Penalised regression improves imputation of cell-type specific expression using RNA-seq data from mixed cell populations compared to domain-specific methods.
Gene expression studies often use bulk RNA sequencing of mixed cell populations because single cell or sorted cell sequencing may be prohibitively expensive. However, mixed cell studies may miss expression patterns that are restricted to specific cell populations. Computational deconvolution can be...
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Public Library of Science (PLoS)
2025-03-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1012859 |
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| author | Wei-Yu Lin Melissa Kartawinata Bethany R Jebson Restuadi Restuadi Hannah Peckham Anna Radziszewska Claire T Deakin Coziana Ciurtin CLUSTER Consortium Lucy R Wedderburn Chris Wallace |
| author_facet | Wei-Yu Lin Melissa Kartawinata Bethany R Jebson Restuadi Restuadi Hannah Peckham Anna Radziszewska Claire T Deakin Coziana Ciurtin CLUSTER Consortium Lucy R Wedderburn Chris Wallace |
| author_sort | Wei-Yu Lin |
| collection | DOAJ |
| description | Gene expression studies often use bulk RNA sequencing of mixed cell populations because single cell or sorted cell sequencing may be prohibitively expensive. However, mixed cell studies may miss expression patterns that are restricted to specific cell populations. Computational deconvolution can be used to estimate cell fractions from bulk expression data and infer average cell-type expression in a set of samples (e.g., cases or controls), but imputing sample-level cell-type expression is required for more detailed analyses, such as relating expression to quantitative traits, and is less commonly addressed. Here, we assessed the accuracy of imputing sample-level cell-type expression using a real dataset where mixed peripheral blood mononuclear cells (PBMC) and sorted (CD4, CD8, CD14, CD19) RNA sequencing data were generated from the same subjects (N=158), and pseudobulk datasets synthesised from eQTLgen single cell RNA-seq data. We compared three domain-specific methods, CIBERSORTx, bMIND and debCAM/swCAM, and two cross-domain machine learning methods, multiple response LASSO and ridge, that had not been used for this task before. We also assessed the methods according to their ability to recover differential gene expression (DGE) results. LASSO/ridge showed higher sensitivity but lower specificity for recovering DGE signals seen in observed data compared to deconvolution methods, although LASSO/ridge had higher area under curves than deconvolution methods. Machine learning methods have the potential to outperform domain-specific methods when suitable training data are available. |
| format | Article |
| id | doaj-art-513197c246284facb690f140e0fcd6d4 |
| institution | Kabale University |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Public Library of Science (PLoS) |
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| series | PLoS Computational Biology |
| spelling | doaj-art-513197c246284facb690f140e0fcd6d42025-08-20T03:52:48ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582025-03-01213e101285910.1371/journal.pcbi.1012859Penalised regression improves imputation of cell-type specific expression using RNA-seq data from mixed cell populations compared to domain-specific methods.Wei-Yu LinMelissa KartawinataBethany R JebsonRestuadi RestuadiHannah PeckhamAnna RadziszewskaClaire T DeakinCoziana CiurtinCLUSTER ConsortiumLucy R WedderburnChris WallaceGene expression studies often use bulk RNA sequencing of mixed cell populations because single cell or sorted cell sequencing may be prohibitively expensive. However, mixed cell studies may miss expression patterns that are restricted to specific cell populations. Computational deconvolution can be used to estimate cell fractions from bulk expression data and infer average cell-type expression in a set of samples (e.g., cases or controls), but imputing sample-level cell-type expression is required for more detailed analyses, such as relating expression to quantitative traits, and is less commonly addressed. Here, we assessed the accuracy of imputing sample-level cell-type expression using a real dataset where mixed peripheral blood mononuclear cells (PBMC) and sorted (CD4, CD8, CD14, CD19) RNA sequencing data were generated from the same subjects (N=158), and pseudobulk datasets synthesised from eQTLgen single cell RNA-seq data. We compared three domain-specific methods, CIBERSORTx, bMIND and debCAM/swCAM, and two cross-domain machine learning methods, multiple response LASSO and ridge, that had not been used for this task before. We also assessed the methods according to their ability to recover differential gene expression (DGE) results. LASSO/ridge showed higher sensitivity but lower specificity for recovering DGE signals seen in observed data compared to deconvolution methods, although LASSO/ridge had higher area under curves than deconvolution methods. Machine learning methods have the potential to outperform domain-specific methods when suitable training data are available.https://doi.org/10.1371/journal.pcbi.1012859 |
| spellingShingle | Wei-Yu Lin Melissa Kartawinata Bethany R Jebson Restuadi Restuadi Hannah Peckham Anna Radziszewska Claire T Deakin Coziana Ciurtin CLUSTER Consortium Lucy R Wedderburn Chris Wallace Penalised regression improves imputation of cell-type specific expression using RNA-seq data from mixed cell populations compared to domain-specific methods. PLoS Computational Biology |
| title | Penalised regression improves imputation of cell-type specific expression using RNA-seq data from mixed cell populations compared to domain-specific methods. |
| title_full | Penalised regression improves imputation of cell-type specific expression using RNA-seq data from mixed cell populations compared to domain-specific methods. |
| title_fullStr | Penalised regression improves imputation of cell-type specific expression using RNA-seq data from mixed cell populations compared to domain-specific methods. |
| title_full_unstemmed | Penalised regression improves imputation of cell-type specific expression using RNA-seq data from mixed cell populations compared to domain-specific methods. |
| title_short | Penalised regression improves imputation of cell-type specific expression using RNA-seq data from mixed cell populations compared to domain-specific methods. |
| title_sort | penalised regression improves imputation of cell type specific expression using rna seq data from mixed cell populations compared to domain specific methods |
| url | https://doi.org/10.1371/journal.pcbi.1012859 |
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