Workflow from Scientific Research

CeLEry takes an ST dataset as input for model training and a scRNA-seq dataset as input for cell location prediction. CeLEry has an optional data augmentation step, which optionally generates replicates of the ST data via a variational autoencoder. The generated data are then included in the training data. A deep neural network is trained to learn the relationship between the spot-wise gene expression and location information by minimizing a loss function that is specified according to the specific problem. Then, the trained model is applied to the scRNA-seq data to predict the location of each cell.

DOI