LR_cal: Calculate Ligand-Receptor Interaction Scores

In IOBR: Immune Oncology Biological Research

Calculate Ligand-Receptor Interaction Scores

Description

Quantifies ligand-receptor interactions in the tumor microenvironment from bulk gene expression data using the easier package. This function processes raw counts or TPM data and computes interaction scores for each sample.

Usage

LR_cal(
  eset,
  data_type = c("count", "tpm"),
  id_type = "ensembl",
  cancer_type = "pancan"
)

Arguments

eset	Gene expression matrix with genes as rows and samples as columns.
data_type	Type of input data. Options are '"count"' or '"tpm"'. If '"count"', data will be converted to TPM before analysis.
id_type	Type of gene identifier. Default is '"ensembl"'.
cancer_type	Character string specifying the cancer type for easier. Default is '"pancan"' for pan-cancer analysis.

Value

Data frame containing ligand-receptor interaction scores with sample IDs as row names.

References

Lapuente-Santana, van Genderen, M., Hilbers, P., Finotello, F., & Eduati, F. (2021). Interpretable systems biomarkers predict response to immune-checkpoint inhibitors. Patterns (New York, N.Y.), 2(8), 100293. https://doi.org/10.1016/j.patter.2021.100293

Examples

# LR_cal requires HGNC gene symbols as rownames
# Create a simple example with gene symbols
example_genes <- c(
  "TGFB1", "EGFR", "VEGFA", "PDGFB", "FGF2", "CXCL12",
  "CXCR4", "IL6", "IL6R", "TNF", "TNFRSF1A", "IFNG"
)
sim_eset <- as.data.frame(matrix(
  rnorm(length(example_genes) * 10, mean = 5, sd = 2),
  nrow = length(example_genes), ncol = 10
))
rownames(sim_eset) <- example_genes
colnames(sim_eset) <- paste0("Sample", 1:10)
## Not run: 
if (requireNamespace("easier", quietly = TRUE)) {
  tryCatch({
    lr <- LR_cal(eset = sim_eset, data_type = "tpm")
    head(lr)
  }, error = function(e) {
    message("Example skipped: could not download ExperimentHub data")
  })
}

## End(Not run)

IOBR documentation built on May 30, 2026, 5:07 p.m.