Evidence map›Paper›PMID 39713302›Full record

ArticlebioRxiv : the preprint server for biology2025

FLEXIBILITY IN GENE COEXPRESSION AT DEVELOPMENTAL AND EVOLUTIONARY TIMESCALES.

Eva K Fischer, Youngseok Song, Wen Zhou, Kim L Hoke

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Eva K FischerDepartment of Neurobiology, Physiology and Behavior, University of California Davis, Davis, CA 95616, USA.ORCID 0000-0002-2916-0900
Youngseok SongDepartment of Statistics, West Virginia University, Morgantown, WV 26506, USA.ORCID 0000-0003-3577-7509
Wen ZhouDepartment of Biostatistics, School of Global Public Health, New York University, New York, NY 10003, USA.
Kim L HokeDepartment of Biology, Colorado State University, Fort Collins, CO 80523, USA.ORCID 0000-0001-5553-0138

Funding

Statistical methods for higher order dependences to understand protein functionsR01GM144961 · NIGMS · COLORADO STATE UNIVERSITY · PI ZHOU, WEN · 2021 to 2023
$600k
NIGMS NIH HHS R01 GM144961
6 · The paper itself

Abstract

The explosion of next-generation sequencing technologies has allowed researchers to move from studying single genes to thousands of genes, and thereby to also consider the relationships within gene networks. Like others, we are interested in understanding how developmental and evolutionary forces shape the expression of individual genes, as well as the interactions among genes. In pursuing these questions, we confronted the central challenge that standard approaches fail to control the Type I error and/or have low power in the presence of high dimensionality (i.e., large number of genes) and small sample size, as in many gene expression studies. To overcome these challenges, we used random projection tests and correlation network comparisons to characterize differences in network connectivity and density. We detail central challenges, discuss sample size guidelines, and provide rigorous statistical approaches for exploring coexpression differences with small sample sizes. We apply these approaches in a species known for rapid adaptation - the Trinidadian guppy (

Identifiers

PMID39713302
PMCPMC11661222

What Socratic 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 Socratic graph.