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

RNA-seq03:21

RNA-seq

10.4K
RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
10.4K
DNA Microarrays02:34

DNA Microarrays

18.8K
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...
18.8K
Ribosome Profiling02:24

Ribosome Profiling

3.7K
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...
3.7K

You might also read

Related Articles

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

Sort by
Same author

Genomic determinants and an exploratory prognostic model for immunotherapy outcomes in recurrent or metastatic cervical cancer.

The oncologist·2026
Same author

Global Marine LNG Terminals, Tankers & Trade: A High-Resolution AIS-Based Dataset of LNG Trade (2020-2024).

Scientific data·2026
Same author

Hypoxia‑induced exosomal CAMTA1 promotes radio‑resistance in MDA‑MB‑231 cells by regulating NRG1 to mediate M2 macrophage polarization.

International journal of oncology·2026
Same author

Shared genetic and neuroimmune architecture links type 1 diabetes with neurocognitive traits.

Nature communications·2026
Same author

Abyssal hydrothermal alteration drives the evolution from simple alkanes to prebiotic molecular complexity.

Nature communications·2026
Same author

A near-real time daily European Power Consumption and Carbon Intensity Dataset (ECON-PowerCI).

Scientific data·2025

Related Experiment Video

Updated: Sep 24, 2025

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

38.6K

A Markov random field model-based approach for differentially expressed gene detection from single-cell RNA-seq data.

Biqing Zhu1, Hongyu Li2, Le Zhang3

  • 1Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06511, USA.

Briefings in Bioinformatics
|May 6, 2022
PubMed
Summary

MARBLES, a new statistical model, enhances the detection of cell-type-specific differential expression (DE) in single-cell RNA sequencing (scRNA-seq) data. It improves accuracy and identifies novel disease-related genes and pathways.

Keywords:
Markov random fieldParkinson’s diseasedifferential expressionpseudobulkscRNA-seq

More Related Videos

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

3.7K
Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis
12:44

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Published on: November 11, 2014

12.4K

Related Experiment Videos

Last Updated: Sep 24, 2025

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

38.6K
Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

3.7K
Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis
12:44

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Published on: November 11, 2014

12.4K

Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • Single-cell RNA sequencing (scRNA-seq) provides high-resolution insights into biological systems.
  • Identifying cell-type-specific differential gene expression (DE) is crucial for understanding cellular functions and disease mechanisms.

Purpose of the Study:

  • Introduce MARBLES, a novel statistical model for detecting DE genes across conditions using scRNA-seq data.
  • Improve the power and accuracy of DE gene detection compared to existing methods.

Main Methods:

  • MARBLES utilizes a Markov Random Field model to integrate information across similar cell types.
  • It incorporates cell-type-specific pseudobulk counts to manage sample-level variability.
  • The model was evaluated using simulations and applied to real scRNA-seq datasets.

Main Results:

  • MARBLES demonstrated superior power in detecting DE genes while maintaining a controlled false positive rate in simulations.
  • Application to mouse and human datasets identified novel disease-related DE genes and biological pathways.
  • The model effectively analyzed large-scale scRNA-seq data from diverse biological conditions.

Conclusions:

  • MARBLES is a powerful and effective tool for identifying cell-type-specific DE genes from scRNA-seq data.
  • The model advances the analysis of complex biological systems and disease-related gene expression patterns.