ArticleGenome biology2020
Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data.
Article in Genome biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 209 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
209 citing papers in PubMed.
- TNF signature in advanced melanoma patients treated with immune checkpoint inhibitors: Results from the MELANFα clinical study.International journal of cancer · 2025Trial
- Thioredoxin-2 Alleviates Osteoporosis by Restraining FOXO3a Nuclear Translocation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Problems With GO Enrichment as an Endpoint for Ecological Omics Interpretation.Molecular ecology · 2026Article
- Benchmarking methods for inferring single-cell transcription factor activity using large-scale perturbation sequencing data.Briefings in bioinformatics · 2026Article
- Single-cell profiling defines cellular ecosystems and malignant cell state coupling across pan-cancer tumor microenvironments.Molecular cancer · 2026Article
- Single-cell multimodal profiling of pan-cancer cell lines uncovers gene regulatory principles underlying intrinsic cell states and environmental features.Nature communications · 2026Article
- Analytical Methods and Application of Single-Cell and Single-Nucleus Transcriptomics in the Study of Ischemic Stroke.Biomolecules · 2026Review
- PathwayBench: a multi-criterion benchmark of pseudobulk pathway activity scoring methods reveals rank-window competition as a mechanism of biological signal loss in single-cell RNA-seq.Research square · 2026Article
- Decoding apoptosis, ferroptosis, and inflammatory cell death in adenomyosis at single-cell resolution.Briefings in bioinformatics · 2026Article
- PCSK5 promotes angiogenesis and cardiac repair after myocardial infarction.Nature communications · 2026Article
- Single-cell transcriptomics reveals the prognostic and immune infiltration significance of FOLFOX-bevacizumab treatment and lysine crotonylation characteristics in colon cancer.Journal of gastrointestinal oncology · 2026Article
- Article
- Single-cell multimodal profiling of pan-cancer cell lines uncovers gene regulatory principles underlying intrinsic cell states and environmental features.bioRxiv : the preprint server for biology · 2026Article
- Reinvigorating COTL1Nature cell biology · 2026Article
- GLYATL1 is associated with metabolic and epigenetic changes and with endocrine resistance in luminal breast cancer.Clinical epigenetics · 2026Article
- Hypothyroidism impairs skeletal muscle regeneration after injury by altering myogenic and nonmyogenic pathways.JCI insight · 2026Article
- CGRP Enhances the Regeneration of Bone Defects by Regulating Bone Marrow Mesenchymal Stem Cells Through Promoting ANGPTL4 Secretion by Bone Blood Vessels.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Multi-modal skin atlas identifies a multicellular immune-stromal community associated with disrupted cornification and specific T cell expansion in atopic dermatitis.Nature communications · 2026Article
- Review
- Article
149 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
backgroundMany functional analysis tools have been developed to extract functional and mechanistic insight from bulk transcriptome data. With the advent of single-cell RNA sequencing (scRNA-seq), it is in principle possible to do such an analysis for single cells. However, scRNA-seq data has characteristics such as drop-out events and low library sizes. It is thus not clear if functional TF and pathway analysis tools established for bulk sequencing can be applied to scRNA-seq in a meaningful way.
resultsTo address this question, we perform benchmark studies on simulated and real scRNA-seq data. We include the bulk-RNA tools PROGENy, GO enrichment, and DoRothEA that estimate pathway and transcription factor (TF) activities, respectively, and compare them against the tools SCENIC/AUCell and metaVIPER, designed for scRNA-seq. For the in silico study, we simulate single cells from TF/pathway perturbation bulk RNA-seq experiments. We complement the simulated data with real scRNA-seq data upon CRISPR-mediated knock-out. Our benchmarks on simulated and real data reveal comparable performance to the original bulk data. Additionally, we show that the TF and pathway activities preserve cell type-specific variability by analyzing a mixture sample sequenced with 13 scRNA-seq protocols. We also provide the benchmark data for further use by the community.
conclusionsOur analyses suggest that bulk-based functional analysis tools that use manually curated footprint gene sets can be applied to scRNA-seq data, partially outperforming dedicated single-cell tools. Furthermore, we find that the performance of functional analysis tools is more sensitive to the gene sets than to the statistic used.
Indexed as
Identifiers
What OpenQuestion holds
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.