Dimensionality Reduction in the Cytobank Platform

The purpose of performing a dimensionality reduction is to get a visual overview of your data and enable rapid exploratory data analysis.

In the Cytobank platform, the dimensionality reduction suite is a powerful way for exploratory data analysis and data visualization. The suite now contains four dimensionality reduction algorithms that can reduce high-dimensional data to two dimensions for easy visualization:

• viSNE/tSNE¹ (a non-linear dimensionality reduction algorithm developed based on Stochastic Neighbor Embedding)
• tSNE-CUDA² (a state-of-the-art implementation of the t-SNE algorithm)
• UMAP³ (a non-linear dimension reduction algorithm)
• opt-SNE⁴ (a t-SNE based algorithm that can automatically optimize the early exaggeration process and the learning rate value for a t-SNE analysis run)

In cytometry data analysis, researchers usually run these dimensionality reduction algorithms after compensating, scaling and pre-gating the data.

Although each of the algorithms functions in a similar way — i.e., transforming data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation retains some meaningful properties of the original data — they do differ slightly in how they go about reducing dimensionality.

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How to Compare Results of Different Dimensionality Reduction Algorithms

In this video, Jason Emo — an application scientist here at Beckman Coulter Life Sciences — shows you how to use the tools in the new dimensionality reduction suite to analyze your high-dimensional cytometry data, as well as how to compare results of different dimensionality reduction algorithms in the Cytobank platform.

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Cytobank is a cloud-based platform for the analysis, storage, and sharing of flow and mass cytometry data. It offers machine learning-assisted analysis of high-dimensional, single-cell data and is designed to let you easily collaborate with colleagues from different departments and regions from any web-enabled device.

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References

1. L.J.P. van der Maaten, Journal of Machine Learning Research, 2014
2. Chan DM et al., Journal of Parallel and Distributed Computing, 2019
3. orey J. Nolet. et al., arXiv, 2020, arXiv:2008.00325 http://arxiv.org/abs/2008.00325
4. Belkina A. et al., Nat Communications, 2019