ArticleNAR genomics and bioinformatics2021
Single-cell mapper (scMappR): using scRNA-seq to infer the cell-type specificities of differentially expressed genes.
Article in NAR genomics and bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 32 citations in OpenAlex.
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- Molecular Encoding of Ischemic Stroke and its Resolution after Human Neural Stem Cell Therapy by Extracellular Vesicles.MedComm · 2025Article
- Topological data analysis in single cell biology.Frontiers in immunology · 2025Review
- SCInter: A comprehensive single-cell transcriptome integration database for human and mouse.Computational and structural biotechnology journal · 2024Article
- Cell-specific priors rescue differential gene expression in spatial spot-based technologies.Briefings in bioinformatics · 2024Article
- Age, sex, and cell type-resolved hypothalamic gene expression across the pubertal transition in mice.Biology of sex differences · 2024Article
- Decomprolute is a benchmarking platform designed for multiomics-based tumor deconvolution.Cell reports methods · 2024Article
- Systems biology analysis reveals distinct molecular signatures associated with immune responsiveness to the BNT162b COVID-19 vaccine.EBioMedicine · 2024Article
- Article
- CellGO: a novel deep learning-based framework and webserver for cell-type-specific gene function interpretation.Briefings in bioinformatics · 2023Article
- Applications of single-cell RNA sequencing in drug discovery and development.Nature reviews. Drug discovery · 2023Review
- Differential Expression Enrichment Tool (DEET): an interactive atlas of human differential gene expression.NAR genomics and bioinformatics · 2023Article
- Time series transcriptome analysis implicates the circadian clock in theProceedings of the National Academy of Sciences of the United States of America · 2023Article
- Construction of EMT related prognostic signature for kidney renal clear cell carcinoma, through integrating bulk and single-cell gene expression profiles.Frontiers in pharmacology · 2023Article
- Postnatal developmental trajectory of sex-biased gene expression in the mouse pituitary gland.Biology of sex differences · 2022Article
- A single-cell atlas of the normal and malformed human brain vasculature.Science (New York, N.Y.) · 2022Article
- LFSC: A linear fast semi-supervised clustering algorithm that integrates reference-bulk and single-cell transcriptomes.Frontiers in genetics · 2022Article
- The genetic source tracking of human urinary exosomes.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Integrative transcriptomic profiling of a mouse model of hypertension-accelerated diabetic kidney disease.Disease models & mechanisms · 2021Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
RNA sequencing (RNA-seq) is widely used to identify differentially expressed genes (DEGs) and reveal biological mechanisms underlying complex biological processes. RNA-seq is often performed on heterogeneous samples and the resulting DEGs do not necessarily indicate the cell-types where the differential expression occurred. While single-cell RNA-seq (scRNA-seq) methods solve this problem, technical and cost constraints currently limit its widespread use. Here we present single cell Mapper (scMappR), a method that assigns cell-type specificity scores to DEGs obtained from bulk RNA-seq by leveraging cell-type expression data generated by scRNA-seq and existing deconvolution methods. After evaluating scMappR with simulated RNA-seq data and benchmarking scMappR using RNA-seq data obtained from sorted blood cells, we asked if scMappR could reveal known cell-type specific changes that occur during kidney regeneration. scMappR appropriately assigned DEGs to cell-types involved in kidney regeneration, including a relatively small population of immune cells. While scMappR can work with user-supplied scRNA-seq data, we curated scRNA-seq expression matrices for ∼100 human and mouse tissues to facilitate its stand-alone use with bulk RNA-seq data from these species. Overall, scMappR is a user-friendly R package that complements traditional differential gene expression analysis of bulk RNA-seq data.
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Registered trials
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.