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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: Oct 23, 2025

Identification of Circular RNAs using RNA Sequencing

Identification of Circular RNAs using RNA Sequencing

Published on: November 14, 2019

MultiK: an automated tool to determine optimal cluster numbers in single-cell RNA sequencing data.

Siyao Liu^1,2,3, Aatish Thennavan^1,4, Joseph P Garay⁵

¹Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Marsico Hall, 5th floor, CB#7599, 125 Mason Farm Road, Chapel Hill, NC, 27599, USA.

|August 20, 2021

Summary

Estimating the number of cell groups in single-cell RNA sequencing (scRNA-seq) is vital. Our MultiK tool objectively identifies the optimal number of cell clusters using a robust, multi-resolution consensus approach.

Keywords:

Clustering Genomics Multi-resolution Multi-scale Reproducibility Single-cell RNA-seq

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Last Updated: Oct 23, 2025

Identification of Circular RNAs using RNA Sequencing

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Published on: November 14, 2019

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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Published on: January 10, 2019

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Published on: January 19, 2024

Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Single-cell RNA sequencing (scRNA-seq) enables high-resolution cellular analysis.
Clustering is standard for identifying cell populations in scRNA-seq data.
Determining the optimal number of clusters (K) is critical but often overlooked.

Purpose of the Study:

To develop an objective method for estimating the optimal number of clusters (K) in scRNA-seq data.
To introduce MultiK, a robust tool for selecting insightful K values.
To improve the reliability of cell population identification in scRNA-seq analysis.

Main Methods:

Implemented a multi-resolution perspective for cluster number estimation.
Utilized a consensus clustering approach for robustness.
Developed the MultiK tool for objective K selection.

Main Results:

MultiK provides an objective estimation of the number of cell groups.
The tool demonstrates high robustness in identifying reproducible cell populations.
MultiK enhances the accuracy of cell-type identification in scRNA-seq datasets.

Conclusions:

Objective estimation of K is crucial for scRNA-seq data analysis.
MultiK offers a reliable and robust solution for determining the number of cell populations.
This approach facilitates more accurate characterization of cellular heterogeneity.