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Preprocess an expression matrix for CLAMP

Source: R/utils.R
preprocessCLAMP.Rd

Cleans an expression matrix, filters genes by mean expression and variance, and returns the filtered matrix and per-gene statistics. To match preprocessCLAMPFBM(), values are transformed with log2(Y + 1) when the maximum value is at least 100 and log2_transform = TRUE. Missing values are replaced with zero regardless of log2_transform. Row variances use population variance (dividing by the number of samples), matching preprocessCLAMPFBM().

Usage

preprocessCLAMP(Y, mean_cutoff = 0, var_cutoff = 0, log2_transform = TRUE)

Arguments

Y

Numeric matrix of gene expression (rows = genes, cols = samples)

mean_cutoff

Numeric. Minimum row-mean required to keep a gene (default 0).

var_cutoff

Numeric. Minimum row-variance required to keep a gene (default 0).

log2_transform

Logical; enable automatic log2(x + 1) transformation when the maximum value is at least 100 (default TRUE). Set to FALSE to skip transformation. Missing values are still replaced with zero.

Value

A list with components:

  • Y_filtered: filtered matrix (genes x samples)

  • rowStats: data.frame with columns mean and variance for each kept gene

  • kept_rows: integer vector of the original row indices that were kept

Examples

# construct a small example matrix
mat <- matrix(
    c(
        1, 5, 10,
        2, 6, 11,
        3, 7, 12,
        4, 8, 13
    ),
    nrow = 4, byrow = FALSE,
    dimnames = list(paste0("gene", seq_len(4)), paste0("sample", seq_len(3)))
)

# keep genes with mean >= 6 and variance >= 2
res <- preprocessCLAMP(mat, mean_cutoff = 6, var_cutoff = 2)
#> Already on log scale or all NA
#> No NA values found