TOSCCA: a framework for interpretation and testing of sparse canonical correlations.

Autor:	Senar N; Department of Epidemiology & Data Science, Amsterdam School of Public Health, Amsterdam UMC, 1105 AZ Nord-Holland, The Netherlands., van de Wiel M; Department of Epidemiology & Data Science, Amsterdam School of Public Health, Amsterdam UMC, 1105 AZ Nord-Holland, The Netherlands., Zwinderman AH; Department of Epidemiology & Data Science, Amsterdam School of Public Health, Amsterdam UMC, 1105 AZ Nord-Holland, The Netherlands., Hof MH; Department of Epidemiology & Data Science, Amsterdam School of Public Health, Amsterdam UMC, 1105 AZ Nord-Holland, The Netherlands.
Jazyk:	angličtina
Zdroj:	Bioinformatics advances [Bioinform Adv] 2024 Feb 21; Vol. 4 (1), pp. vbae021. Date of Electronic Publication: 2024 Feb 21 (Print Publication: 2024).
DOI:	10.1093/bioadv/vbae021
Abstrakt:	Summary: In clinical and biomedical research, multiple high-dimensional datasets are nowadays routinely collected from omics and imaging devices. Multivariate methods, such as Canonical Correlation Analysis (CCA), integrate two (or more) datasets to discover and understand underlying biological mechanisms. For an explorative method like CCA, interpretation is key. We present a sparse CCA method based on soft-thresholding that produces near-orthogonal components, allows for browsing over various sparsity levels, and permutation-based hypothesis testing. Our soft-thresholding approach avoids tuning of a penalty parameter. Such tuning is computationally burdensome and may render unintelligible results. In addition, unlike alternative approaches, our method is less dependent on the initialization. We examined the performance of our approach with simulations and illustrated its use on real cancer genomics data from drug sensitivity screens. Moreover, we compared its performance to Penalized Matrix Analysis (PMA), which is a popular alternative of sparse CCA with a focus on yielding interpretable results. Compared to PMA, our method offers improved interpretability of the results, while not compromising, or even improving, signal discovery. Availability and Implementation: The software and simulation framework are available at https://github.com/nuria-sv/toscca. Competing Interests: None declared. (© The Author(s) 2024. Published by Oxford University Press.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu