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ShinyButchR: Interactive NMF-based decomposition workflow of genome-scale datasets

Non-negative matrix factorization (NMF) has been widely used for the analysis of genomic data to perform feature extraction and signature identification due to the interpretability of the decomposed signatures. However, running a basic NMF analysis requires the installation of multiple tools and dep...

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Detalles Bibliográficos
Autores principales: Quintero, Andres, Hübschmann, Daniel, Kurzawa, Nils, Steinhauser, Sebastian, Rentzsch, Philipp, Krämer, Stephen, Andresen, Carolin, Park, Jeongbin, Eils, Roland, Schlesner, Matthias, Herrmann, Carl
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7750682/
https://www.ncbi.nlm.nih.gov/pubmed/33376806
http://dx.doi.org/10.1093/biomethods/bpaa022
Descripción
Sumario:Non-negative matrix factorization (NMF) has been widely used for the analysis of genomic data to perform feature extraction and signature identification due to the interpretability of the decomposed signatures. However, running a basic NMF analysis requires the installation of multiple tools and dependencies, along with a steep learning curve and computing time. To mitigate such obstacles, we developed ShinyButchR, a novel R/Shiny application that provides a complete NMF-based analysis workflow, allowing the user to perform matrix decomposition using NMF, feature extraction, interactive visualization, relevant signature identification, and association to biological and clinical variables. ShinyButchR builds upon the also novel R package ButchR, which provides new TensorFlow solvers for algorithms of the NMF family, functions for downstream analysis, a rational method to determine the optimal factorization rank and a novel feature selection strategy.