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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...

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Related Experiment Video

Updated: Mar 15, 2026

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

Scalable nonparametric clustering with unified marker gene selection for single-cell RNA-seq data.

Chibuikem Nwizu¹, Madeline Hughes², Michelle L Ramseier³

¹Center for Computational Molecular Biology, Brown University, Providence, RI 02906, USA; Warren Alpert Medical School of Brown University, Providence, RI 02906, USA.

Cell Reports Methods

|March 13, 2026

Summary

This summary is machine-generated.

NCLUSION is a new nonparametric model for single-cell RNA sequencing (scRNA-seq) analysis. It simultaneously identifies marker genes and clusters cells, offering a faster and more robust approach to understanding cellular heterogeneity.

Keywords:

CP: computational biology CP: systems biology clustering machine learning marker gene selection nonparametric variational inference

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Last Updated: Mar 15, 2026

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

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

Published on: March 23, 2022

Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Single-cell RNA sequencing (scRNA-seq) is crucial for assessing cellular heterogeneity.
Standard clustering methods often require manual parameter tuning and can lead to high false discovery rates in differential expression analysis.

Purpose of the Study:

To introduce NCLUSION, a novel nonparametric infinite mixture model for simultaneous clustering and marker gene identification in scRNA-seq data.
To develop a scalable and statistically robust method for analyzing large scRNA-seq datasets.

Main Methods:

NCLUSION employs Bayesian sparse priors and a variational inference algorithm.
The model is designed to handle large-scale scRNA-seq datasets, potentially including millions of cells.

Main Results:

NCLUSION demonstrates comparable performance to state-of-the-art clustering methods but with significantly reduced computational time.
The identified clusters are supported by statistically robust and biologically relevant transcriptomic signatures.

Conclusions:

NCLUSION provides a reliable and efficient tool for hypothesis generation in single-cell biology.
The method enhances the understanding of expression variation patterns within single-cell populations.