Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Phiclust: a clusterability measure for single-cell transcriptomics reveals phenotypic subpopulations.

Genome biology - 10 Jan 2022

Mircea Maria, Hochane Mazène, Fan Xueying, Chuva de Sousa Lopes Susana M, Garlaschelli Diego, Semrau Stefan

Abstract excerpt

The ability to discover new cell phenotypes by unsupervised clustering of single-cell transcriptomes has revolutionized biology. Currently, there is no principled way to decide whether a cluster of cells contains meaningful subpopulations that should be further resolved. Here, we present phiclust (ϕclust), a clusterability measure derived from random matrix theory that can be used to identify cell clusters with...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Cluster Analysis
Share to Gene Expression Profiling

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Phenotype

Share to Sequence Analysis, RNA

Share to Single-Cell Analysis

Share to Transcriptome

Source

Open in PubMed Open full text

Identifiers

PMID: 35012604
DOI: 10.1186/s13059-021-02590-x
PMCID: PMC8751334

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Phiclust: a clusterability measure for single-cell transcriptomics reveals phenotypic subpopulations.PMID 35012604Read ↗

MicroCellClust: mining rare and highly specific subpopulations from single-cell expression dataSemantic proximity Read ↗

Unsupervised multi-scale clustering of single-cell transcriptomes to identify hierarchical structures of cell subtypesSemantic proximity Read ↗

ASURAT: functional annotation-driven unsupervised clustering of single-cell transcriptomesSemantic proximity Read ↗

treekoR: identifying cellular-to-phenotype associations by elucidating hierarchical relationships in high-dimensional cytometry data.Semantic proximity Read ↗

Identifying cellular-to-phenotype associations by elucidating hierarchical relationships in high-dimensional cytometry dataSemantic proximity Read ↗

SciGeneX: Enhancing transcriptional analysis through gene module detection in single-cell and spatial transcriptomics dataSemantic proximity Read ↗

Resolving single-cell heterogeneity from hundreds of thousands of cells through sequential hybrid clustering and NMFSemantic proximity Read ↗

clusterExperiment and RSEC: A Bioconductor package and framework for clustering of single-cell and other large gene expression datasetsSemantic proximity Read ↗

MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data.Semantic proximity Read ↗

Identification of cell subpopulations associated with disease phenotypes from scRNA-seq data using PACSI.Semantic proximity Read ↗

Sensitive cluster-free differential expression testing.Semantic proximity Read ↗

Cluster similarity spectrum integration of single-cell genomics dataSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.