Visual analysis of mass cytometry data by hierarchical stochastic neighbour embedding reveals rare cell types

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.

Loss of BAP1 expression is associated with an immunosuppressive microenvironment in uveal melanoma, with implications for immunotherapy development

“…Data from mass cytometry were normalised to the EQ 4-element bead signal using normalisation software version 2 (Fluidigm). Live Ir + CD45 + cells were manually gated as previously described [20] (supplementary material, Figure S1A, bottom), and FCS files were downloaded for concatenated analysis using Cytosplore V.2.2.1 for further downstream analysis by hierarchical stochastic neighbour embedding (HSNE) using a coefficient of 4 [21], or individually processed for visualisation of t-distributed stochastic neighbour embedding (viSNE) analysis in Cytobank. For accurate clustering and frequency calculations, a cut-off of 1000 events was considered for the final gate.…”

Systems vaccinology and big data in the vaccine development chain

“…78,79 Hierarchical Stochastic Neighborhood Embedding (HSNE) enables us to visualize data of cellular composition of millions of cells in detail up to the single-cell level. 80 Although these graphs provide an easier perspective on trends in the data sets, they are not designed to provide information on the biological function of individual data points. The development of data mining tools ( Fig.…”

Modelling acute myeloid leukaemia in a continuum of differentiation states

“…In van Unen et al (2017), a new technique for examining high dimensional mass cytometry data, known as hierarchical stochastic neighbor embedding (HSNE) is presented. Mass cytometry allows for the examination of several cellular markers on samples made up of vast quantities of cells.…”

“…Thus, HSNE is an approach that is useful for data that requires different levels of detail at different scales. An illuminating graphical representation of the HSNE process can be found in van Unen et al (2017), Figure 1.…”