Cosmonet: An r package for survival analysis using screening-network methods

Antonella Iuliano, Annalisa Occhipinti, Claudia Angelini, Italia De Feis, Pietro Liò

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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 (PI) to separate high-and low-risk patients, and (iv) a prediction step for patients’ risk class based on the evaluation of PIs. 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.

Original languageEnglish
Article number3262
JournalMathematics
Volume9
Issue number24
DOIs
Publication statusPublished - 15 Dec 2021

Bibliographical note

Funding Information:
Funding: This work was supported by the project “Antitumor Drugs and Vaccines from the Sea (ADViSE)” project (CUP B43D18000240007–SURF 17061BP000000011) funded by POR Campania FESR 2014-2020 “Technology Platform for Therapeutic Strategies against Cancer”—Action 1.2.1 and 1.2.2. P.L. is funded by the EU Horizon 2020 GO-DS21 (grant agreement No 848077). A.O. would like to thank the support from Earlier.org.

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.

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