Differential expression analysis for sequence count data via mixtures of negative binomials

The recent advent of Next-generation sequencing technologies has revolutionized the way of analyzing the genome. This innovation allows to get deeper information at a lower cost and in less time, and provides data that are discrete measurements. One of the most important applications with these dat...

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Main Author: Bonafede, Elisabetta <1987>
Other Authors: Viroli, Cinzia
Format: Doctoral Thesis
Language:en
Published: Alma Mater Studiorum - Università di Bologna 2015
Subjects:
Online Access:http://amsdottorato.unibo.it/6741/
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spelling ndltd-unibo.it-oai-amsdottorato.cib.unibo.it-67412015-02-14T04:50:16Z Differential expression analysis for sequence count data via mixtures of negative binomials Bonafede, Elisabetta <1987> SECS-S/01 Statistica The recent advent of Next-generation sequencing technologies has revolutionized the way of analyzing the genome. This innovation allows to get deeper information at a lower cost and in less time, and provides data that are discrete measurements. One of the most important applications with these data is the differential analysis, that is investigating if one gene exhibit a different expression level in correspondence of two (or more) biological conditions (such as disease states, treatments received and so on). As for the statistical analysis, the final aim will be statistical testing and for modeling these data the Negative Binomial distribution is considered the most adequate one especially because it allows for "over dispersion". However, the estimation of the dispersion parameter is a very delicate issue because few information are usually available for estimating it. Many strategies have been proposed, but they often result in procedures based on plug-in estimates, and in this thesis we show that this discrepancy between the estimation and the testing framework can lead to uncontrolled first-type errors. We propose a mixture model that allows each gene to share information with other genes that exhibit similar variability. Afterwards, three consistent statistical tests are developed for differential expression analysis. We show that the proposed method improves the sensitivity of detecting differentially expressed genes with respect to the common procedures, since it is the best one in reaching the nominal value for the first-type error, while keeping elevate power. The method is finally illustrated on prostate cancer RNA-seq data. Alma Mater Studiorum - Università di Bologna Viroli, Cinzia Robin, Stéphane 2015-02-02 Doctoral Thesis PeerReviewed application/pdf en http://amsdottorato.unibo.it/6741/ info:eu-repo/semantics/openAccess
collection NDLTD
language en
format Doctoral Thesis
sources NDLTD
topic SECS-S/01 Statistica
spellingShingle SECS-S/01 Statistica
Bonafede, Elisabetta <1987>
Differential expression analysis for sequence count data via mixtures of negative binomials
description The recent advent of Next-generation sequencing technologies has revolutionized the way of analyzing the genome. This innovation allows to get deeper information at a lower cost and in less time, and provides data that are discrete measurements. One of the most important applications with these data is the differential analysis, that is investigating if one gene exhibit a different expression level in correspondence of two (or more) biological conditions (such as disease states, treatments received and so on). As for the statistical analysis, the final aim will be statistical testing and for modeling these data the Negative Binomial distribution is considered the most adequate one especially because it allows for "over dispersion". However, the estimation of the dispersion parameter is a very delicate issue because few information are usually available for estimating it. Many strategies have been proposed, but they often result in procedures based on plug-in estimates, and in this thesis we show that this discrepancy between the estimation and the testing framework can lead to uncontrolled first-type errors. We propose a mixture model that allows each gene to share information with other genes that exhibit similar variability. Afterwards, three consistent statistical tests are developed for differential expression analysis. We show that the proposed method improves the sensitivity of detecting differentially expressed genes with respect to the common procedures, since it is the best one in reaching the nominal value for the first-type error, while keeping elevate power. The method is finally illustrated on prostate cancer RNA-seq data.
author2 Viroli, Cinzia
author_facet Viroli, Cinzia
Bonafede, Elisabetta <1987>
author Bonafede, Elisabetta <1987>
author_sort Bonafede, Elisabetta <1987>
title Differential expression analysis for sequence count data via mixtures of negative binomials
title_short Differential expression analysis for sequence count data via mixtures of negative binomials
title_full Differential expression analysis for sequence count data via mixtures of negative binomials
title_fullStr Differential expression analysis for sequence count data via mixtures of negative binomials
title_full_unstemmed Differential expression analysis for sequence count data via mixtures of negative binomials
title_sort differential expression analysis for sequence count data via mixtures of negative binomials
publisher Alma Mater Studiorum - Università di Bologna
publishDate 2015
url http://amsdottorato.unibo.it/6741/
work_keys_str_mv AT bonafedeelisabetta1987 differentialexpressionanalysisforsequencecountdataviamixturesofnegativebinomials
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