Jove
Visualize
Contact Us

Related Concept Videos

Cell Specific Gene Expression01:58

Cell Specific Gene Expression

13.3K
Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
13.3K
Cell Specific Gene Expression01:58

Cell Specific Gene Expression

4.9K
4.9K
Ribosome Profiling02:24

Ribosome Profiling

3.2K
Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...
3.2K
DNA Microarrays02:34

DNA Microarrays

16.8K
Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
16.8K

You might also read

Related Articles

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

Sort by
Same author

GhCHX6A and GhKEA1D Enhance Salt Tolerance by Regulating Na<sup>+</sup>/K<sup>+</sup> Homeostasis in Cotton.

Physiologia plantarum·2026
Same author

Formation and Stability of Pickering Emulsion Stabilized by Self-Aggregated Chitosan Particles near the pK<sub>a</sub>.

ACS omega·2026
Same author

Bridging unpaired single-cell multimodal data for integrative analyses with SuperMap.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Mouse lemur cell atlas informs primate genes, physiology and disease.

Nature·2025
Same author

A molecular cell atlas of mouse lemur, an emerging model primate.

Nature·2025
Same author

Distance weighted directional regression for Fréchet sufficient dimension reduction.

Biometrics·2025
Same journal

K-attention: a biologically informed attention operator for data-efficient sequence-based omics modeling.

Briefings in bioinformatics·2026
Same journal

Accurate prediction of asparagine deamidation in biologics using advanced machine learning models.

Briefings in bioinformatics·2026
Same journal

scImmuneCo: a compendium of cell-type-specific functional modules for decoding immune responses from single-cell RNA-seq data.

Briefings in bioinformatics·2026
Same journal

scGenoByte: a GenoByte embedding transformer with biological priors for cell type annotation.

Briefings in bioinformatics·2026
Same journal

FerroScore: a statistical approach for quantifying tumor-related ferroptosis based on omics data.

Briefings in bioinformatics·2026
Same journal

METEOR: a data-adaptive Mendelian randomization method for powerful detection of shared and specific exposures underlying multiple outcomes.

Briefings in bioinformatics·2026
See all related articles
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: May 1, 2026

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

921

Integrating and mapping single-cell transcriptomics across the entire gene expression space.

Shuzhen Ding1, Xintong Zhai1, Zhou Yu1

  • 1KLATASDS-MOE, School of Statistics, East China Normal University, 3663 North Zhongshan Road, Shanghai, 200062, China.

Briefings in Bioinformatics
|April 30, 2026
PubMed
Summary
This summary is machine-generated.

Single-cell transcriptomics integration faces batch effect challenges. Our novel deep learning framework, scGES, corrects these effects across all genes, improving data analysis and biological insights.

Keywords:
data integration and mappingdeep learninggene expression denoisingscRNA-seq

More Related Videos

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

6.0K
Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

1.0K

Related Experiment Videos

Last Updated: May 1, 2026

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

921
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

6.0K
Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

1.0K

Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Single-cell transcriptomics data is growing exponentially, necessitating integration for large-scale atlases.
  • Batch effects introduce biases, hindering accurate data integration and masking biological signals.
  • Current methods often focus on highly variable genes, potentially missing information in lowly variable genes.

Purpose of the Study:

  • To introduce scGES, a novel deep learning framework for effective batch effect correction in single-cell transcriptomics.
  • To address limitations of existing methods by utilizing information from both highly and lowly variable genes.
  • To improve the construction of single-cell reference atlases and mapping of query datasets.

Main Methods:

  • Developed scGES, a deep learning framework with two models: scGESI for integration and scGESM for mapping.
  • Leveraged information from the entire gene expression space, including highly and lowly variable genes.
  • Applied comprehensive analyses to real-world single-cell transcriptomics datasets.

Main Results:

  • scGES demonstrated superior performance in batch effect correction compared to state-of-the-art methods.
  • The framework effectively conserved biological variations within the datasets.
  • Utilizing all genes, including lowly variable ones, enhanced downstream analyses and biological insights.

Conclusions:

  • scGES offers a robust solution for batch effect correction in single-cell transcriptomics.
  • The framework's ability to integrate information from all genes provides broader biological insights.
  • scGES advances the construction and utilization of large-scale single-cell reference atlases.