Sign in
BioinformaticsBioinformatics
Peer-reviewed journal article

SurvBal: Compositional Microbiome Balances for Survival Outcomes

Ying LiiD, Teresa LeeiD, Kai Marin, Xing HuaiD, Sujatha SrinivasaniD, David N. FredricksiD, John R. LeeiD, Wodan LingiD

Abstract

Identification of balances of bacterial taxa in relation to continuous and dichotomous outcomes is an increasingly frequent analytic objective in microbiome profiling experiments. SurvBal enables the selection of balances in relation to censored survival or time-to-event outcomes which are of considerable interest in many biomedical studies. The most commonly used survival models - the Cox proportional hazards and parametric survival models are included in the package, which are used in combination with step-wise selection procedures to identify the optimal associated balance of microbiome, i.e., the ratio of the geometric means of two groups of taxas relative abundances. Availability and implementationSurvBal is available as an R package and a Shiny app: https://github.com/yinglia/SurvBal, https://yinglistats.shinyapps.io/shinyapp-survbal/.

PDFDOICode

02 /Specification & Classification

Click any tag to browse matching papers
Citations
0
FWCI
0.00
Field percentile
13th percentile

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.

Similarity Map
SelectedSame fieldCross-field
THISHu '22Rivera-Pint '18Hu '21Lyu '26Fei '24Zhang '22
68% semantic matchBioinformatics
Yingtian Hu, Yunxiao Li, Glen A. Satten+1 more·bioRxiv·Mar 11, 2022· 3 citations