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

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

504
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
504
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

16.7K
Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
16.7K
Genome Annotation and Assembly03:36

Genome Annotation and Assembly

22.0K
The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
22.0K
Genomics02:02

Genomics

41.8K
Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
41.8K

You might also read

Related Articles

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

Sort by
Same author

A 3D-printed self-propelled, highly sensitive mini-motor for underwater pesticide detection.

Talanta·2018
Same author

Integrated analysis of microarray data to identify the genes critical for the rupture of intracranial aneurysm.

Oncology letters·2018
Same author

Editorial: Recent Advances of Cell and Gene Therapy in Kidney Diseases.

Current gene therapy·2018
Same author

Photocatalytic Supramolecular Enantiodifferentiating Dimerization of 2-Anthracenecarboxylic Acid through Triplet-Triplet Annihilation.

Organic letters·2018
Same author

On the realization of acoustic attenuation using a microperforated panel alone.

The Journal of the Acoustical Society of America·2018
Same author

Advances in covalent organic frameworks in separation science.

Journal of chromatography. A·2018
Same journal

A Multi-Modal Framework for Phage-Host Interaction Prediction Using Multi-View Contrastive Learning.

IEEE transactions on computational biology and bioinformatics·2026
Same journal

Decoding Gene-Disease Associations with Computational Methods: A Survey.

IEEE transactions on computational biology and bioinformatics·2026
Same journal

A Competitive Coevolution-based Cancer Driver Pathway Identification Algorithm for Maximizing Coverage, Mutual Exclusivity, and Subnet Importance.

IEEE transactions on computational biology and bioinformatics·2026
Same journal

Prediction of GO Terms Based on Partitioning PPI Networks into Highly Connected Components.

IEEE transactions on computational biology and bioinformatics·2026
Same journal

Modeling and Tracking of Heterogeneous Cell Populations via Open Multi-Agent Systems.

IEEE transactions on computational biology and bioinformatics·2026
Same journal

Parameter Efficient Deep Learning Models for Multi-Target Binding Affinity and hERG Cardiotoxicity Prediction.

IEEE transactions on computational biology and bioinformatics·2026
See all related articles
  1. Home
  3. Scmsac Assigns Single-cell Multi-omics Data At The Multi-modal Cluster Via Subgraph Attention Autoencoder.
  1. Home
  3. Scmsac Assigns Single-cell Multi-omics Data At The Multi-modal Cluster Via Subgraph Attention Autoencoder.

Related Experiment Video

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

2.0K

scMSAC Assigns Single-Cell Multi-Omics Data at the Multi-Modal Cluster via Subgraph Attention Autoencoder.

Jing Wang, Cheng Yang, Weijie Cai

    IEEE Transactions on Computational Biology and Bioinformatics
    |April 2, 2026

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    We developed scMSAC, a novel method for single-cell multi-omics data clustering. It effectively integrates diverse omics data, improving cell state analysis and rare cell type detection.

    More Related Videos

    Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
    08:45

    Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

    Published on: October 24, 2012

    15.3K
    Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq
    06:24

    Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

    Published on: March 12, 2021

    4.3K

    Related Experiment Videos

    Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
    09:47

    Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

    Published on: December 15, 2023

    2.0K
    Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
    08:45

    Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

    Published on: October 24, 2012

    15.3K
    Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq
    06:24

    Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

    Published on: March 12, 2021

    4.3K

    Area of Science:

    • Genomics
    • Bioinformatics
    • Computational Biology

    Background:

    • Single-cell multi-omics sequencing allows simultaneous measurement of multiple data types from individual cells.
    • Joint clustering of this data is crucial for understanding cell states and molecular mechanisms in oncology, neurology, and developmental biology.
    • Challenges include feature space disparities and data noise, hindering accurate clustering.

    Purpose of the Study:

    • To introduce scMSAC, a novel clustering method for single-cell multi-omics data.
    • To address challenges posed by feature space differences and data noise in multi-omics data integration.
    • To improve the accuracy and comprehensiveness of cell state depiction and molecular mechanism discovery.

    Main Methods:

    • scMSAC utilizes a denoising subgraph attention autoencoder for clustering single-cell multi-omics data.
  • A weighted nearest neighbor graph strategy assigns weights to multi-omics data, creating a comprehensive similarity graph.
  • Spatial Channel Attention (SCA) mechanism fuses omics features, reducing discrepancies and enhancing clustering.

    • Main Results:

    • scMSAC demonstrates excellent clustering performance compared to existing methods.
    • The method excels in identifying rare cell types.
    • scMSAC performs effectively in differential expression analysis.

    Conclusions:

    • scMSAC offers a robust approach for single-cell multi-omics data clustering.
    • The method successfully integrates diverse omics data, overcoming feature space challenges.
    • scMSAC provides significant advancements for biological research, particularly in oncology and neurology.