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

9.9K
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...
9.9K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

47
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
47

You might also read

Related Articles

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

Sort by
Same author

Exploring Complex Genetic Mechanisms in Brain Imaging Genetics via a New Multi-task Learning Method.

IEEE transactions on computational biology and bioinformatics·2026
Same author

stDGCN: A dual-augmentation graph convolutional network for identifying spatial domains with attention mechanism.

IEEE journal of biomedical and health informatics·2026
Same author

MVCL: A Contrastive Learning Model with Multi-view Networks for Driver Gene Prediction.

IEEE journal of biomedical and health informatics·2026
Same author

SpaVGMC: A Unified Representation Learning Framework via Structural and Semantic Alignment in Spatial Transcriptomics.

Journal of chemical information and modeling·2026
Same author

MHNNMDA: multi-stage hypergraph neural network for predicting miRNA-disease association types.

Journal of computer-aided molecular design·2026
Same author

Prediction of multicategory miRNA-disease associations based on bidirectional hypergraph attention network and gated convolutional strategy.

Journal of computer-aided molecular design·2026

Related Experiment Video

Updated: Jun 19, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.0K

MLRR-ATV: A Robust Manifold Nonnegative Low-Rank Representation With Adaptive Total-Variation Regularization for

Gao-Fei Wang, Juan Wang, Shasha Yuan

    IEEE Transactions on Computational Biology and Bioinformatics
    |July 24, 2024
    PubMed
    Summary

    A new single-cell clustering method, MLRR-ATV, effectively reduces noise in single-cell RNA sequencing data. This robust approach improves gene expression analysis by preserving essential data structures.

    More Related Videos

    Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data
    07:35

    Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data

    Published on: December 1, 2023

    637
    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 19, 2025

    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
    12:27

    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

    Published on: February 15, 2017

    7.0K
    Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data
    07:35

    Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data

    Published on: December 1, 2023

    637
    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:

    • Genomics
    • Bioinformatics
    • Computational Biology

    Background:

    • Single-cell RNA sequencing (scRNA-seq) enables gene expression analysis at the individual cell level.
    • scRNA-seq data is characterized by high dimensionality, sparsity, and significant noise due to technological limitations.
    • Clustering is a fundamental technique for analyzing scRNA-seq data to identify cell populations.

    Purpose of the Study:

    • To develop a novel and robust method for clustering scRNA-seq data.
    • To address the challenges of noise, high dimensionality, and sparsity in scRNA-seq datasets.
    • To improve the accuracy and reliability of cell type identification through advanced clustering.

    Main Methods:

    • Introduction of a novel Robust Manifold Nonnegative Low-Rank Representation with Adaptive Total-Variation Regularization (MLRR-ATV) method.
    • Integration of Adaptive Total-Variation (ATV) regularization within a Low-Rank Representation (LRR) framework to mitigate noise via gradient learning.
    • Incorporation of Euclidean distance and cosine similarity to capture both linear and nonlinear manifold structures within the data.
    • Utilization of the Alternating Direction Method of Multipliers (ADMM) for optimizing the non-convex MLRR-ATV model.

    Main Results:

    • The MLRR-ATV model demonstrated superior performance compared to nine state-of-the-art methods across eight real-world scRNA-seq datasets.
    • The method effectively reduced the influence of noise, preserving crucial biological information within the datasets.
    • Accurate identification of cell populations was achieved, highlighting the model's effectiveness in scRNA-seq data analysis.

    Conclusions:

    • MLRR-ATV offers a significant advancement in single-cell RNA sequencing data clustering.
    • The proposed method provides a robust and accurate solution for analyzing noisy, high-dimensional, and sparse single-cell data.
    • MLRR-ATV enhances the ability to explore gene expression and identify cell types, contributing to a deeper understanding of cellular heterogeneity.