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

Prevalence and Clinical Significance of Adult-Onset Cancer Predisposition Variants in Pediatric Oncology.

medRxiv : the preprint server for health sciences·2026
Same author

Gut Microbiota-derived Acetate Safeguards the Colonic Epithelial Acetyl-CoA Reserve to Avert Colonic Senescence.

bioRxiv : the preprint server for biology·2026
Same author

Protein domain characterization reveals human MIC60 tolerates loss of helical bundle domain.

bioRxiv : the preprint server for biology·2026
Same author

m <sup>6</sup> A-dependent microRNA binding to chromatin-associated RNA for transcriptional activation.

bioRxiv : the preprint server for biology·2026
Same author

Crop-OCT: a Fully Integrated Imageomics Pipeline to Identify Regional and Focal Retinopathy in Murine Models.

bioRxiv : the preprint server for biology·2026
Same author

Introducing iCatalog as a clinical decision support tool for collaborative pediatric precision oncology studies.

Communications medicine·2026

Related Experiment Video

Updated: Jun 24, 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

The curses of performing differential expression analysis using single-cell data.

Chih-Hsuan Wu, Xiang Zhou, Mengjie Chen

    Biorxiv : the Preprint Server for Biology
    |June 10, 2024
    PubMed
    Summary

    Single-cell differential expression analysis faces challenges like normalization and zero inflation. This study introduces a new method to overcome these common issues in transcriptomics research.

    More Related Videos

    Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
    10:10

    Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

    Published on: September 18, 2021

    37.2K
    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

    18.5K

    Related Experiment Videos

    Last Updated: Jun 24, 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
    Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
    10:10

    Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

    Published on: September 18, 2021

    37.2K
    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

    18.5K

    Area of Science:

    • Computational Biology
    • Genomics
    • Bioinformatics

    Background:

    • Differential expression (DE) analysis is crucial for single-cell transcriptomics.
    • Existing DE methods show performance limitations, especially with complex single-cell data.
    • Key challenges include normalization, excessive zeros, donor effects, and cumulative biases.

    Purpose of the Study:

    • To identify and analyze the major challenges in single-cell differential expression analysis.
    • To introduce a novel computational paradigm to address these limitations.

    Main Methods:

    • Dissection of four critical issues in single-cell DE analysis: normalization, zero inflation, donor effects, and cumulative biases.
    • Development of a new analytical framework designed to mitigate these identified problems.

    Main Results:

    • The study highlights significant shortcomings in current single-cell DE analysis workflows.
    • The proposed novel paradigm offers a potential solution to enhance the accuracy and reliability of DE analysis in single-cell studies.

    Conclusions:

    • Existing single-cell DE analysis methods are hampered by inherent challenges.
    • The novel approach presented aims to improve the understanding of cell-type-specific responses in transcriptomics.