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A model-based clustering algorithm with covariates adjustment and its application to lung cancer stratification
J Bioinform Comput Biol. 2023 Aug;21(4):2350019. doi: 10.1142/S0219720023500191. Epub 2023 Sep 8.ABSTRACTUsually, the clustering process is the first step in several data analyses. Clustering allows identify patterns we did not note before and helps raise new hypotheses. However, one challenge when analyzing empirical data is the presence of covariates, which may mask the obtained clustering structure. For example, suppose we are interested in clustering a set of individuals into controls and cancer patients. A clustering algorithm could group subjects into young and elderly in this case. It may happen because the age at d...
Source: Journal of Bioinformatics and Computational Biology - September 11, 2023 Category: Bioinformatics Authors: Carlos E M Relvas Asuka Nakata Guoan Chen David G Beer Noriko Gotoh Andre Fujita Source Type: research

Multi-omics data analysis reveals the biological implications of alternative splicing events in lung adenocarcinoma
J Bioinform Comput Biol. 2023 Aug;21(4):2350020. doi: 10.1142/S0219720023500208. Epub 2023 Sep 8.ABSTRACTCancer is characterized by the dysregulation of alternative splicing (AS). However, the comprehensive regulatory mechanisms of AS in lung adenocarcinoma (LUAD) are poorly understood. Here, we displayed the AS landscape in LUAD based on the integrated analyses of LUAD's multi-omics data. We identified 13,995 AS events in 6309 genes as differentially expressed alternative splicing events (DEASEs) mainly covering protein-coding genes. These DEASEs were strongly linked to "cancer hallmarks", such as apoptosis, DNA repair, c...
Source: Journal of Bioinformatics and Computational Biology - September 11, 2023 Category: Bioinformatics Authors: Fuyan Hu Bifeng Chen Qing Wang Zhiyuan Yang Man Chu Source Type: research

A model-based clustering algorithm with covariates adjustment and its application to lung cancer stratification
J Bioinform Comput Biol. 2023 Aug;21(4):2350019. doi: 10.1142/S0219720023500191. Epub 2023 Sep 8.ABSTRACTUsually, the clustering process is the first step in several data analyses. Clustering allows identify patterns we did not note before and helps raise new hypotheses. However, one challenge when analyzing empirical data is the presence of covariates, which may mask the obtained clustering structure. For example, suppose we are interested in clustering a set of individuals into controls and cancer patients. A clustering algorithm could group subjects into young and elderly in this case. It may happen because the age at d...
Source: Journal of Bioinformatics and Computational Biology - September 11, 2023 Category: Bioinformatics Authors: Carlos E M Relvas Asuka Nakata Guoan Chen David G Beer Noriko Gotoh Andre Fujita Source Type: research

Multi-omics data analysis reveals the biological implications of alternative splicing events in lung adenocarcinoma
J Bioinform Comput Biol. 2023 Aug;21(4):2350020. doi: 10.1142/S0219720023500208. Epub 2023 Sep 8.ABSTRACTCancer is characterized by the dysregulation of alternative splicing (AS). However, the comprehensive regulatory mechanisms of AS in lung adenocarcinoma (LUAD) are poorly understood. Here, we displayed the AS landscape in LUAD based on the integrated analyses of LUAD's multi-omics data. We identified 13,995 AS events in 6309 genes as differentially expressed alternative splicing events (DEASEs) mainly covering protein-coding genes. These DEASEs were strongly linked to "cancer hallmarks", such as apoptosis, DNA repair, c...
Source: Journal of Bioinformatics and Computational Biology - September 11, 2023 Category: Bioinformatics Authors: Fuyan Hu Bifeng Chen Qing Wang Zhiyuan Yang Man Chu Source Type: research

A model-based clustering algorithm with covariates adjustment and its application to lung cancer stratification
J Bioinform Comput Biol. 2023 Aug;21(4):2350019. doi: 10.1142/S0219720023500191. Epub 2023 Sep 8.ABSTRACTUsually, the clustering process is the first step in several data analyses. Clustering allows identify patterns we did not note before and helps raise new hypotheses. However, one challenge when analyzing empirical data is the presence of covariates, which may mask the obtained clustering structure. For example, suppose we are interested in clustering a set of individuals into controls and cancer patients. A clustering algorithm could group subjects into young and elderly in this case. It may happen because the age at d...
Source: Journal of Bioinformatics and Computational Biology - September 11, 2023 Category: Bioinformatics Authors: Carlos E M Relvas Asuka Nakata Guoan Chen David G Beer Noriko Gotoh Andre Fujita Source Type: research