Cell-type classification using Adversarial Autoencoders

The default classification algorithm is based on Pearson correlation as this has been shown to be effective for automatic classification of cell types for single cell RNAseq experiments. This proved to be both highly performant and accurate also for spatial gene expression data. However, it may be desirable to explore other classification methods.

One recent and exciting Deep Learning framework that achieve competitive results in generative modeling and semi-supervised classification tasks are adversarial autoencoders.

SSAM implements a modified version of adversarial autoencoder classifier based on the original implementation by Shahar Azulay.

Mapping cell types using an adversarial autoencoder

In order to use the AAEC classification of pixels instead of the Pearson correlation based method, simply replace analysis.map_celltypes() with :

analysis.map_celltypes_aaec(epochs=1000, seed=0, batch_size=1000, chunk_size=100000, z_dim=10, noise=0)