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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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FSCAM: CAM-Based Feature Selection for Clustering scRNA-seq.

Yan Wang¹, Jie Gao², Chenxu Xuan¹

¹School of Science, Jiangnan University, Wuxi, 214122, China.

Interdisciplinary Sciences, Computational Life Sciences

|January 14, 2022

Summary

A new method, Feature Selection based on Convex Analysis of Mixtures (FSCAM), improves single-cell RNA sequencing (scRNA-seq) by considering complex gene relationships. This enhances cell type determination accuracy through better feature selection.

Keywords:

Convex analysis of mixtures (CAM)Feature selection Single-cell RNA sequencing data (scRNA-seq data)Single-cell clustering algorithm “Many-to-many” relationship

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Identification of Circular RNAs using RNA Sequencing

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Published on: November 14, 2019

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Single-cell RNA sequencing (scRNA-seq) is vital for cell type determination, often using unsupervised clustering.
Effective feature selection is crucial for scRNA-seq clustering accuracy.
Existing methods often overlook gene dependencies or only consider 'one-to-many' relationships.

Purpose of the Study:

To introduce a novel feature selection method, FSCAM, that accounts for 'many-to-many' gene relationships.
To enhance the accuracy and stability of cell type determination in scRNA-seq data.
To develop an improved scRNA-seq clustering algorithm, SCC_FSCAM.

Main Methods:

Developed Feature Selection based on Convex Analysis of Mixtures (FSCAM) to capture 'many-to-many' gene interactions.
FSCAM integrates relevancy, redundancy, and completeness for gene selection.
Integrated FSCAM into the Partition Around Medoids (PAM) clustering framework to create the SCC_FSCAM algorithm.

Main Results:

FSCAM demonstrated superiority over existing methods in benchmarking on real scRNA-seq datasets.
The SCC_FSCAM algorithm showed advantages in both internal (clustering number) and external (adjusted Rand index) criteria.
The developed algorithm exhibited good stability in clustering performance.

Conclusions:

FSCAM offers a more comprehensive approach to feature selection in scRNA-seq by considering complex gene dependencies.
SCC_FSCAM represents an advancement in scRNA-seq clustering, improving accuracy and reliability for cell type identification.
The proposed method and algorithm provide a valuable tool for analyzing single-cell transcriptomic data.