Evidence map›Paper›PMID 31272390›Full record

ArticleBMC bioinformatics2019

Reporting and connecting cell type names and gating definitions through ontologies.

James A Overton, Randi Vita, Patrick Dunn, Julie G Burel, Syed Ahmad Chan Bukhari, Kei-Hoi Cheung, Steven H Kleinstein, Alexander D Diehl, Bjoern Peters

Abstract read
In one paragraph

Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–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

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Visinity: Visual Spatial Neighborhood Analysis for Multiplexed Tissue Imaging Data.IEEE transactions on visualization and computer graphics · 2023
    Article
  5. Article
  6. Article
  7. 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

9 authors.

James A OvertonKnocean Inc., Toronto, Ontario, Canada.
Randi VitaDivision for Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA, USA.
Patrick DunnImmPort Curation Team, NG Health Solutions, Rockville, MD, USA.
Julie G BurelDivision for Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA, USA.
Syed Ahmad Chan BukhariDepartment of Pathology, Yale School of Medicine, New Haven, Connecticut, USA.
Kei-Hoi CheungDepartment of Emergency Medicine and Yale Center for Medical Informatics, Yale School of Medicine, New Haven, CT, USA.
Steven H KleinsteinDepartment of Pathology, Yale School of Medicine, New Haven, Connecticut, USA.
Alexander D DiehlDepartment of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA.
Bjoern PetersDivision for Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA, USA. bpeters@lji.org.

Funding

Project 3 - Ex vivo immune profiling of dengue viruses and vaccinesU19AI118610 · NIAID · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI JOHNSON, JEFFREY R · 2015 to 2021
$46.6M
Single-cell atlas of lung tissue-resident memory T cells reactive to upper and lower respiratory tract pathogensU19AI118626 · NIAID · LA JOLLA INSTITUTE FOR IMMUNOLOGY · PI Bjoern Peters · 2015 to 2026
$36.7M
Services to support the OBO foundry standardsR24HG010032 · NHGRI · LA JOLLA INSTITUTE FOR IMMUNOLOGY · PI MUNGALL, CHRISTOPHER J, PETERS, BJOERN · 2017 to 2019
$1.4M
NHGRI NIH HHS R24 HG010032NIAID NIH HHS HHSN272201200010CNIAID NIH HHS U19 AI118610NIAID NIH HHS U19 AI118626
6 · The paper itself

Abstract

backgroundHuman immunology studies often rely on the isolation and quantification of cell populations from an input sample based on flow cytometry and related techniques. Such techniques classify cells into populations based on the detection of a pattern of markers. The description of the cell populations targeted in such experiments typically have two complementary components: the description of the cell type targeted (e.g. 'T cells'), and the description of the marker pattern utilized (e.g. CD14-, CD3+).

resultsWe here describe our attempts to use ontologies to cross-compare cell types and marker patterns (also referred to as gating definitions). We used a large set of such gating definitions and corresponding cell types submitted by different investigators into ImmPort, a central database for immunology studies, to examine the ability to parse gating definitions using terms from the Protein Ontology (PRO) and cell type descriptions, using the Cell Ontology (CL). We then used logical axioms from CL to detect discrepancies between the two.

conclusionsWe suggest adoption of our proposed format for describing gating and cell type definitions to make comparisons easier. We also suggest a number of new terms to describe gating definitions in flow cytometry that are not based on molecular markers captured in PRO, but on forward- and side-scatter of light during data acquisition, which is more appropriate to capture in the Ontology for Biomedical Investigations (OBI). Finally, our approach results in suggestions on what logical axioms and new cell types could be considered for addition to the Cell Ontology.

Indexed as

Biological OntologiesDatabases, FactualHumansImmune SystemProteinsProtein SubunitsProteinsProtein SubunitsCell ontologyCell typeGating definitionsHIPCHuman immunology project consortiumImmPortImmunology database and analysis portal protein ontologyStandards

Identifiers

PMID31272390
PMCPMC6509839

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.