Calculate splicing diversity changes between two conditions.

Usage

calculate_fc(x, samples, control, method = "mean", pseudocount = 0)

Arguments

x: A matrix with the splicing diversity values.
samples: Character vector with an equal length to the number of columns in the input dataset, specifying the category of each sample.
control: Name of the control sample category, defined in the samples vector, e.g. control = 'Normal' or control = 'WT'.
method: Method to use for calculating the average splicing diversity value in a condition. Can be 'mean' or 'median'.
pseudocount: Numeric scalar. Small value added to non-positive observed group summaries to avoid zeros when computing differences and log2 fold-changes. If pseudocount <= 0 the function will automatically choose a scale-aware value equal to half the smallest positive observed group summary (i.e. half the smallest observed mean/median across groups); if no positive values are present the fallback is 1e-6. Rows with insufficient observations remain NA and are not imputed.

Value

A data.frame with mean or median value of splicing diversity across sample categories, the difference between these values and the log2 fold change values.

Details

The function uses a matrix of splicing diversity values in order to calculate mean or median differences and log2 fold changes between two conditions.

Examples

# Simulate splicing diversity matrix (4 genes x 4 samples)
mat <- matrix(c(
  0.5, 0.6, 0.8, 0.9,  # gene1: low control, high treatment
  0.7, 0.75, 0.6, 0.5  # gene2: high control, low treatment
), nrow = 2, byrow = TRUE)
samples <- c('Normal', 'Normal', 'Tumor', 'Tumor')
result <- calculate_fc(mat, samples, control = 'Normal', method = 'mean')
head(result)
#>    Tumor_mean Normal_mean mean_difference log2_fold_change
#> V1       0.85       0.550           0.300        0.6280312
#> V2       0.55       0.725          -0.175       -0.3985494