Evidence map›Paper›PMID 41825449›Full record

ArticleCell reports methods2026

Scalable nonparametric clustering with unified marker gene selection for single-cell RNA-seq data.

Chibuikem Nwizu, Madeline Hughes, Michelle L Ramseier, Andrew W Navia, Alex K Shalek, Nicolo Fusi, Srivatsan Raghavan, Peter S Winter, Ava P Amini, Lorin Crawford

Abstract read
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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.

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0citing papers in PubMed
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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

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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

10 authors.

Chibuikem NwizuCenter for Computational Molecular Biology, Brown University, Providence, RI 02906, USA; Warren Alpert Medical School of Brown University, Providence, RI 02906, USA.
Madeline HughesMicrosoft Research, Cambridge, MA 02142, USA.
Michelle L RamseierBroad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, 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 ShalekBroad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, 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.
Nicolo FusiMicrosoft Research, Cambridge, MA 02142, 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. Electronic address: ava.amini@microsoft.com.
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

Clustering is commonly used in single-cell RNA sequencing (scRNA-seq) to assess cellular heterogeneity, but standard methods often require user-specified heuristics and rely on post-selective differential expression analyses, which often lead to inflated false discovery rates. Here, we present NCLUSION: a nonparametric infinite mixture model that leverages Bayesian sparse priors to identify marker genes and cluster single-cell expression data simultaneously. NCLUSION uses a variational inference algorithm, which enables it to scale up to millions of cells. Through simulations and analyses of publicly available scRNA-seq studies, we demonstrate that NCLUSION (1) matches the performance of other state-of-the-art clustering techniques with significantly reduced runtime and (2) provides statistically robust and biologically relevant transcriptomic signatures for each of the clusters it identifies. Overall, NCLUSION represents a reliable hypothesis-generating tool for understanding patterns of expression variation present in single-cell populations.

Indexed as

RNA-SeqSequence Analysis, RNASingle-Cell AnalysisAlgorithmsAnimalsBayes TheoremCluster AnalysisClustering AlgorithmsGene Expression ProfilingGenetic MarkersHumansSingle-Cell Gene Expression AnalysisGenetic MarkersclusteringCP: computational biologyCP: systems biologymachine learningmarker gene selectionnonparametricvariational inference

Identifiers

PMID41825449
PMCPMC13030991

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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.