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Perform LP-SGL Screening Analysis

Source: R/21f-LP_GSL_Screen.R
DoLP_SGL.Rd

Identifies phenotype-associated cell subpopulations using Lasso-Penalized Sparse Group Lasso (LP-SGL) with Leiden community detection. This method integrates bulk and single-cell RNA-seq data to identify cell subpopulations associated with phenotypic outcomes.

Usage

DoLP_SGL(
  matched_bulk,
  sc_data,
  phenotype,
  label_type = "LP_SGL",
  family = c("logit", "cox", "linear"),
  resolution = 0.6,
  alpha = 0.5,
  nfold = 5,
  dge_analysis = list(run = FALSE, logFC_threshold = 1, pval_threshold = 0.05),
  ...
)

Arguments

matched_bulk

Bulk expression matrix (features × samples)

sc_data

Single-cell RNA-seq data (Seurat object)

phenotype

Binary phenotype vector for bulk samples

label_type

Character specifying phenotype label type (default: "LP_SGL")

family

Type of regression model: "logit" (logistic), "cox" (Cox), or "linear" (linear regression)

resolution

Resolution parameter for Leiden clustering (default: 0.6)

alpha

Alpha parameter for SGL balancing L1 and L2 penalties (default: 0.5)

nfold

Number of folds for cross-validation (default: 5)

dge_analysis

List controlling differential expression analysis:

  • run: Whether to run DEG analysis (default: FALSE)

  • logFC_threshold: Log fold change threshold (default: 1)

  • pval_threshold: P-value threshold (default: 0.05)

...

Additional arguments passed to preprocessing functions, e.g.:

  • verbose: Whether to print progress messages (default: TRUE)

  • seed: Random seed for reproducibility (default: 123L)

  • assay: Assay to use for clustering (default: "RNA")

Value

A list containing:

scRNA_data

Seurat object with LP-SGL results integrated

sgl_fit

Fitted SGL model object

cvfit

Cross-validation results

dge_res

Differential expression results if requested (NULL otherwise)

References

Li J, Zhang H, Mu B, Zuo H, Zhou K. Identifying phenotype-associated subpopulations through LP_SGL. Briefings in Bioinformatics. 2023 Nov 22;25(1):bbad424.

See also

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

Examples

if (FALSE) { # \dontrun{
# Example using simulated data
set.seed(123)

# Create simulated data
bulk_data <- matrix(rnorm(1000*50), nrow=1000, ncol=50)
sc_data <- matrix(rnorm(1000*500), nrow=1000, ncol=500)
phenotype <- rep(c(0, 1), each=25)

# Run LP-SGL analysis
results <- DoLP_SGL(
matched_bulk = bulk_data,
sc_data = sc_data,
phenotype = phenotype,
family = "logit",
resolution = 0.6,
dge_analysis = list(run = TRUE, logFC_threshold = 1, pval_threshold = 0.05)
)

# Access results
lpsgl_seurat <- results$scRNA_data
sgl_model <- results$sgl_fit
deg_results <- results$dge_res
} # }