Evidence map›Paper›PMID 39697782›Full record

ArticleJournal of the American Statistical Association2024

Dissecting gene expression heterogeneity: generalized Pearson correlation squares and the

Jingyi Jessica Li, Heather J Zhou, Peter J Bickel, Xin Tong

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of the American Statistical Association, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Review
  3. 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

4 authors.

Jingyi Jessica LiDepartment of Statistics, University of California, Los Angeles.
Heather J ZhouDepartment of Statistics, University of California, Los Angeles.
Peter J BickelDepartment of Statistics, University of California, Berkeley.
Xin TongDepartment of Data Sciences and Operations, University of Southern California.

Funding

Statistical Methods for Elucidating Regulatory Mechanisms and Functional Impacts of Transcriptome Variation at Population and Single-Cell ScalesR35GM140888 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI LI, JINGYI JESSICA · 2021 to 2025
$2.0M
Robust Identification and accurate quantification of RNA transcripts on a system wide scaleR01GM120507 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI LI, JINGYI JESSICA · 2016 to 2021
$1.7M
NIGMS NIH HHS R01 GM120507NIGMS NIH HHS R35 GM140888
6 · The paper itself

Abstract

Motivated by the pressing needs for dissecting heterogeneous relationships in gene expression data, here we generalize the squared Pearson correlation to capture a mixture of linear dependences between two real-valued variables, with or without an index variable that specifies the line memberships. We construct the generalized Pearson correlation squares by focusing on three aspects: variable exchangeability, no parametric model assumptions, and inference of population-level parameters. To compute the generalized Pearson correlation square from a sample without a line-membership specification, we develop a

Indexed as

asymptotic distributionmixture of linear dependencesspecified and unspecified generalized Pearson correlation squares

Identifiers

PMID39697782
PMCPMC11651632

What OpenQuestion holds

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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.