CellMapper: rapid and accurate inference of gene expression in difficult-to-isolate cell types
Open accessAbstract
We present a sensitive approach to predict genes expressed selectively in specific cell types, by searching publicly available expression data for genes with a similar expression profile to known cell-specific markers. Our method, CellMapper, strongly outperforms previous computational algorithms to predict cell type-specific expression, especially for rare and difficult-to-isolate cell types. Furthermore, CellMapper makes accurate predictions for human brain cell types that have never been isolated, and can be rapidly applied to diverse cell types from many tissues. We demonstrate a clinically relevant application to prioritize candidate genes in disease susceptibility loci identified by GWAS.
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Advances in cell-type expression profiling have enabled the identification of cell-type-specific gene expression profiles.
“Beyond the identification of specific microglial markers, advances in cell-type expression profiling have enabled the identification of cell-type-specific gene expression profiles (10).”
From The Transcriptional Landscape of Microglial Genes in Aging and Neurodegenerative Disease (2019) · cites this paper as [10]
CellMapper requires only a single cell marker and a smaller training dataset than other techniques, and enables analysis of expression data derived from bulk brain tissue samples.
“The technique we chose, CellMapper, requires only a single cell marker and a smaller training dataset than other techniques, and enables analysis of expression data derived from bulk brain tissue samples (10).”
From The Transcriptional Landscape of Microglial Genes in Aging and Neurodegenerative Disease (2019) · cites this paper as [10]
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The Transcriptional Landscape of Microglial Genes in Aging and Neurodegenerative Disease
Bonham, L. W., Sirkis, D. W., & Yokoyama, J. S. (2019) · Frontiers in Immunology · doi:10.3389/fimmu.2019.01170 · Cited by 83 · see citing works
- Authors
- Nelms, B. D., Waldron, L., Barrera, L. A., Weflen, A. W., Goettel, J. A., Guo, G., Montgomery, R. K., Neutra, M. R., Breault, D. T., Snapper, S. B., Orkin, S. H., Bulyk, M. L., Huttenhower, C., & Lencer, W. I.
- Affiliations
- Boston Children's Hospital (US)
- City University of New York (US)
- Brigham and Women's Hospital (US)
- Zhejiang University (CN)
- Harvard University (US)
- Corresponding author
- Nelms, Bradlee D.
- Lencer, Wayne I.
- ORCID
- Venue
- Genome biology
- Published
- 2016-09-29
- Volume
- 17
- Issue
- 1
- Pages
- 201-201
- ISSN
- 1465-6906 (ISSN-L)
- 1474-760X
- PMC
- PMC5043525
- OpenAlex
- W2527229715
- Cited by
- 24
- References
- 55
- Citations by year
- 2026: 1 · 2023: 3 · 2022: 4 · 2021: 2 · 2020: 4 · 2019: 3 · 2018: 4 · 2017: 2
- FWCI
- 1.56
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Genome biology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- CUNY Academic Works (City University of New York) (submittedVersion, cc-by)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- Funders
- National Institutes of Health (R01 DK084424, R01 DK048106, P30 DK034854, R01 HG003985)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- cell type-specific gene expression, cell type markers, rare cell types, gene expression prediction, candidate gene prioritization
- MeSH terms
- Algorithms, Animals, Brain, Cell Line, Cells, Cultured, Hematopoietic Stem Cells, Humans, Immune System, Mice, Inbred C57BL, Neurons, Spleen, Reproducibility of Results, Caco-2 Cells, Computational Biology, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide, Gene Expression Profiling, Endothelial Cells, Genome-Wide Association Study
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