Differential expression analysis in single-cell and spatial RNA-seq without model assumptions.

Gennady Margolin, Andrew Tang, Sergey Leikin

Abstract read

In one paragraph

Article in Cell reports methods, 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

Update of
Differential expression analysis in single cell and spatial RNASeq without model assumptions.2025

5 · Who and what money

Authors and funding

3 authors.

Gennady MargolinEunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20892, USA.

Andrew TangEunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20892, USA.

Sergey LeikinEunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20892, USA. Electronic address: leikins@mail.nih.gov.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gene up(down)regulation findings in single-cell and spatial RNA sequencing can be inconsistent despite remarkable progress in technology. Inconsistent findings in high-quality samples raise concerns about assumptions behind widely accepted data analysis approaches. We, therefore, propose a weighted averaging approach for data analysis without assuming anything besides randomness of technical noise. This approach is closely related to prior work on statistics of cluster-randomized experiments. We show that weighing transcript counts based on measured noise variances and utilizing weighted rather than standard unweighted tests reduce both false-positive and false-negative findings. Our approach eliminates the need for parametrizing data distributions and/or rescaling transcript counts, which may cause artifacts by distorting and biasing the data. The resulting analysis is less complex and produces more consistent differential gene expression estimates.

Indexed as

Gene Expression ProfilingRNA-SeqSequence Analysis, RNASingle-Cell AnalysisAnimalsHumansSingle-Cell Gene Expression Analysiscluster-randomized experimentsCP: computational biologyCP: systems biologydifferential expressionscRNA-seqspatial RNA sequencingstatistical weightweighted averagingweighted t test

Identifiers

PMID41980775

DOI10.1016/j.crmeth.2026.101383

PMCPMC13198004

What Socratic holds

Textmetadata

LicenceCC BY-NC-ND

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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 Socratic graph.