Visualizing Data Velocity using DSNE

TL;DR

DSNE is a novel visualization technique that embeds high-dimensional data velocities into low-dimensional space, aiding understanding of dynamic processes like cell differentiation.

Contribution

It introduces DSNE, a variation of Stochastic Neighbor Embedding, for visualizing data velocities in low-dimensional maps.

Findings

Enables visualization of data point movements in 2D or 3D.

Assists in understanding biological processes such as cell differentiation.

Provides a new tool for dynamic data analysis.

Abstract

We present a new technique called "DSNE" which learns the velocity embeddings of low dimensional map points when given the high-dimensional data points with its velocities. The technique is a variation of Stochastic Neighbor Embedding, which uses the Euclidean distance on the unit sphere between the unit-length velocity of the point and the unit-length direction from the point to its near neighbors to define similarities, and try to match the two kinds of similarities in the high dimension space and low dimension space to find the velocity embeddings on the low dimension space. DSNE can help to visualize how the data points move in the high dimension space by presenting the movements in two or three dimensions space. It is helpful for understanding the mechanism of cell differentiation and embryo development.