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Perform SIDISH Screening Analysis

Source: R/21g-SIDISH_Screen.R
DoSIDISH.Rd

Perform SIDISH Screening Analysis

Usage

DoSIDISH(
  matched_bulk,
  sc_data,
  phenotype,
  label_type = NULL,
  phenotype_class = "survival",
  assay = "RNA",
  sidish_params = list(),
  env_params = list(),
  ...
)

Arguments

matched_bulk

Matrix or data frame of preprocessed bulk RNA-seq expression data (genes x samples). Column names must match names/IDs in phenotype.

sc_data

A Seurat object containing scRNA-seq data to be screened.

phenotype

Phenotype data, either: - Patient survival Data frame with row names matching matched_bulk columns, colnames named "time" and "status"

label_type

Character specifying phenotype label type

phenotype_class

Type of phenotypic outcome (must be consistent with input data): - "survival": Survival infomation

assay

Seurat assay name, default: "RNA".

sidish_params

List of SIDISH algorithm parameters including: Preprocessing parameters:

  • patient_id: column name for patient identifier in metadata (default: "Sample")

  • celltype_name: column name for cell type annotation in metadata (default: "celltype_major")

  • processed: whether input data is already preprocessed (default: TRUE)

  • n_genes_by_counts: minimum number of genes expressed per cell filter threshold (default: 5000)

  • pct_counts_mt: maximum percentage of mitochondrial genes filter threshold (default: 10)

  • batch_correction: whether to perform batch correction (default: FALSE)

  • survival_: column name for survival time in phenotype data (default: "time")

  • status: column name for event status in phenotype data (default: "status")

Execution environment:

  • device: computation device, "cuda" for GPU acceleration or "cpu" for CPU-only (default: "cuda")

  • use_spatial_graph: whether to use spatial graph information (default: FALSE)

  • k_neighbors: number of neighbors for graph construction (default: NULL, auto-detected)

Phase 1: VAE training parameters:

  • phase1_epochs: total epochs for VAE training (default: 225)

  • phase1_i_epochs: interval epochs for VAE intermediate evaluation (default: 20)

  • phase1_latent_size: dimensionality of latent space (default: 32)

  • phase1_layer_dims: hidden layer dimensions as integer vector (default: c(512, 128))

  • phase1_batch_size: batch size for VAE training (default: 256)

  • phase1_optimizer: optimizer algorithm (default: "Adam")

  • phase1_lr: learning rate for VAE encoder/decoder (default: 1e-4)

  • phase1_lr_3: learning rate for additional VAE component (default: 1e-4)

  • phase1_dropout: dropout rate for VAE layers (default: 0)

  • phase1_type: VAE layer type, "Dense" or "Normal" (default: "Dense")

Phase 2: Deep Cox training parameters:

  • phase2_epochs: total epochs for Cox model training (default: 500)

  • phase2_hidden: number of hidden units in Cox model (default: 128)

  • phase2_lr: learning rate for Cox model (default: 1e-4)

  • phase2_dropout: dropout rate for Cox model (default: 0)

  • phase2_test_size: proportion of data held out for testing (default: 0.2)

  • phase2_batch_size_bulk: batch size for bulk data in Cox training (default: 256)

Training & risk definition parameters:

  • train_iterations: number of risk score iteration rounds (default: 5)

  • train_percentile: percentile threshold for high-risk cell selection (default: 0.95)

  • train_steepness: steepness parameter for risk score transformation (default: 30)

  • train_path: directory path for saving intermediate results (default: "./SIDISH_res/")

  • train_num_workers: number of data loading workers (default: 0)

  • train_distribution_fit: distribution fitting method, "fitted" or "default" (default: "fitted")

env_params

List of environment parameters for Python setup including:

  • env.name: conda/environment name (default: "r-reticulate-sidish-nvidia" for CUDA or "r-reticulate-sidish-cpu" for CPU)

  • env.type: environment type, "conda", "environment", or "venv" (default: "conda")

  • env.method: environment setup method, "system" or "conda" (default: "environment")

  • env.file: path to environment YAML file (default: system.file("conda/SIDISH_nvidia_environment.yml", package = "SigBridgeR") or CPU variant)

  • env.python_version: Python version (default: "3.12.12")

  • env.packages: named vector of Python packages and versions (default: c("numpy" = "1.26.4"), more packages included via env.file)

  • env.recreate: whether to recreate the environment if it already exists (default: FALSE)

  • env.use_conda_forge: whether to use the conda-forge channel (conda only, default: TRUE)

  • env.verbose: verbose output during environment setup (default: value from getFuncOption("verbose"))

...

Additional arguments passed to the function. Common parameters include:

verbose

Logical. Whether to print verbose output (default: TRUE).

Value

A named list containing:

scRNA_data

Modified single-cell data object with integrated screening results.

See also

Other screen_method: DoDEGAS(), DoLP_SGL(), DoPIPET(), DoSCIPAC(), DoScissor(), DoscAB(), DoscPAS(), DoscPP()