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 Experiment Video

Updated: Aug 4, 2025

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

18.6K

Clustering Single-Cell RNA Sequence Data Using Information Maximized and Noise-Invariant Representations.

Arnab Kumar Mondal, Indu Joshi, Pravendra Singh

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |April 4, 2023
    PubMed
    Summary
    This summary is machine-generated.

    Related Concept Videos

    RNA-seq03:21

    RNA-seq

    10.2K
    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...
    10.2K

    You might also read

    Related Articles

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

    Sort by
    Same author

    Dual-view diffusion for pedestrian trajectory imputation.

    Neural networks : the official journal of the International Neural Network Society·2026
    Same author

    Integrating orthogonal supervision for sparse semi-supervised 3D medical image segmentation.

    Neural networks : the official journal of the International Neural Network Society·2026
    Same author

    Learning with less: A survey of deep learning in medical imaging under varying supervision levels.

    Artificial intelligence in medicine·2026
    Same author

    Tubercular Palmar Ganglion Presenting as a Severe Carpal Tunnel Syndrome - A Case Report.

    Journal of orthopaedic case reports·2025
    Same author

    Synthetic Data in Human Analysis: A Survey.

    IEEE transactions on pattern analysis and machine intelligence·2024
    Same author

    Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives.

    Computers in biology and medicine·2024
    Same journal

    circ2DGNN: circRNA-Disease Association Prediction via Transformer-Based Graph Neural Network.

    IEEE/ACM transactions on computational biology and bioinformatics·2024
    Same journal

    Hierarchical Hypergraph Learning in Association- Weighted Heterogeneous Network for miRNA- Disease Association Identification.

    IEEE/ACM transactions on computational biology and bioinformatics·2024
    Same journal

    Discriminative Domain Adaption Network for Simultaneously Removing Batch Effects and Annotating Cell Types in Single-Cell RNA-Seq.

    IEEE/ACM transactions on computational biology and bioinformatics·2024
    Same journal

    MLW-BFECF: A Multi-Weighted Dynamic Cascade Forest Based on Bilinear Feature Extraction for Predicting the Stage of Kidney Renal Clear Cell Carcinoma on Multi-Modal Gene Data.

    IEEE/ACM transactions on computational biology and bioinformatics·2024
    Same journal

    An End-to-End Knowledge Graph Fused Graph Neural Network for Accurate Protein-Protein Interactions Prediction.

    IEEE/ACM transactions on computational biology and bioinformatics·2024
    Same journal

    Generative Biomedical Event Extraction With Constrained Decoding Strategy.

    IEEE/ACM transactions on computational biology and bioinformatics·2024
    See all related articles

    This study introduces sc-INDC, a novel deep learning method for single-cell RNA sequencing (scRNA-seq) data analysis. sc-INDC effectively handles data noise and sparsity, offering improved clustering performance and efficiency.

    Area of Science:

    • Computational Biology
    • Genomics
    • Bioinformatics

    Background:

    • Single-cell RNA sequencing (scRNA-seq) provides high-resolution transcriptome data but faces challenges from high dimensionality, sparsity, and noise.
    • Existing clustering methods struggle with these data characteristics, limiting the accuracy of cell type identification and biological insights.
    • Dropout events, common in scRNA-seq, exacerbate data sparsity and complicate downstream analysis.

    Purpose of the Study:

    • To develop a novel deep clustering method for scRNA-seq data that overcomes computational challenges.
    • To learn informative and noise-invariant representations from scRNA-seq data.
    • To improve the efficiency and accuracy of scRNA-seq data clustering.

    Main Methods:

    • Proposes sc-INDC (Single-Cell Information Maximized Noise-Invariant Deep Clustering), a deep neural network architecture.

    More Related Videos

    Low-input Nucleus Isolation and Multiplexing with Barcoded Antibodies of Mouse Sympathetic Ganglia for Single-nucleus RNA Sequencing
    10:44

    Low-input Nucleus Isolation and Multiplexing with Barcoded Antibodies of Mouse Sympathetic Ganglia for Single-nucleus RNA Sequencing

    Published on: March 23, 2022

    4.3K
    Author Spotlight: Deciphering the Cellular Mysteries of Intermuscular Adipose Tissue in Humans
    05:59

    Author Spotlight: Deciphering the Cellular Mysteries of Intermuscular Adipose Tissue in Humans

    Published on: May 3, 2024

    767

    Related Experiment Videos

    Last Updated: Aug 4, 2025

    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

    18.6K
    Low-input Nucleus Isolation and Multiplexing with Barcoded Antibodies of Mouse Sympathetic Ganglia for Single-nucleus RNA Sequencing
    10:44

    Low-input Nucleus Isolation and Multiplexing with Barcoded Antibodies of Mouse Sympathetic Ganglia for Single-nucleus RNA Sequencing

    Published on: March 23, 2022

    4.3K
    Author Spotlight: Deciphering the Cellular Mysteries of Intermuscular Adipose Tissue in Humans
    05:59

    Author Spotlight: Deciphering the Cellular Mysteries of Intermuscular Adipose Tissue in Humans

    Published on: May 3, 2024

    767
  • Employs information maximization and noise-invariant learning principles to extract robust features.
  • Designed for efficient representation learning with significantly lower time complexity compared to existing methods.

    • Main Results:

    • Demonstrates superior clustering performance across fourteen diverse scRNA-seq datasets.
    • Achieves effective noise reduction and enhances the quality of learned data representations.
    • t-SNE visualizations and ablation studies confirm the model's improved representation ability.

    Conclusions:

    • sc-INDC offers a powerful and efficient solution for scRNA-seq data clustering.
    • The method effectively addresses noise and sparsity, leading to more accurate biological interpretations.
    • The proposed approach advances the field of single-cell data analysis and computational biology.