ArticleBriefings in bioinformatics2022
LRcell: detecting the source of differential expression at the sub-cell-type level from bulk RNA-seq data.
Article in Briefings in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review and meta-analysis on the transcriptomic signatures in alcohol use disorder.Molecular psychiatry · 2025Pooled it
- DEDUCE: statistical inference on disease-associated genes uncovers tissue-disease associations.NAR genomics and bioinformatics · 2025Article
- Phenotype prediction from single-cell RNA-seq data using attention-based neural networks.Bioinformatics (Oxford, England) · 2024Article
- Transcriptome Analysis Reveals a Two-Gene Signature Links to Motor Progression and Alterations of Immune Cells in Parkinson's Disease.Journal of Parkinson's disease · 2023Article
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Authors and funding
5 authors.
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Abstract
Given most tissues are consist of abundant and diverse (sub-)cell types, an important yet unaddressed problem in bulk RNA-seq analysis is to identify at which (sub-)cell type(s) the differential expression occurs. Single-cell RNA-sequencing (scRNA-seq) technologies can answer the question, but they are often labor-intensive and cost-prohibitive. Here, we present LRcell, a computational method aiming to identify specific (sub-)cell type(s) that drives the changes observed in a bulk RNA-seq experiment. In addition, LRcell provides pre-embedded marker genes computed from putative scRNA-seq experiments as options to execute the analyses. We conduct a simulation study to demonstrate the effectiveness and reliability of LRcell. Using three different real datasets, we show that LRcell successfully identifies known cell types involved in psychiatric disorders. Applying LRcell to bulk RNA-seq results can produce a hypothesis on which (sub-)cell type(s) contributes to the differential expression. LRcell is complementary to cell type deconvolution methods.
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