Evidence map›Paper›PMID 30445906›Full record

ArticleBMC bioinformatics2018

RefCell: multi-dimensional analysis of image-based high-throughput screens based on 'typical cells'.

Yang Shen, Nard Kubben, Julián Candia, Alexandre V Morozov, Tom Misteli, Wolfgang Losert

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC bioinformatics, 2018. 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
0.3field-weighted citation impact, top 25% 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, 1 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

6 authors at 5 institutions in 1 country.

Yang ShenDepartment of Physics and Institute for Physical Science and Technology, University of Maryland, College Park, MD, 20742, USA.
Nard KubbenNational Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA.
Julián CandiaTrans-NIH Center for Human Immunology (CHI), National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 20892, USA.
Alexandre V MorozovDepartment of Physics and Astronomy and Center for Quantitative Biology, Rutgers University, Piscataway, NJ, 08854, USA.
Tom MisteliNational Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA.
Wolfgang LosertDepartment of Physics and Institute for Physical Science and Technology, University of Maryland, College Park, MD, 20742, USA. wlosert@umd.edu.
University of Maryland, College Park · USNational Cancer Institute · USNational Institute of Allergy and Infectious Diseases · USNational Institutes of Health · USQuantitative BioSciences · US

Funding

Nuclear Architecture and DiseaseZIABC010309 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI MISTELI, THOMAS A · 2009 to 2025
$39.8M
Center for Human Immunology ZICAI001226 · NIAID · NATIONAL INSTITUTE OF ALLERGY AND INFECTIOUS DISEASES · PI CHERRY, JAMES · 2018 to 2025
$34.1M
Air Force Office of Scientific Research FA9550-16-1-0052National Institutes of Health Trans-NIH Center for Human Immunology (CHI), NIAID
6 · The paper itself

Abstract

backgroundImage-based high-throughput screening (HTS) reveals a high level of heterogeneity in single cells and multiple cellular states may be observed within a single population. Currently available high-dimensional analysis methods are successful in characterizing cellular heterogeneity, but suffer from the "curse of dimensionality" and non-standardized outputs.

resultsHere we introduce RefCell, a multi-dimensional analysis pipeline for image-based HTS that reproducibly captures cells with typical combinations of features in reference states and uses these "typical cells" as a reference for classification and weighting of metrics. RefCell quantitatively assesses heterogeneous deviations from typical behavior for each analyzed perturbation or sample.

conclusionsWe apply RefCell to the analysis of data from a high-throughput imaging screen of a library of 320 ubiquitin-targeted siRNAs selected to gain insights into the mechanisms of premature aging (progeria). RefCell yields results comparable to a more complex clustering-based single-cell analysis method; both methods reveal more potential hits than a conventional analysis based on averages.

Indexed as

High-Throughput Screening AssaysHumansRNA, Small InterferingRNA, Small InterferingHeterogeneityImage-based high-throughput screenSingle-cell analysis

Identifiers

PMID30445906
PMCPMC6240236
OpenAlexW2804332437

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.