چکیده مقاله
Aim: This study aimed to detect gene signatures in RNA sequencing RNA seq data using Pareto optimal cluster size identification Background: RNA seq has emerged as an important technology for transcriptome profiling in recent years Gene expression signatures involving tens of genes have been proven to be predictive of disease type and patient response to treatment Methods: Data was related to liver cancer RNA seq dataset, which included 35 paired Hepatocellular carcinoma HCC and non tumor tissue samples At first, the differentially expressed genes DEGs were finding after performing pre filtering and normalization After that, a multi objective optimization technique namely Multi objective optimization for collecting cluster alternatives MOCCA was used to discover the Pareto optimal cluster size for these DEGs Then k means clustering method was performed on the RNA seq data The best cluster, as a signature for the disease, was found by calculating the average Spearman's correlation score of all the genes in the module in a pair wise manner All analysis was performed in R 4 1 1 package under virtual space with 100Gb RAM memory Results: Using MOCCA, eight Pareto optimal clusters were obtained Finally, two clusters with the greatest average Spearman's correlation coefficient score were chosen as gene signature Eleven prognostic genes involved in HCC's abnormal metabolism were identified In addition three differentially expressed pathways were identified between tumor and non tumor tissues Conclusion: These identified metabolic prognostic genes help us to provide more powerful prognostic information and enhance survival prediction for HCC patients In addition, Pareto optimal cluster size identification is suggested to gene signature in other RNA Seq data
کلیدواژهها
نویسندگان
Akbar Biglarian
Department of Biostatistics and Epidemiology, Social Determinants of Health Research Center, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran
Toktam Akbari Khalaj
Department of Statistics, Emergency Management Services, Mashhad University of Medical Sciences,Mashhad, Iran
Taiebe Kenarangi
Department of Biostatistics and Epidemiology, Faculty of Statistics,University of Social Welfare and Rehabilitation Sciences, Tehran, Iran
Enayatolah Bakhshi
Department of Biostatistics and Epidemiology, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran
Kolsoum Inanloo Rahatloo
Department of cell and molecular biology, school of biology, college of science, university of Tehran, Tehran, Iran
Morteza Lotfi
Executive Vice President, Emergency Management Services, Mashhad University of Medical Sciences,Mashhad, Iran
شیوه ارجاع
Biglarian, Akbar and Akbari Khalaj, Toktam and Kenarangi, Taiebe and Bakhshi, Enayatolah and Inanloo Rahatloo, Kolsoum and Lotfi, Morteza,1403,Identification of gene signature in RNA-Seq liver cancer data using Clustering Algorithms,1st International Biennial Conference of Artificial Intelligence and Data Science 2024,Bushehr
ارائهشده در
مجموعه مقالات اولین کنفرانس بین المللی دوسالانه هوش مصنوعی و علوم داده3 اردیبهشت 1403 · بوشهر