# SlowMoMan: a web app for discovery of important features along user-drawn trajectories in 2D embeddings

## TL;DR

SlowMoMan is a web app that helps users discover important features along paths they draw on 2D data embeddings, useful for analyzing complex datasets like in bioinformatics.

## Contribution

The novelty is a web-based tool that ranks original features based on their importance along user-drawn paths in nonlinear embeddings.

## Key findings

- SlowMoMan enables users to identify important features along paths in 2D embeddings by back-projecting to high-dimensional data.
- The tool is demonstrated to be useful in trajectory inference, spatial transcriptomics, and cell classification.
- The web app provides an intuitive interface for exploring complex datasets with nonlinear embeddings.

## Abstract

Nonlinear low-dimensional embeddings allow humans to visualize high-dimensional data, as is often seen in bioinformatics, where datasets may have tens of thousands of dimensions. However, relating the axes of a nonlinear embedding to the original dimensions is a nontrivial problem. In particular, humans may identify patterns or interesting subsections in the embedding, but cannot easily identify what those patterns correspond to in the original data.

Thus, we present SlowMoMan (SLOW Motions on MANifolds), a web application which allows the user to draw a one-dimensional path onto a 2D embedding. Then, by back-projecting the manifold to the original, high-dimensional space, we sort the original features such that those most discriminative along the manifold are ranked highly. We show a number of pertinent use cases for our tool, including trajectory inference, spatial transcriptomics, and automatic cell classification.

Software: https://yunwilliamyu.github.io/SlowMoMan/; Code: https://github.com/yunwilliamyu/SlowMoMan.