Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Mar 31, 2026

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
10:12

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

19.2K

CFMF: A Clustering-Free Cell Marker Finder for Single-Cell Transcriptomic Data.

Shumin Yin1, Ke Liu1

  • 1Department of Medical Dataology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China.

IET Systems Biology
|March 30, 2026
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A twofold quantum delayed-choice experiment in a superconducting circuit.

Science advances·2017
Same author

Acteoside and Acyl-Migrated Acteoside, Compounds in Chinese Kudingcha Tea, Inhibit α-Amylase In Vitro.

Journal of medicinal food·2017
Same author

Mg<sup>2+</sup>-induced stabilization of β-galactosidase from Bacillus megaterium and its application in the galactosylation of natural products.

Biotechnology letters·2017
Same author

Selection and Assessment of Plant Growth-Promoting Rhizobacteria for Biological Control of Multiple Plant Diseases.

Phytopathology·2017
Same author

Hearing loss and auditory plasticity.

Hearing research·2017
Same author

The woven fiber organic electrochemical transistors based on polypyrrole nanowires/reduced graphene oxide composites for glucose sensing.

Biosensors & bioelectronics·2017

Clustering-Free Cell Marker Finder (CFMF) discovers novel cell marker genes without traditional clustering. This computational framework enhances rare cell detection and tumor heterogeneity analysis in single-cell RNA sequencing (scRNA-seq) studies.

Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • Single-cell RNA sequencing (scRNA-seq) is crucial for identifying novel cell types and marker genes.
  • Conventional methods often rely on subjective and complex cell clustering parameter selection.
  • There is a need for robust computational tools to overcome these limitations.

Purpose of the Study:

  • To develop a novel computational framework, Clustering-Free Cell Marker Finder (CFMF), for marker gene discovery without cell clustering.
  • To validate CFMF's efficacy in identifying both canonical and novel marker genes across various biological datasets.
  • To assess CFMF's performance in detecting rare cell types and analyzing tumor heterogeneity.

Main Methods:

  • Developed the Clustering-Free Cell Marker Finder (CFMF) computational framework.
Keywords:
marker gene identificationrare cell detectionsingle‐cell RNA sequencingtumor heterogeneity

More Related Videos

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq
10:22

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq

Published on: October 31, 2025

817
Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets
11:34

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Published on: July 18, 2019

17.3K

Related Experiment Videos

Last Updated: Mar 31, 2026

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
10:12

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

19.2K
Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq
10:22

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq

Published on: October 31, 2025

817
Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets
11:34

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Published on: July 18, 2019

17.3K
  • Validated CFMF using PBMC3K, glioblastoma, and human normal lung scRNA-seq datasets.
  • Performed integrative analysis on colorectal cancer scRNA-seq datasets.
  • Systematically benchmarked CFMF against existing gene selection methods.

    • Main Results:

    • CFMF successfully identified canonical and novel marker genes in the PBMC3K dataset.
    • CFMF recapitulated established cellular state signatures in glioblastoma datasets.
    • Demonstrated superior sensitivity in detecting rare cell types in human normal lung datasets.
    • Defined seven distinct transcriptional states in colorectal cancer, including a metastasis-associated LGR5+ state.
    • CFMF outperformed widely used methods in identifying biologically meaningful genes.

    Conclusions:

    • CFMF provides a powerful, clustering-free approach for marker gene discovery in scRNA-seq data.
    • The framework enhances the identification of novel cell types and transcriptional states.
    • CFMF is effective for analyzing tumor heterogeneity and understanding complex biological systems.