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COSMONET: An R Package for Survival Analysis Using Screening-Network Methods

Author

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  • Antonella Iuliano

    (Dipartimento di Matematica, Informatica ed Economia (DIMIE), Università degli Studi della Basilicata, 85100 Potenza, Italy
    These first authors contributed equally to this work.)

  • Annalisa Occhipinti

    (School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough TS1 3BX, UK
    These first authors contributed equally to this work.)

  • Claudia Angelini

    (Istituto per le Applicazioni del Calcolo “Mauro Picone” (IAC), Consiglio Nazionale delle Ricerche, 80131 Naples, Italy
    These authors contributed equally to this work.)

  • Italia De Feis

    (Istituto per le Applicazioni del Calcolo “Mauro Picone” (IAC), Consiglio Nazionale delle Ricerche, 80131 Naples, Italy
    These authors contributed equally to this work.)

  • Pietro Liò

    (Computer Laboratory, University of Cambridge, Cambridge CB2 1TN, UK
    These authors contributed equally to this work.)

Abstract

Identifying relevant genomic features that can act as prognostic markers for building predictive survival models is one of the central themes in medical research, affecting the future of personalized medicine and omics technologies. However, the high dimension of genome-wide omic data, the strong correlation among the features, and the low sample size significantly increase the complexity of cancer survival analysis, demanding the development of specific statistical methods and software. Here, we present a novel R package, COSMONET (COx Survival Methods based On NETworks), that provides a complete workflow from the pre-processing of omics data to the selection of gene signatures and prediction of survival outcomes. In particular, COSMONET implements (i) three different screening approaches to reduce the initial dimension of the data from a high-dimensional space p to a moderate scale d , (ii) a network-penalized Cox regression algorithm to identify the gene signature, (iii) several approaches to determine an optimal cut-off on the prognostic index ( P I ) to separate high- and low-risk patients, and (iv) a prediction step for patients’ risk class based on the evaluation of P I s . Moreover, COSMONET provides functions for data pre-processing, visualization, survival prediction, and gene enrichment analysis. We illustrate COSMONET through a step-by-step R vignette using two cancer datasets.

Suggested Citation

  • Antonella Iuliano & Annalisa Occhipinti & Claudia Angelini & Italia De Feis & Pietro Liò, 2021. "COSMONET: An R Package for Survival Analysis Using Screening-Network Methods," Mathematics, MDPI, vol. 9(24), pages 1-25, December.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:24:p:3262-:d:703501
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    References listed on IDEAS

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    5. Renaud Tissier & Jeanine Houwing-Duistermaat & Mar Rodríguez-Girondo, 2018. "Improving stability of prediction models based on correlated omics data by using network approaches," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-23, February.
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    Cited by:

    1. Claudia Angelini & Daniela De Canditiis & Anna Plaksienko, 2022. "Jewel 2.0 : An Improved Joint Estimation Method for Multiple Gaussian Graphical Models," Mathematics, MDPI, vol. 10(21), pages 1-20, October.

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