Evidence map›Paper›PMID 26350211›Full record

ArticleNucleic acids research2016

Turning publicly available gene expression data into discoveries using gene set context analysis.

Zhicheng Ji, Steven A Vokes, Chi V Dang, Hongkai Ji

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nucleic acids research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

18 citing papers in PubMed.

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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

4 authors.

Zhicheng JiDepartment of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD 21205, USA.
Steven A VokesDepartment of Molecular Biosciences, The University of Texas at Austin, 2500 Speedway Stop A4800, Austin, TX 78712, USA Institute for Cellular and Molecular Biology, The University of Texas at Austin, 2500 Speedway Stop A4800, Austin, TX 78712, USA.
Chi V DangAbramson Cancer Center, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, USA.
Hongkai JiDepartment of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD 21205, USA hji@jhu.edu.

Funding

MYC--TRANSCRIPTION AND INTERACTION WITH P107R01CA051497 · NCI · WISTAR INSTITUTE · PI DANG, CHI V. · 1995 to 2025
$5.3M
Computational Tools for Mining Large Amounts of ChIP and Gene Expression DataR01HG006282 · NHGRI · JOHNS HOPKINS UNIVERSITY · PI JI, HONGKAI · 2012 to 2016
$2.0M
NCI NIH HHS R01 CA051497NHGRI NIH HHS R01 HG006282NHGRI NIH HHS R01HG006282
6 · The paper itself

Abstract

Gene Set Context Analysis (GSCA) is an open source software package to help researchers use massive amounts of publicly available gene expression data (PED) to make discoveries. Users can interactively visualize and explore gene and gene set activities in 25,000+ consistently normalized human and mouse gene expression samples representing diverse biological contexts (e.g. different cells, tissues and disease types, etc.). By providing one or multiple genes or gene sets as input and specifying a gene set activity pattern of interest, users can query the expression compendium to systematically identify biological contexts associated with the specified gene set activity pattern. In this way, researchers with new gene sets from their own experiments may discover previously unknown contexts of gene set functions and hence increase the value of their experiments. GSCA has a graphical user interface (GUI). The GUI makes the analysis convenient and customizable. Analysis results can be conveniently exported as publication quality figures and tables. GSCA is available at https://github.com/zji90/GSCA. This software significantly lowers the bar for biomedical investigators to use PED in their daily research for generating and screening hypotheses, which was previously difficult because of the complexity, heterogeneity and size of the data.

Indexed as

Databases, Nucleic AcidAlgorithmsAnimalsComputational BiologyDatasets as TopicGene Expression ProfilingHumansSoftware

Identifiers

PMID26350211
PMCPMC4705686

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.