Cell Rep Methods. 2024; 4(12):100933.

PMID: 39662473 PMC: 11704610. DOI: 10.1016/j.crmeth.2024.100933.

NiCo identifies extrinsic drivers of cell state modulation by niche covariation analysis.

Agrawal A, Thomann S, Basu S, Grun D Nat Commun. 2024; 15(1):10628.

PMID: 39639035 PMC: 11621405. DOI: 10.1038/s41467-024-54973-w.

cytoKernel: Robust kernel embeddings for assessing differential expression of single cell data.

Ghosh T, Baxter R, Seal S, Lui V, Rudra P, Vu T bioRxiv. 2024; .

PMID: 39229233 PMC: 11370373. DOI: 10.1101/2024.08.16.608287.

Quantitative comparison of single-cell RNA sequencing versus single-molecule RNA imaging for quantifying transcriptional noise.

Khetan N, Zuckerman B, Calia G, Chen X, Garcia Arceo X, Weinberger L bioRxiv. 2024; .

PMID: 39149226 PMC: 11326230. DOI: 10.1101/2024.08.09.607289.

COMSE: analysis of single-cell RNA-seq data using community detection-based feature selection.

Luo Q, Chen Y, Lan X BMC Biol. 2024; 22(1):167.

PMID: 39113021 PMC: 11304914. DOI: 10.1186/s12915-024-01963-5.

References

Stegle O, Teichmann S, Marioni J . Computational and analytical challenges in single-cell transcriptomics. Nat Rev Genet. 2015; 16(3):133-45. DOI: 10.1038/nrg3833. View

Murphy C, Polak J . Differentiating embryonic stem cells: GAPDH, but neither HPRT nor beta-tubulin is suitable as an internal standard for measuring RNA levels. Tissue Eng. 2002; 8(4):551-9. DOI: 10.1089/107632702760240472. View

Newton M, Noueiry A, Sarkar D, Ahlquist P . Detecting differential gene expression with a semiparametric hierarchical mixture method. Biostatistics. 2004; 5(2):155-76. DOI: 10.1093/biostatistics/5.2.155. View

Richardson S, Gilks W . A Bayesian approach to measurement error problems in epidemiology using conditional independence models. Am J Epidemiol. 1993; 138(6):430-42. DOI: 10.1093/oxfordjournals.aje.a116875. View

Carter M, Stagg C, Falco G, Yoshikawa T, Bassey U, Aiba K . An in situ hybridization-based screen for heterogeneously expressed genes in mouse ES cells. Gene Expr Patterns. 2008; 8(3):181-98. PMC: 2238805. DOI: 10.1016/j.gep.2007.10.009. View

Tang F, Barbacioru C, Wang Y, Nordman E, Lee C, Xu N . mRNA-Seq whole-transcriptome analysis of a single cell. Nat Methods. 2009; 6(5):377-82. DOI: 10.1038/nmeth.1315. View

Gupta R, Greco D, Auvinen P, Arjas E . Bayesian integrated modeling of expression data: a case study on RhoG. BMC Bioinformatics. 2010; 11:295. PMC: 2894040. DOI: 10.1186/1471-2105-11-295. View

Anders S, Huber W . Differential expression analysis for sequence count data. Genome Biol. 2010; 11(10):R106. PMC: 3218662. DOI: 10.1186/gb-2010-11-10-r106. View

Jiang L, Schlesinger F, Davis C, Zhang Y, Li R, Salit M . Synthetic spike-in standards for RNA-seq experiments. Genome Res. 2011; 21(9):1543-51. PMC: 3166838. DOI: 10.1101/gr.121095.111. View

10.

Brennecke P, Anders S, Kim J, Kolodziejczyk A, Zhang X, Proserpio V . Accounting for technical noise in single-cell RNA-seq experiments. Nat Methods. 2013; 10(11):1093-5. DOI: 10.1038/nmeth.2645. View

11.

Islam S, Zeisel A, Joost S, La Manno G, Zajac P, Kasper M . Quantitative single-cell RNA-seq with unique molecular identifiers. Nat Methods. 2013; 11(2):163-6. DOI: 10.1038/nmeth.2772. View

12.

Macaulay I, Voet T . Single cell genomics: advances and future perspectives. PLoS Genet. 2014; 10(1):e1004126. PMC: 3907301. DOI: 10.1371/journal.pgen.1004126. View

13.

Grun D, Kester L, van Oudenaarden A . Validation of noise models for single-cell transcriptomics. Nat Methods. 2014; 11(6):637-40. DOI: 10.1038/nmeth.2930. View

14.

Patel A, Tirosh I, Trombetta J, Shalek A, Gillespie S, Wakimoto H . Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science. 2014; 344(6190):1396-401. PMC: 4123637. DOI: 10.1126/science.1254257. View

15.

Zeisel A, Munoz-Manchado A, Codeluppi S, Lonnerberg P, La Manno G, Jureus A . Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science. 2015; 347(6226):1138-42. DOI: 10.1126/science.aaa1934. View

16.

Saliba A, Westermann A, Gorski S, Vogel J . Single-cell RNA-seq: advances and future challenges. Nucleic Acids Res. 2014; 42(14):8845-60. PMC: 4132710. DOI: 10.1093/nar/gku555. View

17.

Buettner F, Natarajan K, Casale F, Proserpio V, Scialdone A, Theis F . Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nat Biotechnol. 2015; 33(2):155-60. DOI: 10.1038/nbt.3102. View