Interpretable single-cell factor decomposition using sciRED
Abstract Single-cell RNA sequencing maps gene expression heterogeneity within a tissue. However, identifying biological signals in this data is challenging due to confounding technical factors, sparsity, and high dimensionality. Data factorization methods address this by separating and identifying s...
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Nature Portfolio
2025-02-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-57157-2 |
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| author | Delaram Pouyabahar Tallulah Andrews Gary D. Bader |
| author_facet | Delaram Pouyabahar Tallulah Andrews Gary D. Bader |
| author_sort | Delaram Pouyabahar |
| collection | DOAJ |
| description | Abstract Single-cell RNA sequencing maps gene expression heterogeneity within a tissue. However, identifying biological signals in this data is challenging due to confounding technical factors, sparsity, and high dimensionality. Data factorization methods address this by separating and identifying signals in the data, such as gene expression programs, but the resulting factors must be manually interpreted. We developed Single-Cell Interpretable REsidual Decomposition (sciRED) to improve the interpretation of scRNA-seq factor analysis. sciRED removes known confounding effects, uses rotations to improve factor interpretability, maps factors to known covariates, identifies unexplained factors that may capture hidden biological phenomena, and determines the genes and biological processes represented by the resulting factors. We apply sciRED to multiple scRNA-seq datasets and identify sex-specific variation in a kidney map, discern strong and weak immune stimulation signals in a PBMC dataset, reduce ambient RNA contamination in a rat liver atlas to help identify strain variation and reveal rare cell type signatures and anatomical zonation gene programs in a healthy human liver map. These demonstrate that sciRED is useful in characterizing diverse biological signals within scRNA-seq data. |
| format | Article |
| id | doaj-art-8f82c069bfb649ca9f6a830eb1026ebe |
| institution | DOAJ |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-8f82c069bfb649ca9f6a830eb1026ebe2025-08-20T03:10:57ZengNature PortfolioNature Communications2041-17232025-02-0116111610.1038/s41467-025-57157-2Interpretable single-cell factor decomposition using sciREDDelaram Pouyabahar0Tallulah Andrews1Gary D. Bader2Department of Molecular Genetics, University of TorontoDepartment of Biochemistry, Schulich School of Medicine and Dentistry, University of Western OntarioDepartment of Molecular Genetics, University of TorontoAbstract Single-cell RNA sequencing maps gene expression heterogeneity within a tissue. However, identifying biological signals in this data is challenging due to confounding technical factors, sparsity, and high dimensionality. Data factorization methods address this by separating and identifying signals in the data, such as gene expression programs, but the resulting factors must be manually interpreted. We developed Single-Cell Interpretable REsidual Decomposition (sciRED) to improve the interpretation of scRNA-seq factor analysis. sciRED removes known confounding effects, uses rotations to improve factor interpretability, maps factors to known covariates, identifies unexplained factors that may capture hidden biological phenomena, and determines the genes and biological processes represented by the resulting factors. We apply sciRED to multiple scRNA-seq datasets and identify sex-specific variation in a kidney map, discern strong and weak immune stimulation signals in a PBMC dataset, reduce ambient RNA contamination in a rat liver atlas to help identify strain variation and reveal rare cell type signatures and anatomical zonation gene programs in a healthy human liver map. These demonstrate that sciRED is useful in characterizing diverse biological signals within scRNA-seq data.https://doi.org/10.1038/s41467-025-57157-2 |
| spellingShingle | Delaram Pouyabahar Tallulah Andrews Gary D. Bader Interpretable single-cell factor decomposition using sciRED Nature Communications |
| title | Interpretable single-cell factor decomposition using sciRED |
| title_full | Interpretable single-cell factor decomposition using sciRED |
| title_fullStr | Interpretable single-cell factor decomposition using sciRED |
| title_full_unstemmed | Interpretable single-cell factor decomposition using sciRED |
| title_short | Interpretable single-cell factor decomposition using sciRED |
| title_sort | interpretable single cell factor decomposition using scired |
| url | https://doi.org/10.1038/s41467-025-57157-2 |
| work_keys_str_mv | AT delarampouyabahar interpretablesinglecellfactordecompositionusingscired AT tallulahandrews interpretablesinglecellfactordecompositionusingscired AT garydbader interpretablesinglecellfactordecompositionusingscired |