Identifying interphase vs mitotic cell cycle phases using oxidative stress and a proximity-based null model

Author:

Michelle Kovarik, Tyler Allcroft, Per Sebastian Skardal

Keyword:

Nonlinear Sciences, Pattern Formation and Solitons, Pattern Formation and Solitons (nlin.PS)

journal:

date:

2023-10-23 16:00:00

Abstract

Detecting communities in large complex networks has found a wide range of applications in physical, biological, and social sciences by identifying mesoscopic groups based on the links between individual units. Moreover, community detection approaches have been generalized to various data analysis tasks by constructing networks whose links depend on individual units' measurements. However, identifying well separated subpopulations in data sets, e.g., multimodality, still presents challenges due to both the inherent spatial nature of the resulting networks and the generic emergence of communities in such networks and the similarity between network structures and distance-dependent null models. Here we introduce a new spatially informed null model for this task that takes into account spatial structure but does not explicitly depend on distances between measurements. We find that community detection using this null model successfully identifies subpopulations in multimodal data and accurately does not for unimodal data. We apply this new null model to the task of identifying interphase vs mitotic cell cycle phases in a group of Dictyostelium discoideum cells using measurements of oxidative stress, which have been shown to correlate strongly with cell cycle behaviors.

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