Visual Analysis of Mass Cytometry Data by Hierarchical Stochastic Neighbour Embedding Reveals Rare Cell Types

Overview

Journal Nat Commun

Specialty Biology

Date 2017 Nov 25

PMID 29170529

Citations 112

Authors

Vincent van Unen

Thomas Hollt

Nicola Pezzotti

Na Li

Marcel J T Reinders

Elmar Eisemann

Frits Koning

Anna Vilanova

Boudewijn P F Lelieveldt

Affiliations

Soon will be listed here.

Abstract

Mass cytometry allows high-resolution dissection of the cellular composition of the immune system. However, the high-dimensionality, large size, and non-linear structure of the data poses considerable challenges for the data analysis. In particular, dimensionality reduction-based techniques like t-SNE offer single-cell resolution but are limited in the number of cells that can be analyzed. Here we introduce Hierarchical Stochastic Neighbor Embedding (HSNE) for the analysis of mass cytometry data sets. HSNE constructs a hierarchy of non-linear similarities that can be interactively explored with a stepwise increase in detail up to the single-cell level. We apply HSNE to a study on gastrointestinal disorders and three other available mass cytometry data sets. We find that HSNE efficiently replicates previous observations and identifies rare cell populations that were previously missed due to downsampling. Thus, HSNE removes the scalability limit of conventional t-SNE analysis, a feature that makes it highly suitable for the analysis of massive high-dimensional data sets.

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