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Perform ssGSEA-based Subtyping Using Marker Gene Sets

Source: R/Classifier.ssGSEA.R
Classifier.ssGSEA.Rd

This function performs single-sample Gene Set Enrichment Analysis (ssGSEA) to assign subtypes to samples based on marker gene sets. It calculates enrichment scores with permutation testing and predicts sample subtypes based on the most significant enrichment results.

Usage

Classifier.ssGSEA(
  data.test,
  marker.list,
  dir.file = ".",
  gct.filename = "data.gct",
  number.perms = 100,
  tolerate.mixed = FALSE,
  method = c("internal", "GSVA", "external"),
  seed = 12345
)

Arguments

data.test

A matrix or data frame representing the input expression data, where rows are genes and columns are samples.

marker.list

A named list of marker gene sets, where each list element corresponds to a specific subtype or category of interest.

dir.file

Character. Directory for saving the output files (default: '.'). Set to NULL to skip file output.

gct.filename

Character. The filename for the generated GCT file (default: 'data.gct').

number.perms

Integer. Number of permutations for ssGSEA analysis (default: 100).

tolerate.mixed

Logical. Whether to allow "Mixed" predictions when multiple gene sets have the same minimum p-value (default: FALSE).

method

Character. The ssGSEA implementation to use: "internal" (built-in), "GSVA" (requires GSVA package), or "external" (requires ssgsea.GBM.classification package). Default: "internal".

seed

Integer. Random seed for reproducibility (default: 12345).

Value

A data frame with the following columns:

  • ID: Sample identifiers.

  • Predict: Predicted subtype for each sample.

  • Columns with _pval: P-values for each marker gene set or subtype.

Details

The function:

  1. Calculates ssGSEA enrichment scores for each marker gene set in every sample.

  2. Performs permutation testing to estimate statistical significance.

  3. Predicts subtypes by identifying the marker gene set with the most significant enrichment (smallest p-value).

  4. If tolerate.mixed is TRUE and multiple gene sets share the same minimum p-value, the sample is labeled as "Mixed".

References

Wang Q, Hu B, Hu X, Kim H, Squatrito M, Scarpace L, et al. Tumor Evolution of Glioma-Intrinsic Gene Expression Subtypes Associates with Immunological Changes in the Microenvironment. Cancer Cell. July 2017;32(1):42-56.e6.

Author

Zaoqu Liu; Email: liuzaoqu@163.com

Examples

# Simulated expression data
data.test <- matrix(rnorm(10000), nrow = 100, ncol = 100)
rownames(data.test) <- paste0("Gene", 1:100)
colnames(data.test) <- paste0("Sample", 1:100)

# Example marker list
marker.list <- list(
  Subtype1 = c("Gene1", "Gene2", "Gene3"),
  Subtype2 = c("Gene4", "Gene5", "Gene6")
)

# Run ssGSEA-based subtyping
result <- Classifier.ssGSEA(
  data.test = data.test,
  marker.list = marker.list,
  number.perms = 50,
  tolerate.mixed = TRUE
)
print(result)