RNA-Seq has become the de facto standard technique for characterization and quantification of transcriptomes, and a large number of methods and tools have been proposed to model and detect differential gene expression based on the comparison of transcript abundances across different samples. However, state-of-the-art methods for this task are usually designed for pairwise comparisons, that is, can identify significant variation of expression only between two conditions or samples. We describe the use of RNentropy, a methodology based on information theory, devised to overcome this limitation. RNentropy can thus detect significant variations of gene expression in RNA-Seq data across any number of samples and conditions, and can be applied downstream of any analysis pipeline for the quantification of gene expression from raw sequencing data. RNentropy takes as input gene (or transcript) expression values, defined with any measure suitable for the comparison of transcript levels across samples and conditions. The output consists of genes (or transcripts) exhibiting significant variation of expression across the conditions studied, together with the samples in which they result to be over- or underexpressed. RNentropy is implemented as an R package and freely available from the CRAN repository. We provide a detailed guide to the functions and parameters of the package and usage examples to demonstrate the software capabilities, also showing how it can be applied to the analysis of single-cell RNA sequencing data.

Using RNentropy to Detect Significant Variation in Gene Expression Across Multiple RNA-Seq or Single-Cell RNA-Seq Samples / F. Zambelli, G. Pavesi (METHODS IN MOLECULAR BIOLOGY). - In: RNA Bioinformatics / [a cura di] E. Picardi. - [s.l] : Springer, 2021. - ISBN 9781071613061. - pp. 77-96

Using RNentropy to Detect Significant Variation in Gene Expression Across Multiple RNA-Seq or Single-Cell RNA-Seq Samples

F. Zambelli
Primo
;
G. Pavesi
Ultimo
2021

Abstract

RNA-Seq has become the de facto standard technique for characterization and quantification of transcriptomes, and a large number of methods and tools have been proposed to model and detect differential gene expression based on the comparison of transcript abundances across different samples. However, state-of-the-art methods for this task are usually designed for pairwise comparisons, that is, can identify significant variation of expression only between two conditions or samples. We describe the use of RNentropy, a methodology based on information theory, devised to overcome this limitation. RNentropy can thus detect significant variations of gene expression in RNA-Seq data across any number of samples and conditions, and can be applied downstream of any analysis pipeline for the quantification of gene expression from raw sequencing data. RNentropy takes as input gene (or transcript) expression values, defined with any measure suitable for the comparison of transcript levels across samples and conditions. The output consists of genes (or transcripts) exhibiting significant variation of expression across the conditions studied, together with the samples in which they result to be over- or underexpressed. RNentropy is implemented as an R package and freely available from the CRAN repository. We provide a detailed guide to the functions and parameters of the package and usage examples to demonstrate the software capabilities, also showing how it can be applied to the analysis of single-cell RNA sequencing data.
Differential gene expression; Marker genes; Next-generation sequencing; RNA-seq; Single cell RNA-seq; Tissue-specific genes; Transcriptome quantification
Settore BIO/11 - Biologia Molecolare
Settore INF/01 - Informatica
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/841827
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