Beyond differential expression: Differential variability as an underappreciated axis in transcriptomics.

Chenxin Flora Jiang, Changhu Wang, Jingyi Jessica Li

Abstract read

In one paragraph

Article in Cell systems, 2026. 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

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.

Chenxin Flora JiangDepartment of Statistics and Data Science, University of California, Los Angeles, Los Angeles, CA 90095, USA.

Changhu WangBiostatistics Program, Public Health Science Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA.

Jingyi Jessica LiDepartment of Statistics and Data Science, University of California, Los Angeles, Los Angeles, CA 90095, USA; Biostatistics Program, Public Health Science Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA; Department of Biostatistics, University of Washington, Seattle, WA 98195, USA. Electronic address: lijy03@fredhutch.org.

Funding

Technology Development Coordinating CenterU24HG011735 · NHGRI · JACKSON LABORATORY · PI Mark D ADAMS · 2021 to 2026

$8.1M

Experimental-data-based in-silico data generation platform to improve the accuracy and reliability of single-cell and spatial omics data analysisR01HG014687 · NHGRI · FRED HUTCHINSON CANCER CENTER · PI LI, JINGYI JESSICA · 2025 to 2025

$2.4M

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

NHGRI NIH HHS R01 HG014687NHGRI NIH HHS U24 HG011735NIGMS NIH HHS R35 GM140888

6 · The paper itself

Abstract

Differential expression has long been the default lens for comparing transcriptomes between conditions, but mean shifts alone can miss important biological signals. Using a recent debate on memory-associated transcriptome changes, this commentary highlights differential variability as an underappreciated axis for interpreting transcriptome variation.

Indexed as

Gene Expression ProfilingTranscriptomeAnimalsGenetic VariationHumans

Identifiers

PMID42617588

DOI10.1016/j.cels.2026.101706

PMCPMC13533298

What OpenQuestion holds

Textmetadata

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Registered trials

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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.