Evidence map›Paper›PMID 30962623›Full record

ArticleNature methods2019

cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data.

Carmen Bravo González-Blas, Liesbeth Minnoye, Dafni Papasokrati, Sara Aibar, Gert Hulselmans, Valerie Christiaens, Kristofer Davie, Jasper Wouters, Stein Aerts

Abstract read
In one paragraph

Article in Nature methods, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 270 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
270citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

270 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  17. Hybrid Encoding and Adaptive Guidance for Enhanced Single-Cell Multi-omics Clustering.Interdisciplinary sciences, computational life sciences · 2026
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210 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Carmen Bravo González-Blas *VIB Center for Brain & Disease Research, Leuven, Belgium.ORCID http://orcid.org/0000-0003-0973-9410
Liesbeth Minnoye *VIB Center for Brain & Disease Research, Leuven, Belgium.ORCID http://orcid.org/0000-0002-0898-2817
Dafni PapasokratiVIB Center for Brain & Disease Research, Leuven, Belgium.ORCID http://orcid.org/0000-0002-1524-2155
Sara AibarVIB Center for Brain & Disease Research, Leuven, Belgium.ORCID http://orcid.org/0000-0001-6104-7134
Gert HulselmansVIB Center for Brain & Disease Research, Leuven, Belgium.ORCID http://orcid.org/0000-0003-2205-1899
Valerie ChristiaensVIB Center for Brain & Disease Research, Leuven, Belgium.
Kristofer DavieVIB Center for Brain & Disease Research, Leuven, Belgium.ORCID http://orcid.org/0000-0003-2182-1249
Jasper WoutersVIB Center for Brain & Disease Research, Leuven, Belgium.ORCID http://orcid.org/0000-0002-7129-2990
Stein AertsVIB Center for Brain & Disease Research, Leuven, Belgium. stein.aerts@kuleuven.vib.be.ORCID http://orcid.org/0000-0002-8006-0315

Funding

European Research Council 724226
6 · The paper itself

Abstract

We present cisTopic, a probabilistic framework used to simultaneously discover coaccessible enhancers and stable cell states from sparse single-cell epigenomics data ( http://github.com/aertslab/cistopic ). Using a compendium of single-cell ATAC-seq datasets from differentiating hematopoietic cells, brain and transcription factor perturbations, we demonstrate that topic modeling can be exploited for robust identification of cell types, enhancers and relevant transcription factors. cisTopic provides insight into the mechanisms underlying regulatory heterogeneity in cell populations.

Indexed as

Models, TheoreticalAnimalsBlood CellsBrainCells, CulturedCluster AnalysisEpigenomicsGene Expression ProfilingGene Regulatory NetworksHumansMiceRegulatory Sequences, Nucleic AcidSequence Analysis, RNASingle-Cell AnalysisWorkflow

Identifiers

PMID30962623
PMCPMC6517279

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Registered trials

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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.