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Compute Ranked Gene Expression Matrix

Source: R/compute_ranked_matrix.R
ComputeRankedMatrix.Rd

This function transforms a gene expression matrix by applying top-k filtering, optional rank transformation, binning, scaling, and/or expression weighting.

Usage

computeRankedMatrix(
  data,
  weight_by_expr = TRUE,
  rank_zeros = FALSE,
  bin_rank = TRUE,
  scale_rank = TRUE,
  k = 20,
  use_data = FALSE
)

Arguments

data

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

weight_by_expr

Logical. Whether to weight the ranks by log-transformed expression values. Default is TRUE.

rank_zeros

Logical. Whether to include zero values in the ranking. Default is FALSE.

bin_rank

Logical. Whether to discretize ranks into bins. Default is TRUE.

scale_rank

Logical. Whether to z-score normalize the ranks across columns. Default is TRUE.

k

Integer. Number of top expressed genes to retain per column. Default is 20.

use_data

Logical. If TRUE, returns the full input expression matrix (unfiltered and unranked). Default is FALSE.

Value

A numeric matrix of ranked expression values (or the original expression matrix if use_data = TRUE). Output is always returned as a dense matrix.

Details

If use_data = TRUE, the function bypasses all transformation steps and returns the full input expression matrix. This is useful for benchmarking performance of rank-based versus raw log-normalized expression models.

See also

maskTopKGenes, trainRankModel, predictRankModel

Examples

mat <- matrix(runif(1000), nrow = 100)
ranked <- computeRankedMatrix(mat, k = 10)
raw_expr <- computeRankedMatrix(mat, use_data = TRUE)