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

A new cancer progression model: From synthetic tumors to real data and back.

PLoS computational biology·2026

Same author

Dissecting minimal residual disease dynamics to improve outcome prediction in mantle cell lymphoma: Data from the Fondazione Italiana Linfomi (FIL)-MCL0208 clinical trial.

HemaSphere·2026

Same author

Common genomic and transcriptomic signatures in Richter transformation highlight druggable vulnerabilities and guide drug repurposing strategies.

Leukemia·2026

Same author

Gut microbiome modulation by cricket, pea, and whey protein using the SHIME in vitro simulator.

NPJ science of food·2026

Same author

Correction: Nosi et al. MET Exon 14 Skipping: A Case Study for the Detection of Genetic Variants in Cancer Driver Genes by Deep Learning. <i>Int. J. Mol. Sci.</i> 2021, <i>22</i>, 4217.

International journal of molecular sciences·2026

Same author

Single cell transcriptional perturbome in pluripotent stem cell models.

Molecular systems biology·2025

Same journal

Isolation of Mesenchymal Stem Cell-Derived Extracellular Vesicles.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Modeling Melanoma Immune Surveillance by CAR-T Cells in Human Skin Organoids.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Stepwise Optimization of a Matrigel-Based In Vitro Angiogenesis Assay for Reproducible and Quantifiable 2D-Tube Formation Using HUVECs.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Quantifying Mechanical Properties of Fresh Ovarian Tissue with Optical Brillouin Microscopy.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

3D Chromatin Architecture During Early Development: New Methods and New Findings.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Metabolic Plasticity in Embryogenesis Throughout the Lens of NAD<sup></sup>.

Methods in molecular biology (Clifton, N.J.)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Aug 18, 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

Single-Cell RNAseq Complexity Reduction.

Francesca Cordero¹, Raffaele A Calogero²

¹Department of Computer Science, University of Torino, Turin, Italy.

Methods in Molecular Biology (Clifton, N.J.)

|December 10, 2022

Summary

This summary is machine-generated.

This chapter details complexity reduction for 3' end single-cell RNA sequencing (scRNAseq) data. This preparation step is crucial for effectively partitioning cell sub-populations in transcriptomics analysis.

Keywords:

Data preprocessing Dimensionality reduction Single cell transcriptomics

More Related Videos

Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms

Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms

Published on: February 2, 2024

Author Spotlight: Enhancing Drug Discovery - Development of Automated, Standardized Protocols for Nuclei Extraction from Frozen Tissues

Author Spotlight: Enhancing Drug Discovery - Development of Automated, Standardized Protocols for Nuclei Extraction from Frozen Tissues

Published on: July 28, 2023

Related Experiment Videos

Last Updated: Aug 18, 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

Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms

Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms

Published on: February 2, 2024

Author Spotlight: Enhancing Drug Discovery - Development of Automated, Standardized Protocols for Nuclei Extraction from Frozen Tissues

Author Spotlight: Enhancing Drug Discovery - Development of Automated, Standardized Protocols for Nuclei Extraction from Frozen Tissues

Published on: July 28, 2023

Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

Single-cell RNA sequencing (scRNAseq) is a powerful technology for analyzing cellular heterogeneity.
Effective data preparation is essential for accurate downstream analyses, including cell sub-population identification.
Complexity reduction is a key step in handling the high-dimensional nature of scRNAseq data.

Purpose of the Study:

To describe methods for complexity reduction of 3' end scRNAseq transcriptomics data.
To provide a guide for researchers preparing scRNAseq data for cell sub-population partitioning.

Main Methods:

Focuses on complexity reduction techniques specifically for 3' end scRNAseq data.
Details the data preparation steps necessary prior to cell sub-population partitioning.

Main Results:

Provides a clear methodology for reducing the complexity of scRNAseq datasets.
Enables more efficient and accurate cell sub-population identification through optimized data preparation.

Conclusions:

Complexity reduction is a critical and often overlooked step in scRNAseq data analysis.
The described methods facilitate improved cell sub-population partitioning for 3' end scRNAseq data.