ArticleGenome research2021
Bayesian estimation of cell type-specific gene expression with prior derived from single-cell data.
Article in Genome research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers.
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Who cites it
62 citing papers in PubMed.
- CausalCellInfer: Prioritizing Cell-Type-Specific Disease Associations From Genetically Predicted Transcriptomes in Biobank Cohorts.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Single-nucleus atlas of cell-type specific genetic regulation in the human brain.Nature genetics · 2026Article
- Integrated inference of cellular compositions and gene expression programs by deconvolution.Cell regeneration (London, England) · 2026Article
- High-resolution reconstruction of cell-type-specific transcriptional regulatory processes from bulk sequencing samples.Nature biotechnology · 2026Article
- BMI-genome interactions regulate global gene expression with emphasis in brain and gut.Cell genomics · 2026Article
- Trisomy 21 Drives ADARB1 Overexpression and Premature RNA Recoding in the Developing Fetal Brain.Nature communications · 2026Article
- Evaluating reference-mixture matching in cell-type deconvolution with single-cell RNA-seq references.Briefings in bioinformatics · 2026Article
- Integrating multi-omic QTLs and predictive models reveals regulatory architectures at immune related GWAS loci in CD4+ T cells.medRxiv : the preprint server for health sciences · 2026Article
- UBD: incorporating uncertainty in cell type proportion estimates from bulk samples to infer cell-type-specific profiles.Briefings in bioinformatics · 2026Article
- Evaluating and Optimizing Mass Spectrometry Proteomics Data to Deconvolve Cell-Type-Specific Protein Expression in Tumors.Journal of proteome research · 2026Article
- Article
- Cell type-specific inference from bulk RNA-sequencing data by integrating single-cell reference profiles via EPIC-unmix.Genome biology · 2025Article
- Unico: a unified model for cell-type resolution genomics from heterogeneous omics data.Genome biology · 2025Article
- BLEND: probabilistic cellular deconvolution with individualized single-cell reference integration.Genome biology · 2025Article
- Deconvolution-Based Transcriptomic Analysis in the Hippocampus Reveals Cell Type-Specific Risk Genes and Pathways Associated With Depression and Suicide.Biological psychiatry global open science · 2025Article
- Direct single cell-type gene expression analysis in peripheral blood: novel ratio-based gene expression biomarkers using 2 novel monocyte reference genes (PSAP and CTSS) for detection of bacterial infection.Human molecular genetics · 2025Article
- Cell-type-specific and inflammatory DNA methylation patterns associated with PTSD.Brain, behavior, and immunity · 2025Article
- Novel insights into post-myocardial infarction cardiac remodeling through algorithmic detection of cell-type composition shifts.PLoS genetics · 2025Article
- Chromatin accessibility provides a window into the genetic etiology of human brain disease.Trends in genetics : TIG · 2025Review
- An atlas of single-cell eQTLs dissects autoimmune disease genes and identifies novel drug classes for treatment.Cell genomics · 2025Article
2 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors.
Funding
Abstract
When assessed over a large number of samples, bulk RNA sequencing provides reliable data for gene expression at the tissue level. Single-cell RNA sequencing (scRNA-seq) deepens those analyses by evaluating gene expression at the cellular level. Both data types lend insights into disease etiology. With current technologies, scRNA-seq data are known to be noisy. Constrained by costs, scRNA-seq data are typically generated from a relatively small number of subjects, which limits their utility for some analyses, such as identification of gene expression quantitative trait loci (eQTLs). To address these issues while maintaining the unique advantages of each data type, we develop a Bayesian method (bMIND) to integrate bulk and scRNA-seq data. With a prior derived from scRNA-seq data, we propose to estimate sample-level cell type-specific (CTS) expression from bulk expression data. The CTS expression enables large-scale sample-level downstream analyses, such as detection of CTS differentially expressed genes (DEGs) and eQTLs. Through simulations, we show that bMIND improves the accuracy of sample-level CTS expression estimates and increases the power to discover CTS DEGs when compared to existing methods. To further our understanding of two complex phenotypes, autism spectrum disorder and Alzheimer's disease, we apply bMIND to gene expression data of relevant brain tissue to identify CTS DEGs. Our results complement findings for CTS DEGs obtained from snRNA-seq studies, replicating certain DEGs in specific cell types while nominating other novel genes for those cell types. Finally, we calculate CTS eQTLs for 11 brain regions by analyzing Genotype-Tissue Expression Project data, creating a new resource for biological insights.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.