Evidence map›Paper›PMID 40081375›Full record

ArticleAmerican journal of human genetics2025

Artificial variables help to avoid over-clustering in single-cell RNA sequencing.

Alan DenAdel, Michelle L Ramseier, Andrew W Navia, Alex K Shalek, Srivatsan Raghavan, Peter S Winter, Ava P Amini, Lorin Crawford

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in American journal of human genetics, 2025. 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. Article
  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

8 authors.

Alan DenAdelCenter for Computational Molecular Biology, Brown University, Providence, RI 02912, USA.
Michelle L RamseierInstitute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA 02139, USA.
Andrew W NaviaBroad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Alex K ShalekInstitute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA 02139, USA.
Srivatsan RaghavanBroad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Harvard Medical School, Boston, MA 02115, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA.
Peter S WinterBroad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Ava P AminiMicrosoft Research, Cambridge, MA 02142, USA.
Lorin CrawfordMicrosoft Research, Cambridge, MA 02142, USA. Electronic address: lcrawford@microsoft.com.

Funding

Regulation and targeting of tumor cell states and plasticity in pancreatic cancerK08CA260442 · NCI · DANA-FARBER CANCER INST · PI Srivatsan Raghavan · 2022 to 2026
$1.5M
NCI NIH HHS K08 CA260442
6 · The paper itself

Abstract

Standard single-cell RNA sequencing (scRNA-seq) pipelines nearly always include unsupervised clustering as a key step in identifying biologically distinct cell types. A follow-up step in these pipelines is to test for differential expression between the identified clusters. When algorithms over-cluster, downstream analyses can produce misleading results. In this work, we present "recall" (calibrated clustering with artificial variables), a method for protecting against over-clustering by controlling for the impact of reusing the same data twice when performing differential expression analysis, commonly known as "double dipping." Importantly, our approach can be applied to a wide range of clustering algorithms. Using real and simulated data, we show that recall provides state-of-the-art clustering performance and can rapidly analyze large-scale scRNA-seq studies, even on a personal laptop.

Indexed as

RNA-SeqSequence Analysis, RNASingle-Cell AnalysisAlgorithmsCluster AnalysisGene Expression ProfilingHumanscalibrationclusteringdifferential expression analysisdouble dippingmachine learningsingle cell

Identifiers

PMID40081375
PMCPMC12081238

What OpenQuestion holds

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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.