In recent years, significant advances in next generation sequencing technologies have made RNA\u00a0sequencing (RNA-seq) a popular choice for studies of gene expression. Although microarrays and RNA-seq\u00a0both aim to characterize transcriptional activity, the statistical tools developed for the analysis of the former\u00a0are ill-suited to the latter. To date, the methodological developments for RNA-seq data have mainly focused\u00a0on normalization and differential analysis, but the testing procedures currently proposed lack power to detect\u00a0differentially expressed genes; little methodological research has been devoted to the identification of coexpressed\u00a0genes in RNA-seq data. However, as costs for RNA-seq experiments continue to decrease, it is\u00a0likely that such studies will replace the use of microarrays for many applications involving investigations of\u00a0the transcriptome. It is therefore crucial to pursue research on the development of statistical methods that\u00a0allow biologists to exploit RNA-seq data.\u00a0In the MixStatSeq project, we focus on three main biological questions for RNA-seq data: (i) the\u00a0detection of differentially expressed genes, (ii) the detection of co-expressed gene clusters, and (iii) the\u00a0detection of invariant genes, i.e., those with stable expression in several biological conditions. To address\u00a0these three biological questions, we propose to develop a suite of statistically sound methods based on\u00a0mixture models.
\nFor the analysis of differential expression, two points of view are envisaged. In the first, we aim to\u00a0construct a powerful testing procedure by first performing a gene clustering step, followed by a testing\u00a0procedure for each subgroup of genes and a correction for multiple testing. In the second, we will investigate\u00a0model-based clustering procedures that directly cluster genes into groups representing differential and nondifferential\u00a0expression.
\nFor the detection of co-expressed gene clusters, we will extend our preliminary work on the use of\u00a0mixture models. In particular, as the number of RNA-seq experiments will continue to increase in the\u00a0coming years, it is crucial to develop variable selection procedures, as well as to incorporate external\u00a0biological knowledge, in order to improve the interpretability of gene clustering.
\nFor the detection of invariant genes, we aim to develop a non-asymptotic multiple hypothesis testing\u00a0procedure to test a single distribution against a mixture of distributions, and to study its theoretical properties\u00a0to ensure a powerful test. Beyond the biological application, such a development is a difficult theoretical\u00a0challenge.
\nThroughout the MixStatSeq project, the team will foster collaborations with biologists of several\u00a0laboratories to validate chosen models and test the developed approaches on real RNA-seq data obtained\u00a0from different organisms. The originality of the MixStatSeq project will be the continuous exchange between\u00a0theoretical, methodological and applied research, including the assessment of biologists, in order to ensure\u00a0the immediate potential impact of the developed procedures. Moreover, beyond the RNA-seq data study, this\u00a0project will provide new theoretical and methodological knowledge for the study of count data with
\nmixtures.<\/p>\n","protected":false},"excerpt":{"rendered":"
In recent years, significant advances in next generation sequencing technologies have made RNA\u00a0sequencing (RNA-seq) a popular choice for studies of gene expression. Although microarrays and RNA-seq\u00a0both aim to characterize transcriptional activity, the statistical tools developed for the analysis of the former\u00a0are ill-suited to the latter. To date, the methodological developments for RNA-seq data have mainly […]<\/p>\n","protected":false},"author":128,"featured_media":0,"parent":126,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-145","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/perso.math.univ-toulouse.fr\/maugis\/wp-json\/wp\/v2\/pages\/145","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/perso.math.univ-toulouse.fr\/maugis\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/perso.math.univ-toulouse.fr\/maugis\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/perso.math.univ-toulouse.fr\/maugis\/wp-json\/wp\/v2\/users\/128"}],"replies":[{"embeddable":true,"href":"https:\/\/perso.math.univ-toulouse.fr\/maugis\/wp-json\/wp\/v2\/comments?post=145"}],"version-history":[{"count":4,"href":"https:\/\/perso.math.univ-toulouse.fr\/maugis\/wp-json\/wp\/v2\/pages\/145\/revisions"}],"predecessor-version":[{"id":176,"href":"https:\/\/perso.math.univ-toulouse.fr\/maugis\/wp-json\/wp\/v2\/pages\/145\/revisions\/176"}],"up":[{"embeddable":true,"href":"https:\/\/perso.math.univ-toulouse.fr\/maugis\/wp-json\/wp\/v2\/pages\/126"}],"wp:attachment":[{"href":"https:\/\/perso.math.univ-toulouse.fr\/maugis\/wp-json\/wp\/v2\/media?parent=145"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}