SIDISH.SIDISH.SIDISH.train

SIDISH.train(iterations, percentile, steepness, path, num_workers=0, show=True, distribution_fit='default')[source]

Trains the SIDISH framework iteratively, refining the identification of High-Risk cells.

This function iteratively updates High-Risk cell classifications by integrating single-cell and bulk RNA-seq data. Each iteration includes: - Training the VAE model on single-cell data. - Training the Deep Cox model on bulk RNA-seq survival data. - Updating weight matrices to improve High-Risk cell identification.

Parameters:

  • iterations (int) – Number of training iterations.

  • percentile (float) – Threshold percentile for defining High-Risk cells.

  • steepness (float) – Scaling factor for updating weights.

  • path (str) – Directory for saving model checkpoints.

  • num_workers (int, optional) – Number of parallel workers (default=8).

  • show (bool, optional) – If True, displays training progress (default=True).

Return type:

anndata._core.anndata.AnnData

Returns:

sc.AnnData – Updated AnnData object containing the refined High-Risk cell classifications.