Multiparameter persistent homology landscapes identify immune cell spatial patterns in tumors.

ABSTRACT: Highly resolved spatial data of complex systems encode rich and nonlinear information. Quantification of heterogeneous and noisy data-often with outliers, artifacts, and mislabeled points-such as those from tissues, remains a challenge. The mathematical field that extracts information from the shape of data, topological data analysis (TDA), has expanded its capability for analyzing real-world datasets in recent years by extending theory, statistics, and computation. An extension to the standard theory to handle heterogeneous data is multiparameter persistent homology (MPH). Here we provide an application of MPH landscapes, a statistical tool with theoretical underpinnings. MPH landscapes, computed for (noisy) data from agent-based model simulations of immune cells infiltrating into a spher

SUBMITTER: Vipond O 

PROVIDER: S-EPMC8522280 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature