Evidence map›Paper›PMID 37862243›Full record

ArticleBioinformatics (Oxford, England)2023

Inference of differential key regulatory networks and mechanistic drug repurposing candidates from scRNA-seq data with SCANet.

Mhaned Oubounyt, Lorenz Adlung, Fabio Patroni, Nina Kerstin Wenke, Andreas Maier, Michael Hartung, Jan Baumbach, Maria L Elkjaer

Open access · goldAbstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
2.0field-weighted citation impact, top 13% of its field
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

5 citing papers in PubMed, 13 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 1 institution in 3 countries.

Mhaned OubounytInstitute for Computational Systems Biology, University of Hamburg, Hamburg 22607, Germany.ORCID 0000-0001-5011-6683
Lorenz AdlungDepartment of Medicine, Hamburg Center for Translational Immunology (HCTI) and Center for Biomedical AI (bAIome), University Medical Center Hamburg-Eppendorf (UKE), Hamburg 20246, Germany.
Fabio PatroniInstitute for Computational Systems Biology, University of Hamburg, Hamburg 22607, Germany.
Nina Kerstin WenkeInstitute for Computational Systems Biology, University of Hamburg, Hamburg 22607, Germany.
Andreas MaierInstitute for Computational Systems Biology, University of Hamburg, Hamburg 22607, Germany.ORCID 0000-0003-4408-0068
Michael HartungInstitute for Computational Systems Biology, University of Hamburg, Hamburg 22607, Germany.
Jan BaumbachInstitute for Computational Systems Biology, University of Hamburg, Hamburg 22607, Germany.ORCID 0000-0002-0282-0462
Maria L ElkjaerInstitute for Computational Systems Biology, University of Hamburg, Hamburg 22607, Germany.ORCID 0000-0002-8596-142X
Universität Hamburg · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

motivationThe reconstruction of small key regulatory networks that explain the differences in the development of cell (sub)types from single-cell RNA sequencing is a yet unresolved computational problem.

resultsTo this end, we have developed SCANet, an all-in-one package for single-cell profiling that covers the whole differential mechanotyping workflow, from inference of trait/cell-type-specific gene co-expression modules, driver gene detection, and transcriptional gene regulatory network reconstruction to mechanistic drug repurposing candidate prediction. To illustrate the power of SCANet, we examined data from two studies. First, we identify the drivers of the mechanotype of a cytokine storm associated with increased mortality in patients with acute respiratory illness. Secondly, we find 20 drugs for eight potential pharmacological targets in cellular driver mechanisms in the intestinal stem cells of obese mice. AVAILABILITY AND IMPLEMENTATION: SCANet is a free, open-source, and user-friendly Python package that can be seamlessly integrated into single-cell-based systems medicine research and mechanistic drug discovery.

Indexed as

Gene Expression ProfilingSoftwareAnimalsDrug RepositioningGene Regulatory NetworksHumansMiceSequence Analysis, RNASingle-Cell AnalysisSingle-Cell Gene Expression Analysis

Identifiers

PMID37862243
PMCPMC10628438
OpenAlexW4387823583

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.