Extreme value analysis of gut microbial alterations in colorectal cancer

TL;DR

This paper introduces an extreme value analysis method to identify microbial signatures in colorectal cancer, providing a robust alternative to mean-based estimates and enhancing reliability in microbial community profiling.

Contribution

The study presents a novel extreme value analysis approach that captures microbial signatures effectively and is robust to variations in microbial community data.

Findings

Power law fit captures microbial signatures similar to meta-analyses

Method is robust to microbial community profiling variations

Points to future development of sensitive analytical methods

Abstract

Gut microbes play a key role in colorectal carcinogenesis, yet reaching a consensus on microbial signatures remains a challenge. This is in part due to a reliance on mean value estimates. We present an extreme value analysis for overcoming these limitations. By characterizing a power law fit to the relative abundances of microbes, we capture the same microbial signatures as more complex meta-analyses. Importantly, we show that our method is robust to the variations inherent in microbial community profiling and point to future directions for developing sensitive, reliable analytical methods.