ArticlePloS one2020
Sources of variation in cell-type RNA-Seq profiles.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Multi‑omics insights into uveitis: From mechanisms to precision medicine (Review).International journal of molecular medicine · 2026Review
- Nanostring Transcriptomic Analysis Highlights IL-6 Family and TGF-β Pathways in the Pathogenesis of Prurigo Nodularis.JID innovations : skin science from molecules to population health · 2026Article
- Blood transcriptomics analysis offers insights into variant-specific immune response to SARS-CoV-2.Scientific reports · 2024Article
- scPML: pathway-based multi-view learning for cell type annotation from single-cell RNA-seq data.Communications biology · 2023Article
- Blood transcriptomics analysis offers insights into variant-specific immune response to SARS-CoV-2.bioRxiv : the preprint server for biology · 2023Article
- Characterizing cancer metabolism from bulk and single-cell RNA-seq data using METAFlux.Nature communications · 2023Article
- A statistical framework to identify cell types whose genetically regulated proportions are associated with complex diseases.PLoS genetics · 2023Article
- Radiomics for characterization of the glioma immune microenvironment.NPJ precision oncology · 2023Review
- A comprehensive platform for analyzing longitudinal multi-omics data.Nature communications · 2023Article
- Generation and analysis of context-specific genome-scale metabolic models derived from single-cell RNA-Seq data.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Article
- Behavioral Neuroscience in the Era of Genomics: Tools and Lessons for Analyzing High-Dimensional Datasets.International journal of molecular sciences · 2022Review
- A review of digital cytometry methods: estimating the relative abundance of cell types in a bulk of cells.Briefings in bioinformatics · 2021Review
- Genetic Regulation of Transcription in the Endometrium in Health and Disease.Frontiers in reproductive health · 2021Review
- Learning the Regulatory Code of Gene Expression.Frontiers in molecular biosciences · 2021Review
- Article
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Authors and funding
6 authors.
Funding
Abstract
Cell-type specific gene expression profiles are needed for many computational methods operating on bulk RNA-Seq samples, such as deconvolution of cell-type fractions and digital cytometry. However, the gene expression profile of a cell type can vary substantially due to both technical factors and biological differences in cell state and surroundings, reducing the efficacy of such methods. Here, we investigated which factors contribute most to this variation. We evaluated different normalization methods, quantified the variance explained by different factors, evaluated the effect on deconvolution of cell type fractions, and examined the differences between UMI-based single-cell RNA-Seq and bulk RNA-Seq. We investigated a collection of publicly available bulk and single-cell RNA-Seq datasets containing B and T cells, and found that the technical variation across laboratories is substantial, even for genes specifically selected for deconvolution, and this variation has a confounding effect on deconvolution. Tissue of origin is also a substantial factor, highlighting the challenge of using cell type profiles derived from blood with mixtures from other tissues. We also show that much of the differences between UMI-based single-cell and bulk RNA-Seq methods can be explained by the number of read duplicates per mRNA molecule in the single-cell sample. Our work shows the importance of either matching or correcting for technical factors when creating cell-type specific gene expression profiles that are to be used together with bulk samples.
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