Cluster partitions and fitness landscapes of the Drosophila fly microbiome

TL;DR

This paper introduces cluster partitions and filtrations as new mathematical tools to analyze fitness landscapes, applied to Drosophila microbiome data, revealing insights into epistatic interactions beyond previous methods.

Contribution

It develops a novel combinatorial approach for analyzing fitness landscapes using cluster partitions and filtrations, enhancing understanding of epistatic interactions.

Findings

Identifies key epistatic regions in Drosophila microbiome data

Shows similarities and differences with previous landscape analysis methods

Locates new epistatic information where prior approaches are inconclusive

Abstract

Beerenwinkel et al.(2007) suggested studying fitness landscapes via regular subdivisions of convex polytopes. Building on their approach we propose cluster partitions and cluster filtrations of fitness landscapes as a new mathematical tool. In this way, we provide a concise combinatorial way of processing metric information from epistatic interactions. Using existing Drosophila microbiome data, we demonstrate similarities with and differences to the previous approach. As one outcome we locate interesting epistatic information where the previous approach is less conclusive.