SIDISH.SIDISH

class SIDISH.SIDISH(adata, bulk, device='cpu', seed=1234, use_spatial_graph=False, k_neighbors=None)[source]

SIDISH (Semi-Supervised Iterative Deep Learning for Identifying High-Risk Cells).

This framework integrates single-cell and bulk RNA-seq data to identify High-Risk cancer cells and potential biomarkers.

Parameters:

  • adata (AnnData) – Single-cell RNA-seq data.

  • bulk (pd.DataFrame) – Bulk RNA-seq data.

  • use_spatial_graph (bool, optional) – Whether to use spatial graph information (default=False).

  • k_neighbors (int, optional) – Number of neighbors to use for constructing the spatial graph (default=5).

  • device (str) – Computation device (‘cpu’ or ‘cuda’).

  • seed (int, optional) – Random seed for reproducibility (default=1234).

__init__(adata, bulk, device='cpu', seed=1234, use_spatial_graph=False, k_neighbors=None)[source]

Methods

__init__(adata, bulk[, device, seed, ...])

analyze_perturbation_effects()

annotateCells(test_adata, percentile_cells, mode)

getEmbedding_adata()

Extracts latent representations from the trained VAE.

get_MarkerGenes([logfc_threshold, ...])

Identifies marker genes for the specified group using different statistical methods.

get_embedding([n_neighbors, resolution, ...])

get_percentille(percentile)

init_Phase1(epochs, i_epochs, latent_size, ...)

Initializes Phase 1: training a Variational Autoencoder (VAE) on single-cell RNA-seq data.

init_Phase2(epochs, hidden, lr, dropout, ...)

Initializes Phase 2: training a Deep Cox model for survival analysis using bulk RNA-seq data.

plotUMAP(resolution[, figure_size, ...])

Performs UMAP dimensionality reduction and Leiden clustering on the latent space.

plot_CellType_UMAP([size, resolution, celltype])

plot_HighRisk_UMAP([size, resolution, celltype])

plot_KM([penalizer, data_name, ...])

Plot Kaplan-Meier survival curves for High-Risk and background patient groups.

plot_double_Perturbation_Heatmap(...[, top_n])

plot_perturbation_UMAP_default(genes_of_interest)

Generates UMAP visualizations for specified genes after in-silico perturbation.

plot_perturbation_UMAP_differential(...[, ...])

Generates UMAP visualizations for specified genes after in-silico perturbation.

plot_top_perturbed_genes(gene_data[, top_n])

Plots a barplot of the top N genes with the highest percentage reduction in High-Risk cells after in-silico perturbation.

reload(path[, num_workers])

run_Perturbation([n_jobs])

run_double_Perturbation(genes[, top_n, ...])

run_double_Perturbation_score(genes[, ...])

set_adata()

train(iterations, percentile, steepness, path)

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