Discovering key transcriptomic regulators in pancreatic ductal adenocarcinoma using Dirichlet process Gaussian mixture model

Abstract Pancreatic Ductal Adenocarcinoma (PDAC) is the most lethal type of pancreatic cancer, late detection leading to its therapeutic failure. This study aims to determine the key regulatory genes and their impacts on the disease’s progression, helping the disease’s etiology, which is still mostl...

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Autores principales:	Sk Md Mosaddek Hossain, Aanzil Akram Halsana, Lutfunnesa Khatun, Sumanta Ray, Anirban Mukhopadhyay
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b07f52d2385847e087e1de38db80b5b7
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spelling	oai:doaj.org-article:b07f52d2385847e087e1de38db80b5b72021-12-02T18:02:48ZDiscovering key transcriptomic regulators in pancreatic ductal adenocarcinoma using Dirichlet process Gaussian mixture model10.1038/s41598-021-87234-72045-2322https://doaj.org/article/b07f52d2385847e087e1de38db80b5b72021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-87234-7https://doaj.org/toc/2045-2322Abstract Pancreatic Ductal Adenocarcinoma (PDAC) is the most lethal type of pancreatic cancer, late detection leading to its therapeutic failure. This study aims to determine the key regulatory genes and their impacts on the disease’s progression, helping the disease’s etiology, which is still mostly unknown. We leverage the landmark advantages of time-series gene expression data of this disease and thereby identified the key regulators that capture the characteristics of gene activity patterns in the cancer progression. We have identified the key gene modules and predicted the functions of top genes from a reconstructed gene association network (GAN). A variation of the partial correlation method is utilized to analyze the GAN, followed by a gene function prediction task. Moreover, we have identified regulators for each target gene by gene regulatory network inference using the dynamical GENIE3 (dynGENIE3) algorithm. The Dirichlet process Gaussian process mixture model and cubic spline regression model (splineTimeR) are employed to identify the key gene modules and differentially expressed genes, respectively. Our analysis demonstrates a panel of key regulators and gene modules that are crucial for PDAC disease progression.Sk Md Mosaddek HossainAanzil Akram HalsanaLutfunnesa KhatunSumanta RayAnirban MukhopadhyayNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sk Md Mosaddek Hossain Aanzil Akram Halsana Lutfunnesa Khatun Sumanta Ray Anirban Mukhopadhyay Discovering key transcriptomic regulators in pancreatic ductal adenocarcinoma using Dirichlet process Gaussian mixture model
description	Abstract Pancreatic Ductal Adenocarcinoma (PDAC) is the most lethal type of pancreatic cancer, late detection leading to its therapeutic failure. This study aims to determine the key regulatory genes and their impacts on the disease’s progression, helping the disease’s etiology, which is still mostly unknown. We leverage the landmark advantages of time-series gene expression data of this disease and thereby identified the key regulators that capture the characteristics of gene activity patterns in the cancer progression. We have identified the key gene modules and predicted the functions of top genes from a reconstructed gene association network (GAN). A variation of the partial correlation method is utilized to analyze the GAN, followed by a gene function prediction task. Moreover, we have identified regulators for each target gene by gene regulatory network inference using the dynamical GENIE3 (dynGENIE3) algorithm. The Dirichlet process Gaussian process mixture model and cubic spline regression model (splineTimeR) are employed to identify the key gene modules and differentially expressed genes, respectively. Our analysis demonstrates a panel of key regulators and gene modules that are crucial for PDAC disease progression.
format	article
author	Sk Md Mosaddek Hossain Aanzil Akram Halsana Lutfunnesa Khatun Sumanta Ray Anirban Mukhopadhyay
author_facet	Sk Md Mosaddek Hossain Aanzil Akram Halsana Lutfunnesa Khatun Sumanta Ray Anirban Mukhopadhyay
author_sort	Sk Md Mosaddek Hossain
title	Discovering key transcriptomic regulators in pancreatic ductal adenocarcinoma using Dirichlet process Gaussian mixture model
title_short	Discovering key transcriptomic regulators in pancreatic ductal adenocarcinoma using Dirichlet process Gaussian mixture model
title_full	Discovering key transcriptomic regulators in pancreatic ductal adenocarcinoma using Dirichlet process Gaussian mixture model
title_fullStr	Discovering key transcriptomic regulators in pancreatic ductal adenocarcinoma using Dirichlet process Gaussian mixture model
title_full_unstemmed	Discovering key transcriptomic regulators in pancreatic ductal adenocarcinoma using Dirichlet process Gaussian mixture model
title_sort	discovering key transcriptomic regulators in pancreatic ductal adenocarcinoma using dirichlet process gaussian mixture model
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b07f52d2385847e087e1de38db80b5b7
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_version_	1718378890063249408

Discovering key transcriptomic regulators in pancreatic ductal adenocarcinoma using Dirichlet process Gaussian mixture model

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