SlowMoMan: a web app for discovery of important features along user-drawn trajectories in 2D embeddings.
Bioinform Adv
; 4(1): vbae095, 2024.
Article
in En
| MEDLINE
| ID: mdl-38962404
ABSTRACT
Motivation 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. Results:
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. Availability and implementation Software https//yunwilliamyu.github.io/SlowMoMan/; Code https//github.com/yunwilliamyu/SlowMoMan.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Language:
En
Journal:
Bioinform Adv
Year:
2024
Document type:
Article
Affiliation country:
Canadá