Evidence map›Paper›PMID 36452867›Full record

ArticleCell reports methods2022

Multiset multicover methods for discriminative marker selection.

Euxhen Hasanaj, Amir Alavi, Anupam Gupta, Barnabás Póczos, Ziv Bar-Joseph

Abstract read
In one paragraph

Article in Cell reports methods, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Optimal marker genes forGenome research · 2025
    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.

Euxhen HasanajMachine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Amir AlaviComputational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Anupam GuptaComputer Science Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Barnabás PóczosMachine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Ziv Bar-JosephMachine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.

Funding

SenNet Supplement - Consortium BenchmarkingU24CA268108 · NCI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI Philip D. Blood, JONATHAN C. SILVERSTEIN · 2021 to 2026
$22.1M
TriState SenNET (Lung and Heart) Tissue Map and Atlas consortiumU54AG075931 · NIA · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI TOREN FINKEL, Melanie Koenigshoff · 2021 to 2026
$14.0M
Comprehensive, Flexible and FAIR Tools for the HuBMAP HIVEOT2OD026682 · OD · CARNEGIE-MELLON UNIVERSITY · PI PATEN, BENEDICT, RUFFALO, MATTHEW · 2018 to 2021
$3.4M
NCI NIH HHS U24 CA268108NIA NIH HHS U54 AG075931NIH HHS OT2 OD026682
6 · The paper itself

Abstract

Markers are increasingly being used for several high-throughput data analysis and experimental design tasks. Examples include the use of markers for assigning cell types in scRNA-seq studies, for deconvolving bulk gene expression data, and for selecting marker proteins in single-cell spatial proteomics studies. Most marker selection methods focus on differential expression (DE) analysis. Although such methods work well for data with a few non-overlapping marker sets, they are not appropriate for large atlas-size datasets where several cell types and tissues are considered. To address this, we define the phenotype cover (PC) problem for marker selection and present algorithms that can improve the discriminative power of marker sets. Analysis of these sets on several marker-selection tasks suggests that these methods can lead to solutions that accurately distinguish different phenotypes in the data.

Indexed as

Gene Expression ProfilingSingle-Cell AnalysisAlgorithmsCluster AnalysisPhenotypealgorithmbiomarkercross-entropy methodgene setsmarker discoverymultiset multicoverphenotype coverscRNA-seqset cover

Identifiers

PMID36452867
PMCPMC9701606

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.