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Semi-Supervised Generative Autoencoder Models for Single-Cell Data

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Project Goal

To develop deep neural network (autoencoder) models for single-cell data and testing their application to generate a suitable data representation for downstream analysis of cell phenotypes, such as metabolic activity prediction.

Results & Resources

The Heinaniemi lab developed four semi-supervised network models that were benchmark against three published methods using CITE-seq data. All data and models can be found on their github repository, and the models are further described in their preprint.


Lead Investigator

Merja Heinaniemi
Merja Heinaniemi