Evidence map›Paper›PMID 39712486›Full record

ArticleStat (International Statistical Institute)2024

Multivariate differential association analysis.

Hoseung Song, Michael C Wu

Abstract read
In one paragraph

Article in Stat (International Statistical Institute), 2024. 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
–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

1 citing paper in PubMed.

  1. Review
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

2 authors.

Hoseung SongDepartment of Industrial and Systems Engineering, KAIST, Daejeon, Republic of Korea.
Michael C WuPublic Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, U.S.A.

Funding

Joint Analysis of Microbiome and Other Genomic Data TypesR01GM129512 · NIGMS · FRED HUTCHINSON CANCER RESEARCH CENTER · PI WU, MICHAEL CHIAO-AN · 2018 to 2021
$1.6M
NIGMS NIH HHS R01 GM129512
6 · The paper itself

Abstract

Identifying how dependence relationships vary across different conditions plays a significant role in many scientific investigations. For example, it is important for the comparison of biological systems to see if relationships between genomic features differ between cases and controls. In this paper, we seek to evaluate whether relationships between two sets of variables are different or not across two conditions. Specifically, we assess:

Indexed as

co-expressioncorrelationhigh-dimensional datakernel-methodsnon-linear dependencenonparametricspermutation null distribution

Identifiers

PMID39712486
PMCPMC11661859

What OpenQuestion holds

Textmetadata
LicenceTDM
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.