Hypothesis testing for differentially correlated features.

Elisa Sheng, Daniela Witten, Xiao-Hua Zhou

Abstract read

In one paragraph

Article in Biostatistics (Oxford, England), 2016. 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.

Feature Optimization Method of Material Identification for Loose Particles Inside Sealed Relays.Sensors (Basel, Switzerland) · 2022
Article
A TESTING BASED APPROACH TO THE DISCOVERY OF DIFFERENTIALLY CORRELATED VARIABLE SETS.The annals of applied statistics · 2018
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

3 authors.

Elisa ShengDepartment of Biostatistics, University of Washington, Seattle, WA, USA.

Daniela WittenDepartment of Biostatistics, University of Washington, Seattle, Washington, USA and Department of Statistics, University of Washington, Seattle, Washington, USA.

Xiao-Hua ZhouDepartment of Biostatistics, University of Washington, Seattle, Washington, USA and Veterans Affairs Seattle Medical Center, Seattle, Washington, USA azhou@uw.edu.

Funding

High-dimensional unsupervised learning, with applications to genomicsDP5OD009145 · OD · UNIVERSITY OF WASHINGTON · PI WITTEN, DANIELA · 2011 to 2015

$1.8M

NIH HHS DP5 OD009145

6 · The paper itself

Abstract

In a multivariate setting, we consider the task of identifying features whose correlations with the other features differ across conditions. Such correlation shifts may occur independently of mean shifts, or differences in the means of the individual features across conditions. Previous approaches for detecting correlation shifts consider features simultaneously, by computing a correlation-based test statistic for each feature. However, since correlations involve two features, such approaches do not lend themselves to identifying which feature is the culprit. In this article, we instead consider a serial testing approach, by comparing columns of the sample correlation matrix across two conditions, and removing one feature at a time. Our method provides a novel perspective and favorable empirical results compared with competing approaches.

Indexed as

Data Interpretation, StatisticalModels, TheoreticalResearch DesignBiostatisticsHumansCorrelation matrixDifferential correlationFeature selectionHypothesis testingWald test

Identifiers

PMID27044327

DOI10.1093/biostatistics/kxw013

PMCPMC5031944

What OpenQuestion holds

Textmetadata

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.