The impact of similarity metrics on cell-type clustering in highly multiplexed in situ imaging cytometry data.

ABSTRACT:

Motivation

The advent of highly multiplexed in situ imaging cytometry assays has revolutionized the study of cellular systems, offering unparalleled detail in observing cellular activities and characteristics. These assays provide comprehensive insights by concurrently profiling the spatial distribution and molecular features of numerous cells. In navigating this complex data landscape, unsupervised machine learning techniques, particularly clustering algorithms, have become essential tools. They enable the identification and categorization of cell types and subsets based on their molecular characteristics. Despite their widespread adoption, most clustering algorithms in use were initially developed for cell suspension technologies, leading to a potential mismatch in application. T

SUBMITTER: Willie E 

PROVIDER: S-EPMC10625459 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature