SurvBal：用于生存结果的组合微生物组平衡。

SurvBal: compositional microbiome balances for survival outcomes.

机构信息

Division of Biostatistics, Department of Population Health Sciences, Weill Cornell Medicine, New York, NY 10065, United States.

Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, United States.

出版信息

Bioinformatics. 2024 Oct 1;40(10). doi: 10.1093/bioinformatics/btae612.

DOI:10.1093/bioinformatics/btae612

PMID:39404767

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11639162/

Abstract

SUMMARY

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 taxa's relative abundances.

AVAILABILITY AND IMPLEMENTATION

The SurvBal R package and Shiny app can be accessed at https://github.com/yinglia/SurvBal and https://yinglistats.shinyapps.io/shinyapp-survbal/.

摘要

在微生物组分析实验中，识别与连续和二分结果相关的细菌分类群平衡是一种越来越常见的分析目标。SurvBal 能够选择与许多生物医学研究中非常关注的删失生存或事件时间结果相关的平衡。该软件包中包含最常用的生存模型——Cox 比例风险和参数生存模型，它们与逐步选择程序结合使用，以确定与微生物组相关的最佳关联平衡，即两组分类群相对丰度的几何平均值之比。