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 Concept Videos

You might also read

Related Articles

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

Sort by
Same author

MRD-negativity by PBMCs and ctDNA confirms deep and durable responses following epcoritamab monotherapy in R/R FL.

Blood advances·2026
Same author

Early postnatal DNA methylation dynamics define neuronal subtypes and are disrupted by MECP2 loss.

bioRxiv : the preprint server for biology·2026
Same author

Prognostic factors and survival outcomes in relapsed/refractory aggressive B-cell lymphomas treated with epcoritamab.

Blood advances·2026
Same author

Cyclin-Dependent Kinase-9 and Oxidative Phosphorylation Inhibition Overcomes Ibrutinib Resistance in Mantle Cell Lymphoma.

Cancer research communications·2026
Same author

Cell fusion reprograms tumor cells and promotes RUNX1-mediated invasion and dissemination in colorectal cancer.

bioRxiv : the preprint server for biology·2026
Same author

scSurvival: Single-Cell Survival Analysis of Clinical Cancer Cohort Data at Cellular Resolution.

Cancer discovery·2026

Related Experiment Video

Updated: Aug 5, 2025

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations
09:34

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

6.7K

Supervised learning of high-confidence phenotypic subpopulations from single-cell data.

Tao Ren1,2, Canping Chen3,4, Alexey V Danilov5

  • 1Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China.

Biorxiv : the Preprint Server for Biology
|March 30, 2023
PubMed
Summary

PENCIL, a new supervised learning tool, precisely identifies cell subpopulations linked to specific phenotypes in single-cell data. It simultaneously selects genes and predicts cell trajectory changes, advancing biological and clinical research.

Keywords:
feature selectionlearning with rejectionphenotype-associated subpopulationsingle-cell data

More Related Videos

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells
11:26

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Published on: May 22, 2017

13.9K
Reconstruction of Single-Cell Innate Fluorescence Signatures by Confocal Microscopy
07:29

Reconstruction of Single-Cell Innate Fluorescence Signatures by Confocal Microscopy

Published on: May 27, 2020

2.8K

Related Experiment Videos

Last Updated: Aug 5, 2025

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations
09:34

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

6.7K
Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells
11:26

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Published on: May 22, 2017

13.9K
Reconstruction of Single-Cell Innate Fluorescence Signatures by Confocal Microscopy
07:29

Reconstruction of Single-Cell Innate Fluorescence Signatures by Confocal Microscopy

Published on: May 27, 2020

2.8K

Area of Science:

  • Single-cell genomics
  • Computational biology
  • Biomedical data analysis

Background:

  • Identifying phenotype-relevant cell subsets is critical for understanding biological and clinical phenotypes.
  • Existing methods struggle to simultaneously select informative genes and identify cell subpopulations.

Approach:

  • Developed PENCIL, a novel supervised learning framework using a learning with rejection strategy.
  • Integrated a feature selection function for simultaneous gene selection and subpopulation identification.
  • Incorporated a regression mode for supervised phenotypic trajectory learning.

Key Points:

  • PENCIL accurately identifies phenotypic subpopulations missed by other methods.
  • Enables simultaneous gene selection and cell subpopulation identification.
  • Regression mode facilitates supervised phenotypic trajectory learning from single-cell data.

Conclusions:

  • PENCIL offers a scalable and flexible infrastructure for phenotype-associated subpopulation identification.
  • Demonstrated utility in identifying T-cell subpopulations in melanoma immunotherapy.
  • Revealed drug treatment response trajectories in mantle cell lymphoma using scRNA-seq data.