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; Nicolò Fusi; Srivatsan Raghavan; Peter Winter; Ava P. Amini; Lorin Crawford

doi:10.1016/j.crmeth.2026.101329

March 1, 2026Cell Reports MethodsOpen Access

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

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Key Points

This research aims to develop a nonparametric clustering method for analyzing single-cell RNA-seq data without pre-defining cluster numbers.
Introduced the NCLUSION algorithm for clustering scRNA-seq data.
The method does not require a pre-specified number of clusters.
Simultaneously learns both cluster memberships and marker genes.
Demonstrated effective clustering performance on single-cell RNA-seq datasets.
Showed the algorithm's capability to identify meaningful biological clusters.

Implication

Nonparametric clustering analyzes single-cell RNA-seq data, highlighting unified marker gene selection in clusters.

Abstract

NCLUSION is a nonparametric clustering algorithm for scRNA-seq data analysis The method does not require users to specify the number of clusters a priori It jointly learns cluster-defining marker genes while learning cluster memberships

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Cite This Study

Nwizu et al. (2026) studied this question.

synapsesocial.com/papers/69b64c9ab42794e3e660dd38 https://doi.org/10.1016/j.crmeth.2026.101329

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

Key Points

Implication

Abstract

Discussion

Cite This Study

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

Key Points

Implication

Abstract

Discussion

Cite This Study

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