TOSCCA: a framework for interpretation and testing of sparse canonical correlations.
Bioinform Adv
; 4(1): vbae021, 2024.
Article
en En
| MEDLINE
| ID: mdl-38456127
ABSTRACT
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.
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1
Colección:
01-internacional
Base de datos:
MEDLINE
Idioma:
En
Revista:
Bioinform Adv
Año:
2024
Tipo del documento:
Article
País de afiliación:
Países Bajos