Fig. 5

A schematic illustration of the proposed autoencoder-based cluster ensemble framework. The first step is the sampling of multiple random projections from the original input scRNA-seq data set. A separate autoencoder artificial neural network is trained on each of these random projections and used to encode the data to a smaller-dimensional space. Subsequently, clustering of each encoded dataset is conducted using an arbitrary clustering method; the final clustering output is produced by integrating individual clustering results using a fixed-point algorithm [31]