Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

RNA-seq

RNA-seq

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...

You might also read

Related Articles

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

Sort by

Same author

RSTG: Robust Generation of High Quality Spatial Transcriptomics Data using Beta Divergence Based AutoEncoder.

IEEE journal of biomedical and health informatics·2026

Same author

MutAIverse: an AI-powered, mechanism-backed platform for discovering novel DNA adducts and their precursor genotoxins.

Journal of cheminformatics·2026

Same author

Gene dependency-informed inference of response to targeted cancer therapies.

Nature communications·2026

Same author

Optimizing genomics-aware clinical agents in precision oncology.

NPJ systems biology and applications·2026

Same author

Altered chromatin accessibility and nucleosome positioning landscape upon HDAC and LSD1 inhibition in cancer cell.

bioRxiv : the preprint server for biology·2026

Same author

Soft Bayesian Additive Regression Trees (SBART) for correlated survey response with non-Gaussian error.

Journal of nonparametric statistics·2026

Search research articles

Related Experiment Video

Updated: Jan 4, 2026

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

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

Published on: December 1, 2023

Improved dropClust R package with integrative analysis support for scRNA-seq data.

Debajyoti Sinha^1,2, Pradyumn Sinha³, Ritwik Saha³

¹SyMeC Data Center, Indian Statistical Institute, Kolkata, India.

Bioinformatics (Oxford, England)

|November 7, 2019

Summary

This summary is machine-generated.

This study introduces an improved R package, dropClust, for fast and efficient clustering of large single-cell RNA sequencing datasets. The enhanced package includes a new algorithm for batch effect removal, enabling integrated analysis of multiple datasets.

More Related Videos

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Published on: November 7, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Related Experiment Videos

Last Updated: Jan 4, 2026

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

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

Published on: December 1, 2023

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Published on: November 7, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Area of Science:

Computational Biology
Bioinformatics
Genomics

Background:

Single-cell RNA sequencing (scRNA-seq) generates large, complex datasets.
Clustering is essential for analyzing scRNA-seq data to identify cell populations.
Existing methods can be computationally intensive and resource-demanding.

Purpose of the Study:

To present an improved R package, dropClust, for efficient clustering of large-scale scRNA-seq data.
To introduce a novel batch effect removal algorithm for integrative analysis.
To provide a fast, interoperable, and resource-efficient tool.

Main Methods:

Leveraging Locality Sensitive Hashing (LSH) for accelerated clustering.
Development of a complete R package with enhanced functionalities.
Implementation of a new algorithm for batch effect correction.

Main Results:

dropClust offers a fast and scalable solution for scRNA-seq data clustering.
The integrated batch effect removal enables seamless analysis of diverse datasets.
The package is designed to be interoperable and minimally resource-intensive.

Conclusions:

The improved dropClust package significantly enhances the analysis of large-scale scRNA-seq data.
Its novel batch effect correction facilitates integrative studies across datasets.
dropClust provides a valuable, efficient tool for the single-cell genomics community.