Rapid single-cell cytometry data visualization with EmbedSOM

Abstract

E cient unbiased data analysis is a major challenge for laboratories handling large cytometry datasets. We present EmbedSOM, a non-linear embedding algorithm based on FlowSOM that improves the analyses by providing high-performance visualization of complex single cell distributions within cellular populations and their transition states. e algorithm is designed for linear scaling and speed suitable for interactive analyses of millions of cells without downsampling. At the same time, the visualization quality is competitive with current stateof-art algorithms. We demonstrate the properties of EmbedSOM on work ows that improve two essential types of analyses: e native ability of EmbedSOM to align population positions in embedding is used for comparative analysis of multi-sample data, and the connection to FlowSOM is exploited for simplifying the supervised hierarchical dissection of cell populations. Additionally, we discuss the visualization of the trajectories between cellular states facilitated by the local linearity of the embedding.

From Bivariate to Multivariate Analysis of Cytometric Data: Overview of Computational Methods and Their Application in Vaccination Studies

“…Moreover, it is important to remark that the algorithm includes a series of stochastic steps and consequently different analyses will give slightly different results. Recently, new dimensionality reduction tools such as EmbedSOM [39], diffusion maps [58,59], Fit-SNE [42], and UMAP (Uniform Manifold Approximation and Projection) [40] have been developed and applied to single-cell data to overcame t-SNE limitations. Dimensionality reduction is purely a visualization tool and does not allow the exact quantification of the identified population that requires a subsequent step.…”

Generalized EmbedSOM on quadtree-structured self-organizing maps

“…EmbedSOM is a dimensionality reduction (DR) algorithm for single-cell cytometry data, designed for high scalability, computational efficiency and performance 1 . The design is based off FlowSOM 2 , which utilizes unsupervised manifold learning by self-organizing maps (SOMs) to find structure in the high-dimensional data, and process the result into a meaningful and easily interpretable clustering of the dataset.…”