Computational deconvolution to estimate cell type-specific gene expression from bulk data. Article Swipe
Maria K. Jaakkola
,
Laura L. Elo
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1093/nargab/lqaa110
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1093/nargab/lqaa110
Computational deconvolution is a time and cost-efficient approach to obtain cell type-specific information from bulk gene expression of heterogeneous tissues like blood. Deconvolution can aim to either estimate cell type proportions or abundances in samples, or estimate how strongly each present cell type expresses different genes, or both tasks simultaneously. Among the two separate goals, the estimation of cell type proportions/abundances is widely studied, but less attention has been paid on defining the cell type-specific expression profiles. Here, we address this gap by introducing a novel method Rodeo and empirically evaluating it and the other available tools from multiple perspectives utilizing diverse datasets.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://pubmed.ncbi.nlm.nih.gov/33575652
- OA Status
- green
- Cited By
- 16
- References
- 43
- Related Works
- 20
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3124408455Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/nargab/lqaa110Digital Object Identifier
- Title
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Computational deconvolution to estimate cell type-specific gene expression from bulk data.Work title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-03-01Full publication date if available
- Authors
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Maria K. Jaakkola, Laura L. EloList of authors in order
- Landing page
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https://pubmed.ncbi.nlm.nih.gov/33575652Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://www.ncbi.nlm.nih.gov/pmc/articles/7803005Direct OA link when available
- Concepts
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Deconvolution, Type (biology), Expression (computer science), Cell type, Computational biology, Computer science, Gene expression, Gene, Data type, Cell, Data mining, Biological system, Biology, Mathematics, Algorithm, Genetics, Ecology, Programming languageTop concepts (fields/topics) attached by OpenAlex
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16Total citation count in OpenAlex
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2025: 3, 2024: 4, 2023: 4, 2022: 2, 2021: 3Per-year citation counts (last 5 years)
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43Number of works referenced by this work
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20Other works algorithmically related by OpenAlex
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| publication_date | 2021-03-01 |
| publication_year | 2021 |
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