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MESBC: A novel mutually exclusive spectral biclustering method for cancer subtyping.
Liu, Fengrong; Yang, Yaning; Xu, Xu Steven; Yuan, Min.
Afiliación
  • Liu F; Department of Statistics and Finance, University of Science and Technology of China, Hefei 230026, China.
  • Yang Y; Department of Statistics and Finance, University of Science and Technology of China, Hefei 230026, China.
  • Xu XS; Genmab US, Inc, Princeton, NJ 08540, USA. Electronic address: sxu@genmab.com.
  • Yuan M; School of Public Health Administration, Anhui Medical University, Hefei 230032, China. Electronic address: myuan.ahmu@gmail.com.
Comput Biol Chem ; 109: 108009, 2024 Apr.
Article en En | MEDLINE | ID: mdl-38219419
ABSTRACT
Many soft biclustering algorithms have been developed and applied to various biological and biomedical data analyses. However, few mutually exclusive (hard) biclustering algorithms have been proposed, which could better identify disease or molecular subtypes with survival significance based on genomic or transcriptomic data. In this study, we developed a novel mutually exclusive spectral biclustering (MESBC) algorithm based on spectral method to detect mutually exclusive biclusters. MESBC simultaneously detects relevant features (genes) and corresponding conditions (patients) subgroups and, therefore, automatically uses the signature features for each subtype to perform the clustering. Extensive simulations revealed that MESBC provided superior accuracy in detecting pre-specified biclusters compared with the non-negative matrix factorization (NMF) and Dhillon's algorithm, particularly in very noisy data. Further analysis of the algorithm on real datasets obtained from the TCGA database showed that MESBC provided more accurate (i.e., smaller p-value) overall survival prediction in patients with lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) cancers when compared to the existing, gold-standard subtypes for lung cancers (integrative clustering). Furthermore, MESBC detected several genes with significant prognostic value in both LUAD and LUSC patients. External validation on an independent, unseen GEO dataset of LUAD showed that MESBC-derived clusters based on TCGA data still exhibited clear biclustering patterns and consistent, outstanding prognostic predictability, demonstrating robust generalizability of MESBC. Therefore, MESBC could potentially be used as a risk stratification tool to optimize the treatment for the patient, improve the selection of patients for clinical trials, and contribute to the development of novel therapeutic agents.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Carcinoma de Células Escamosas / Carcinoma de Pulmón de Células no Pequeñas / Adenocarcinoma del Pulmón / Neoplasias Pulmonares Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Comput Biol Chem Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA / QUIMICA Año: 2024 Tipo del documento: Article Pais de publicación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Carcinoma de Células Escamosas / Carcinoma de Pulmón de Células no Pequeñas / Adenocarcinoma del Pulmón / Neoplasias Pulmonares Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Comput Biol Chem Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA / QUIMICA Año: 2024 Tipo del documento: Article Pais de publicación: Reino Unido