BASiCS: Bayesian Analysis of Single-Cell Sequencing Data
Open accessAbstract
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.
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Papers evaluating the performance of single-cell-specific tools all compute the simulations as if cells were independent.
“Furthermore, papers evaluating the performance of single-cell-specific tools all compute the simulations as if cells were independent15–21.”
From A practical solution to pseudoreplication bias in single-cell studies (2021) · cites this paper as [21]
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- Authors
- Vallejos, C. A., Marioni, J. C., & Richardson, S.
- Affiliations
- European Bioinformatics Institute (GB)
- MRC Biostatistics Unit (GB)
- Corresponding author
- Vallejos, Catalina A.
- Marioni, John C.
- Richardson, Sylvia
- Venue
- PLOS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2015-06-24
- Volume
- 11
- Issue
- 6
- Pages
- e1004333
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC4480965
- OpenAlex
- W839653734
- Cited by
- 302
- References
- 20
- Citations by year
- 2026: 10 · 2025: 16 · 2024: 27 · 2023: 32 · 2022: 24 · 2021: 44 · 2020: 28 · 2019: 31 · 2018: 37 · 2017: 35 · 2016: 18
- FWCI
- 13.02
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- UCL Discovery (University College London) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Spiral (Imperial College London) (submittedVersion, cc-by)
- Funders
- European Bioinformatics Institute
- Cancer Research UK (22231)
- Medical Research Council
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- single-cell RNA sequencing, cellular heterogeneity, Bayesian hierarchical model, technical noise, mouse embryonic stem cells, gene expression variation
- MeSH terms
- Animals, Bayes Theorem, RNA, Messenger, Reproducibility of Results, Computational Biology, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, Mice, Embryonic Stem Cells, Single-Cell Analysis
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