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

Degraded mapping of disparity tuning in visual cortex explains deficits in binocular depth perception.

bioRxiv : the preprint server for biology·2026

Same author

Lessons Learned from Designing and Evaluating a Robot-assisted Feeding System for Out-of-lab Use.

ACM/IEEE International Conference on Human-Robot Interaction : [proceedings]. ACM/IEEE Conference on Human-Robot Interaction·2026

Same author

Identification of public neoantigens with broad HLA class I coverage as candidates for off-the-shelf cancer vaccine development in colorectal cancer.

Frontiers in immunology·2026

Same author

Total Chemical Synthesis and Evaluation of the Antimicrobial Properties of Porcine β-Defensin 5 Against Gram-Positive and Gram-Negative Bacteria.

Probiotics and antimicrobial proteins·2026

Same author

Indolent primary cutaneous B-cell lymphomas resemble persistent antigen reactions without signs of dedifferentiation.

Nature communications·2026

Same author

Healthcare system readiness to manage viral hepatitis in Viet Nam and the Philippines: results of a brief health facility assessment.

BMC health services research·2026

Search research articles

Related Experiment Video

Updated: Oct 6, 2025

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Published on: May 22, 2017

scAnnotatR: framework to accurately classify cell types in single-cell RNA-sequencing data.

Vy Nguyen¹, Johannes Griss²

¹Department of Dermatology, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria.

BMC Bioinformatics

|January 18, 2022

Summary

This summary is machine-generated.

scAnnotatR is a new R package for automated cell type identification in single-cell RNA sequencing (scRNA-seq) data. It accurately classifies cells, handles unknown cell types, and scales to large datasets.

Keywords:

Bioconductor Cell classification Machine learning R SVM scAnnotatR scRNAseq

More Related Videos

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

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

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

Published on: May 3, 2024

Related Experiment Videos

Last Updated: Oct 6, 2025

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Published on: May 22, 2017

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

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

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

Published on: May 3, 2024

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Automatic cell type identification is crucial for single-cell RNA sequencing (scRNA-seq) data analysis.
Existing tools struggle with classifying unknown cell types and scaling to large datasets.

Purpose of the Study:

To introduce scAnnotatR, an R package for robust cell type classification in scRNA-seq data.
To address limitations of current tools regarding unknown cell types and scalability.

Main Methods:

scAnnotatR utilizes hierarchically organized Support Vector Machines (SVMs).
It is compatible with Seurat and SingleCellExperiment objects, common in R workflows.
The package uses pre-trained classifiers for cell type identification.

Main Results:

scAnnotatR demonstrates comparable or superior accuracy, sensitivity, and specificity to existing tools.
It effectively identifies unknown cell types not present in reference datasets.
The package scales to process datasets with over 600,000 cells, outperforming other leading tools.

Conclusions:

scAnnotatR offers a scalable and accurate solution for cell type classification in scRNA-seq.
It is available as an R package via GitHub and Bioconductor.
The tool performs consistently well on large-scale datasets.