Train Multinomial Rank-Based Model

This function trains a multinomial logistic regression model using ranked gene expression. Optionally performs cross-validation to select the optimal regularization parameter.

Usage

trainRankModel(
  data = NULL,
  labels = NULL,
  alpha = 0.1,
  cv = FALSE,
  nfolds = 5,
  ...
)

Arguments

data

A gene expression matrix with genes as rows and cells (or spatial spots) as columns. Can be a Seurat object, a SummarizedExperiment object, a dense numeric matrix, or a sparse dgCMatrix.

labels

A vector of cell type labels (one per column of data).

alpha

Elastic net mixing parameter. 1 = LASSO, 0 = Ridge. Default is 0.1.

cv

Logical. If TRUE, performs cross-validation using cv.glmnet. Default is FALSE.

nfolds

Number of folds for cross-validation (only used if cv = TRUE). Default is 5.

...

Additional arguments passed to computeRankedMatrix.

Value

A fitted glmnet or cv.glmnet model object.

See also

computeRankedMatrix, predictRankModel

Examples

# Read in single-cell reference data
seu_sc <- readRDS(system.file("extdata", "seu_sc.rds",
    package = "RankMap"
))

# Extract normalized expression data
mat <- extractData(seu_sc)

# Train a model
set.seed(42)
model <- trainRankModel(mat, seu_sc$cell_type)

# Train a model with cross-validation
model <- trainRankModel(
    data = mat,
    labels = seu_sc$cell_type,
    cv = TRUE,
    nfolds = 3
)