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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 microarray-based...

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Updated: Jun 26, 2026

Identification of Circular RNAs using RNA Sequencing

Identification of Circular RNAs using RNA Sequencing

Published on: November 14, 2019

K-Volume Clustering Algorithms for scRNA-Seq Data Analysis.

Yong Chen¹, Fei Li²

¹Department of Biological and Biomedical Sciences, Rowan University, Glassboro, NJ 08028, USA.

|March 26, 2025

Summary

This summary is machine-generated.

A new algorithm, K-volume clustering, addresses challenges in analyzing complex biological data. It uses geometric volume to improve clustering accuracy for single-cell and multi-omics datasets.

Keywords:

clustering algorithms gene regulatory networks single-cell omics

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Area of Science:

Computational biology
Bioinformatics
Data science

Background:

Clustering high-dimensional and structural data is difficult, particularly for complex single-cell and multi-omics datasets.
Existing methods often struggle with the intricate nature of modern biological data.

Purpose of the Study:

Introduce K-volume clustering, a novel algorithm for analyzing complex biological datasets.
Provide a geometrically interpretable and biologically relevant criterion for clustering.

Main Methods:

Developed K-volume clustering, an algorithm utilizing total convex volume within clusters.
Employed nonlinear optimization to simultaneously refine hierarchical structure and cluster count.
Validated the algorithm on diverse real-world biological datasets.

Main Results:

K-volume clustering demonstrated superior performance compared to traditional methods.
The algorithm proved effective across various biological applications.
Showcased the method's theoretical soundness and broad applicability.

Conclusions:

K-volume clustering offers a promising new tool for computational biology.
The algorithm enhances the analysis of single-cell and multi-omics data.
Its geometric interpretability and optimization capabilities make it valuable for diverse data analysis tasks.