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Please use this identifier to cite or link to this item: https://elib.bsu.by/handle/123456789/233400
Title: A random graph generation model for transcription networks and nonparametric simulator for RNA-seq expression data
Authors: Grimes, T.
Datta, S.
Issue Date: 2019
Publisher: Minsk : BSU
Citation: Computer Data Analysis and Modeling: Stochastics and Data Science : Proc. of the Twelfth Intern. Conf., Minsk, Sept. 18-22, 2019. – Minsk : BSU, 2019. – P. 37-42.
Abstract: With the advent of high-throughput sequencing and continually decreasing costs, an abundance of gene expression data has been generated and is available for analysis. These data provide a rich resource for inferring connections in gene regulatory networks. A vast collection of methodologies have been developed, but a challenge remains in assessing and benchmarking their performance. Gold-standard datasets are scarce, and commonly-used simulators are not designed to resemble the data generated from RNA-seq experiments. The present study provides a novel random graph generator that produces networks having comparable topology to transcription networks. In addition, a nonparametric simulator is proposed that generates conditionally dependent expression data; the conditional dependencies are based on an underlying network structure, and the marginal distribution of gene expression profiles are based on a reference RNA-seq dataset. These methods provide tools for creating in silico RNA-seq data for benchmarking and assessing gene network inference methods. Keywords: data science, random graph, transcription network, RNA expression
URI: http://elib.bsu.by/handle/123456789/233400
ISBN: 978-985-566-811-5
Appears in Collections:2019. Computer Data Analysis and Modeling : Stochastics and Data Science

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