Class Discovery, Comparison, and Prediction Methods for RNA-Seq Data

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Author(s): Ahu Cephe (Erciyes University, Turkey), Necla Koçhan (İzmir Biomedicine and Genome Center, Turkey), Gözde Ertürk Zararsız (Erciyes University, Turkey), Vahap Eldem (İstanbul University, Turkey)and Gökmen Zararsız (Erciyes University, Turkey)
Copyright: 2023
Pages: 25
Source title: Encyclopedia of Data Science and Machine Learning
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-7998-9220-5.ch123

Keywords: Data Analysis and Statistics / Information Science Reference / Knowledge Management and Library Science / Machine Learning

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Abstract

Gene-expression studies have been studied using microarray data for many years, and numerous methods have been developed for these data. However, microarray technology is old technology and has some limitations. RNA-sequencing (RNA-seq) is a new transcriptomics technique capable of coping with these limitations, using the capabilities of new generation sequencing technologies, and performing operations quickly and cheaply based on the principle of high-throughput sequencing technology. Compared to microarrays, RNA-seq offers several advantages: (1) having less noisy data, (2) being able to detect new transcripts and coding regions, (3) not requiring pre-determination of the transcriptomes of interest. However, RNA-seq data has several features that pose statistical challenges. Thus, one cannot directly use methods developed for microarray analyses, which has a discrete and overdispersed nature of data, quite different from the continuous data structure of microarrays. This article aims to provide an overview and practical guidance to researchers working with RNA-seq data for different purposes.

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Class Discovery, Comparison, and Prediction Methods for RNA-Seq Data

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