SuMO-Fil: Supervised multi-omic filtering prior to performing network analysis.

Multi-omic analyses that integrate many high-dimensional datasets often present significant deficiencies in statistical power and require time consuming computations to execute the analytical methods. We present SuMO-Fil to remedy against these issues which is a pre-processing method for Supervised...

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Autores principales:	Lorin M Towle-Miller, Jeffrey C Miecznikowski, Fan Zhang, David L Tritchler
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/612fc3b4f9594a5cb31bcde04825371e
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id	oai:doaj.org-article:612fc3b4f9594a5cb31bcde04825371e
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spelling	oai:doaj.org-article:612fc3b4f9594a5cb31bcde04825371e2021-12-02T20:18:48ZSuMO-Fil: Supervised multi-omic filtering prior to performing network analysis.1932-620310.1371/journal.pone.0255579https://doaj.org/article/612fc3b4f9594a5cb31bcde04825371e2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0255579https://doaj.org/toc/1932-6203Multi-omic analyses that integrate many high-dimensional datasets often present significant deficiencies in statistical power and require time consuming computations to execute the analytical methods. We present SuMO-Fil to remedy against these issues which is a pre-processing method for Supervised Multi-Omic Filtering that removes variables or features considered to be irrelevant noise. SuMO-Fil is intended to be performed prior to downstream analyses that detect supervised gene networks in sparse settings. We accomplish this by implementing variable filters based on low similarity across the datasets in conjunction with low similarity with the outcome. This approach can improve accuracy, as well as reduce run times for a variety of computationally expensive downstream analyses. This method has applications in a setting where the downstream analysis may include sparse canonical correlation analysis. Filtering methods specifically for cluster and network analysis are introduced and compared by simulating modular networks with known statistical properties. The SuMO-Fil method performs favorably by eliminating non-network features while maintaining important biological signal under a variety of different signal settings as compared to popular filtering techniques based on low means or low variances. We show that the speed and accuracy of methods such as supervised sparse canonical correlation are increased after using SuMO-Fil, thus greatly improving the scalability of these approaches.Lorin M Towle-MillerJeffrey C MiecznikowskiFan ZhangDavid L TritchlerPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0255579 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Lorin M Towle-Miller Jeffrey C Miecznikowski Fan Zhang David L Tritchler SuMO-Fil: Supervised multi-omic filtering prior to performing network analysis.
description	Multi-omic analyses that integrate many high-dimensional datasets often present significant deficiencies in statistical power and require time consuming computations to execute the analytical methods. We present SuMO-Fil to remedy against these issues which is a pre-processing method for Supervised Multi-Omic Filtering that removes variables or features considered to be irrelevant noise. SuMO-Fil is intended to be performed prior to downstream analyses that detect supervised gene networks in sparse settings. We accomplish this by implementing variable filters based on low similarity across the datasets in conjunction with low similarity with the outcome. This approach can improve accuracy, as well as reduce run times for a variety of computationally expensive downstream analyses. This method has applications in a setting where the downstream analysis may include sparse canonical correlation analysis. Filtering methods specifically for cluster and network analysis are introduced and compared by simulating modular networks with known statistical properties. The SuMO-Fil method performs favorably by eliminating non-network features while maintaining important biological signal under a variety of different signal settings as compared to popular filtering techniques based on low means or low variances. We show that the speed and accuracy of methods such as supervised sparse canonical correlation are increased after using SuMO-Fil, thus greatly improving the scalability of these approaches.
format	article
author	Lorin M Towle-Miller Jeffrey C Miecznikowski Fan Zhang David L Tritchler
author_facet	Lorin M Towle-Miller Jeffrey C Miecznikowski Fan Zhang David L Tritchler
author_sort	Lorin M Towle-Miller
title	SuMO-Fil: Supervised multi-omic filtering prior to performing network analysis.
title_short	SuMO-Fil: Supervised multi-omic filtering prior to performing network analysis.
title_full	SuMO-Fil: Supervised multi-omic filtering prior to performing network analysis.
title_fullStr	SuMO-Fil: Supervised multi-omic filtering prior to performing network analysis.
title_full_unstemmed	SuMO-Fil: Supervised multi-omic filtering prior to performing network analysis.
title_sort	sumo-fil: supervised multi-omic filtering prior to performing network analysis.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/612fc3b4f9594a5cb31bcde04825371e
work_keys_str_mv	AT lorinmtowlemiller sumofilsupervisedmultiomicfilteringpriortoperformingnetworkanalysis AT jeffreycmiecznikowski sumofilsupervisedmultiomicfilteringpriortoperformingnetworkanalysis AT fanzhang sumofilsupervisedmultiomicfilteringpriortoperformingnetworkanalysis AT davidltritchler sumofilsupervisedmultiomicfilteringpriortoperformingnetworkanalysis
_version_	1718374227771392000

SuMO-Fil: Supervised multi-omic filtering prior to performing network analysis.

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