Methods for discovery and characterization of cell subsets in high dimensional mass cytometry data.

ABSTRACT: The flood of high-dimensional data resulting from mass cytometry experiments that measure more than 40 features of individual cells has stimulated creation of new single cell computational biology tools. These tools draw on advances in the field of machine learning to capture multi-parametric relationships and reveal cells that are easily overlooked in traditional analysis. Here, we introduce a workflow for high dimensional mass cytometry data that emphasizes unsupervised approaches and visualizes data in both single cell and population level views. This workflow includes three central components that are common across mass cytometry analysis approaches: (1) distinguishing initial populations, (2) revealing cell subsets, and (3) characterizing subset features. In the implementation describ

SUBMITTER: Diggins KE 

PROVIDER: S-EPMC4468028 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature