Evidence map›Paper›PMID 37546812›Full record

ArticlebioRxiv : the preprint server for biology2024

Synthetic control removes spurious discoveries from double dipping in single-cell and spatial transcriptomics data analyses.

Dongyuan Song, Siqi Chen, Christy Lee, Kexin Li, Xinzhou Ge, Jingyi Jessica Li

Open access · greenAbstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. 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, 17 citations in OpenAlex.

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

6 authors at 3 institutions in 1 country.

Dongyuan SongDepartment of Genetics and Genome Sciences, University of Connecticut Health Center, Farmington, CT 06030.ORCID 0000-0003-1114-1215
Siqi ChenDepartment of Statistics and Data Science, University of California, Los Angeles, CA 90095-1554.ORCID 0000-0002-4955-3832
Christy LeeDepartment of Statistics and Data Science, University of California, Los Angeles, CA 90095-1554.ORCID 0009-0002-6803-8948
Kexin LiDepartment of Statistics and Data Science, University of California, Los Angeles, CA 90095-1554.ORCID 0000-0002-4955-3832
Xinzhou GeDepartment of Statistics and Data Science, University of California, Los Angeles, CA 90095-1554.ORCID 0000-0002-8229-5716
Jingyi Jessica LiInterdepartmental Program of Bioinformatics, University of California, Los Angeles, CA 90095-7246.ORCID 0000-0002-9288-5648
University of California, Los Angeles · USHarvard University · USOregon State University · US

Funding

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
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
NHGRI NIH HHS R01 HG014687NIGMS NIH HHS R01 GM120507NIGMS NIH HHS R35 GM140888
6 · The paper itself

Abstract

Double dipping is a well-known pitfall in single-cell and spatial transcriptomics data analysis: after a clustering algorithm finds clusters as putative cell types or spatial domains, statistical tests are applied to the same data to identify differentially expressed (DE) genes as potential cell-type or spatial-domain markers. Because the genes that contribute to clustering are inherently likely to be identified as DE genes, double dipping can result in false-positive cell-type or spatial-domain markers, especially when clusters are spurious, leading to ambiguously defined cell types or spatial domains. To address this challenge, we propose ClusterDE, a statistical method designed to identify post-clustering DE genes as reliable markers of cell types and spatial domains, while controlling the false discovery rate (FDR) regardless of clustering quality. The core of ClusterDE involves generating synthetic null data as an

Indexed as

ClusteringDifferential Expression AnalysisMarker Gene IdentificationSingle-cell RNA-seqSpatial Transcriptomics

Identifiers

PMID37546812
PMCPMC10401959
OpenAlexW4385251842

What OpenQuestion 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 OpenQuestion graph.