Evidence map›Paper›PMID 31988302›Full record

ArticleScientific reports2020

Stabilized Reconstruction of Signaling Networks from Single-Cell Cue-Response Data.

Sunil Kumar, Xiao-Kang Lun, Bernd Bodenmiller, María Rodríguez Martínez, Heinz Koeppl

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.2field-weighted citation impact, top 51% 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

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. Article
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

5 authors at 5 institutions in 3 countries.

Sunil KumarInstitute of Biochemistry, ETH Zurich, Zurich, 8093, Switzerland.
Xiao-Kang LunInstitute of Molecular Life Sciences, University of Zurich, Zurich, 8057, Switzerland.ORCID http://orcid.org/0000-0003-0534-469X
Bernd BodenmillerInstitute of Molecular Life Sciences, University of Zurich, Zurich, 8057, Switzerland.ORCID http://orcid.org/0000-0002-6325-7861
María Rodríguez MartínezIBM Research Zurich, Zurich, 8803, Switzerland. mrm@zurich.ibm.com.ORCID https://orcid.org/0000-0003-3766-4233
Heinz KoepplDepartment of Electrical Engineering and Information Technology, Technische Universität Darmstadt, Darmstadt, Germany. Heinz.koeppl@bcs.tu-darmstadt.de.
ETH Zurich · CHHarvard University · USIBM Research - Zurich · CHTechnische Universität Darmstadt · DEUniversity of Zurich · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Inferring cell-signaling networks from high-throughput data is a challenging problem in systems biology. Recent advances in cytometric technology enable us to measure the abundance of a large number of proteins at the single-cell level across time. Traditional network reconstruction approaches usually consider each time point separately, resulting thus in inferred networks that strongly vary across time. To account for the possibly time-invariant physical couplings within the signaling network, we extend the traditional graphical lasso with an additional regularizer that penalizes network variations over time. ROC evaluation of the method on in silico data showed higher reconstruction accuracy than standard graphical lasso. We also tested our approach on single-cell mass cytometry data of IFNγ-stimulated THP1 cells with 26 phospho-proteins simultaneously measured. Our approach recapitulated known signaling relationships, such as connection within the JAK/STAT pathway, and was further validated in characterizing perturbed signaling network with PI3K, MEK1/2 and AMPK inhibitors.

Indexed as

AlgorithmsComputational BiologyComputer SimulationGene Regulatory NetworksGenomeHumansModels, StatisticalROC CurveSingle-Cell AnalysisSystems BiologyTHP-1 Cells

Identifiers

PMID31988302
PMCPMC6985232
OpenAlexW3004556141

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.