Penalised regression improves imputation of cell-type specific expression using RNA-seq data from mixed cell populations compared to domain-specific methods
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| Publicado en: | bioRxiv (Jan 24, 2025) |
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| Autor principal: | |
| Otros Autores: | , , , , , , , , , |
| Publicado: |
Cold Spring Harbor Laboratory Press
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| Acceso en línea: | Citation/Abstract Full Text - PDF Full text outside of ProQuest |
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| 001 | 3159548383 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2692-8205 | ||
| 024 | 7 | |a 10.1101/2023.09.11.556650 |2 doi | |
| 035 | |a 3159548383 | ||
| 045 | 0 | |b d20250124 | |
| 100 | 1 | |a Wei-Yu, Lin | |
| 245 | 1 | |a Penalised regression improves imputation of cell-type specific expression using RNA-seq data from mixed cell populations compared to domain-specific methods | |
| 260 | |b Cold Spring Harbor Laboratory Press |c Jan 24, 2025 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a 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 (eg 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.Competing Interest StatementThe CLUSTER consortium has been provided with generous grants from AbbVie and Sobi. CW receives funding from MSD and GSK and is a part-time employee of GSK. These companies had no involvement in the work presented here.Footnotes* Add contributing authors. Add Figure 1 to illustrate the application of the multi-response LASSO/ridge model for predicting sample-level cell-type expression. Add benchmarking results based on simulated dichotomous and continuous phenotypes, with/without sex as a covariate in the DGE models and update the original Figure 4 (now Figure 5) accordingly. Include benchmarking results based on the pseudobulk data in Figure 6. Add four Sup Figures (S3, S6, S8, S9) to summarise additional results Arrange Sup Figures in the order they appear in the content Add Sup Tables to summarise the existing methods (S1 Table) and computing usage (S2 & S3 Table) | |
| 653 | |a Machine learning | ||
| 653 | |a Ribonucleic acid--RNA | ||
| 653 | |a Gene expression | ||
| 653 | |a CD19 antigen | ||
| 653 | |a Observational learning | ||
| 653 | |a Phenotypes | ||
| 653 | |a Quorum sensing | ||
| 653 | |a CD14 antigen | ||
| 653 | |a Blood levels | ||
| 653 | |a Peripheral blood mononuclear cells | ||
| 653 | |a Leukocytes (mononuclear) | ||
| 653 | |a CD4 antigen | ||
| 653 | |a Population studies | ||
| 653 | |a CD8 antigen | ||
| 653 | |a Learning algorithms | ||
| 700 | 1 | |a Kartawinata, Melissa | |
| 700 | 1 | |a Bethany Rose Jebson | |
| 700 | 1 | |a Restuadi, Restuadi | |
| 700 | 1 | |a Peckham, Hannah | |
| 700 | 1 | |a Radziszewska, Anna | |
| 700 | 1 | |a Deakin, Claire | |
| 700 | 1 | |a Ciurtin, Coziana | |
| 700 | 1 | |a Consortium, Cluster | |
| 700 | 1 | |a Wedderburn, Lucy R | |
| 700 | 1 | |a Wallace, Chris | |
| 773 | 0 | |t bioRxiv |g (Jan 24, 2025) | |
| 786 | 0 | |d ProQuest |t Biological Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3159548383/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3159548383/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u https://www.biorxiv.org/content/10.1101/2023.09.11.556650v4 |