SISUA: Semi-Supervised Generative Autoencoder for Single Cell Data

1 code implementation • ICML Workshop on Computational Biology 2019 2019 • Trung Ngo Trong, Roger Kramer, Juha Mehtonen, Gerardo González, Ville Hautamäki, Merja Heinäniemi

In this study, we propose models based on the Bayesian generative approach, where protein quantification available as CITE-seq counts from the same cells are used to constrain the learning process, thus forming a semi-supervised model.

Single-cell modeling