MiSurv: an Integrative Web Cloud Platform for User-Friendly Microbiome Data Analysis with Survival Responses.

Area of Science:

• Microbiome research
• Computational biology
• Survival analysis

Background:

• Clinical trials often generate survival data (time-to-event) stratified by treatment groups.
• Existing web platforms facilitate microbiome data processing but lack survival analysis integration.
• Understanding the interplay between the microbiome, treatments, and disease progression is crucial.

Purpose of the Study:

• To introduce MiSurv, an integrative web cloud platform for analyzing microbiome data with survival responses.
• To provide a comprehensive solution for microbiome data processing and survival analysis in clinical research.

Main Methods:

• MiSurv comprises a data processing module and four analytical modules: comparative survival analysis, microbial composition comparison, microbiome-survival association testing, and predictive modeling.
• The platform was demonstrated using a trial examining antibiotic effects on type 1 diabetes (T1D) onset and gut microbiome composition, and the microbiome's role in T1D survival.

Main Results:

• MiSurv enables comparative survival analysis between treatment groups based on microbiome data.
• It facilitates analysis of microbial composition differences and associations with survival outcomes.
• The platform supports the development of predictive models for survival using microbial taxa.

Conclusions:

• MiSurv addresses the unmet need for a web-based platform combining microbiome and survival data analysis.
• The platform offers a valuable tool for researchers investigating treatment effects, microbiome roles, and disease progression.
• MiSurv is accessible via a web server and local installation for broad research community adoption.