Evidence map›Paper›PMID 32196115›Full record

ArticleNucleic acids research2020

SingleCellSignalR: inference of intercellular networks from single-cell transcriptomics.

Simon Cabello-Aguilar, Mélissa Alame, Fabien Kon-Sun-Tack, Caroline Fau, Matthieu Lacroix, Jacques Colinge

Abstract read
In one paragraph

Article in Nucleic acids research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 292 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
292citing papers in PubMed, 1 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

292 citing papers in PubMed, 1 synthesis or guideline pooled it.

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232 more citing papers are in PubMed but not listed here.

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

6 authors.

Simon Cabello-AguilarInstitut de Recherche en Cancérologie de Montpellier, Inserm, F-34298 Montpellier, France.
Mélissa AlameInstitut de Recherche en Cancérologie de Montpellier, Inserm, F-34298 Montpellier, France.
Fabien Kon-Sun-TackInstitut de Recherche en Cancérologie de Montpellier, Inserm, F-34298 Montpellier, France.
Caroline FauInstitut de Recherche en Cancérologie de Montpellier, Inserm, F-34298 Montpellier, France.
Matthieu LacroixInstitut de Recherche en Cancérologie de Montpellier, Inserm, F-34298 Montpellier, France.
Jacques ColingeInstitut de Recherche en Cancérologie de Montpellier, Inserm, F-34298 Montpellier, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Single-cell transcriptomics offers unprecedented opportunities to infer the ligand-receptor (LR) interactions underlying cellular networks. We introduce a new, curated LR database and a novel regularized score to perform such inferences. For the first time, we try to assess the confidence in predicted LR interactions and show that our regularized score outperforms other scoring schemes while controlling false positives. SingleCellSignalR is implemented as an open-access R package accessible to entry-level users and available from https://github.com/SCA-IRCM. Analysis results come in a variety of tabular and graphical formats. For instance, we provide a unique network view integrating all the intercellular interactions, and a function relating receptors to expressed intracellular pathways. A detailed comparison of related tools is conducted. Among various examples, we demonstrate SingleCellSignalR on mouse epidermis data and discover an oriented communication structure from external to basal layers.

Indexed as

Signal TransductionSoftwareAnimalsEpidermisGene Expression ProfilingLigandsMiceSingle-Cell AnalysisWorkflowLigands

Identifiers

PMID32196115
PMCPMC7261168

What OpenQuestion holds

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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.