A multi-encoder variational autoencoder controls multiple transformational features in single-cell image analysis

A multi-encoder variational autoencoder controls multiple transformational features in single-cell image analysis

The Multi-Encoder Variational AutoEncoder (ME-VAE) is a computational model that can control for multiple transformational features in single-cell imaging data, enabling researchers to extract meaningful single-cell information and better separate heterogeneous cell types.

Bibliographic Details
Main Authors: Luke Ternes, Mark Dane, Sean Gross, Marilyne Labrie, Gordon Mills, Joe Gray, Laura Heiser, Young Hwan Chang
Format: Article
Language:English
Published: Nature Portfolio 2022-03-01
Series:Communications Biology
Online Access:https://doi.org/10.1038/s42003-022-03218-x

Internet

https://doi.org/10.1038/s42003-022-03218-x

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