Visualizing Transitions and Structure for High Dimensional Data Exploration

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Authors Kevin R. Moon, David van Dijk, +8 authors Smita Krishnaswamy
Journal/Conference Name bioRxiv
Paper Category
Paper Abstract In the era of ‘Big Data’ there is a pressing need for tools that provide human interpretable visualizations of emergent patterns in high-throughput high-dimensional data. Further, to enable insightful data exploration, such visualizations should faithfully capture and emphasize emergent structures and patterns without enforcing prior assumptions on the shape or form of the data. In this paper, we present PHATE (Potential of Heat-diffusion for Affinity-based Transition Embedding) an unsupervised low-dimensional embedding for visualization of data that is aimed at solving these issues. Unlike previous methods that are commonly used for visualization, such as PCA and tSNE, PHATE is able to capture and highlight both local and global structure in the data. In particular, in addition to clustering patterns, PHATE also uncovers and emphasizes progression and transitions (when they exist) in the data, which are often missed in other visualization-capable methods. Such 1 . CC-BY-NC-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was not . doi: bioRxiv preprint first posted online Mar. 24, 2017;
Date of publication 2017
Code Programming Language R

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