Benchmarking of cell type deconvolution pipelines for transcriptomics data
Open accessAbstract
Many computational methods have been developed to infer cell type proportions from bulk transcriptomics data. However, an evaluation of the impact of data transformation, pre-processing, marker selection, cell type composition and choice of methodology on the deconvolution results is still lacking. Using five single-cell RNA-sequencing (scRNA-seq) datasets, we generate pseudo-bulk mixtures to evaluate the combined impact of these factors. Both bulk deconvolution methodologies and those that use scRNA-seq data as reference perform best when applied to data in linear scale and the choice of normalization has a dramatic impact on some, but not all methods. Overall, methods that use scRNA-seq data have comparable performance to the best performing bulk methods whereas semi-supervised approaches show higher error values. Moreover, failure to include cell types in the reference that are present in a mixture leads to substantially worse results, regardless of the previous choices. Altogether, we evaluate the combined impact of factors affecting the deconvolution task across different datasets and propose general guidelines to maximize its performance.
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Computational deconvolution tools provide a less accurate estimation of cell-type-specific information from bulk sequence reads than direct isolation methods.
“While computational deconvolution tools exists for obtaining cell-type-specific information from bulk sequence reads from heterogenous tissue, they nonetheless provide a less accurate estimation [148].”
From Cell-type-specific epigenetic effects of early life stress on the brain (2022) · cites this paper as [148]
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- Authors
- Avila Cobos, F., Alquicira-Hernandez, J., Powell, J. E., Mestdagh, P., & De Preter, K.
- Affiliations
- Garvan Institute of Medical Research (AU)
- Ghent University (BE)
- ORCID
- Venue
- Nature Communications
- Published
- 2020-11-06
- Volume
- 11
- Issue
- 1
- Pages
- 5650-5650
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC7648640
- OpenAlex
- W3096486123
- Cited by
- 446
- References
- 76
- Citations by year
- 2026: 56 · 2025: 76 · 2024: 118 · 2023: 95 · 2022: 63 · 2021: 38
- FWCI
- 16.79
- Type
- journal article
- Language
- en
- Update notices
- correction (2020-12-02)
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Ghent University Academic Bibliography (Ghent University) (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- European Commission (668858, HORIZON2020, 826121)
- Fonds Wetenschappelijk Onderzoek
- Universiteit Gent
- Vlaamse regering
- Bijzonder Onderzoeksfonds UGent
- 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
- cell type deconvolution, benchmarking, bulk transcriptomics, single-cell RNA sequencing, normalization, marker selection, cell type composition, data transformation
- MeSH terms
- Animals, Cells, Humans, Species Specificity, Sequence Analysis, RNA, Computational Biology, Gene Expression Profiling, Mice, Data Mining, Single-Cell Analysis, Transcriptome
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