Compute deconvolution benchmark

Usage

compute.benchmark(
  deconvolution,
  groundtruth,
  cells_extra = NULL,
  corr_type = "spearman",
  scatter = TRUE,
  plot = FALSE,
  pval = 0.05,
  file_name = NULL,
  width = 16,
  height = 8
)

Arguments

deconvolution: The deconvolution matrix output from compute.deconvolution()
groundtruth: A matrix with the cell type proportions (samples as rows and cell types as columns). Cell types names should correspond to the ones on the deconvolution matrix.
cells_extra: A string specifying the cells names to consider and that are not including in the nomenclature of multideconv (see Readme)
corr_type: Secifies the type of correlations to compute ('spearman' or 'pearson').
scatter: Boolean value to specify if scatter plots should be returned.
plot: Boolean value to whether save or not the plot of the benchmark in the Results/ directory.
pval: A numeric value with the pvalue to use for selecting significant features.
file_name: A string specifying the name of the plot saved in Results/
width: A numeric value with the width for the returned plot.
height: A numeric value with the height for the returned plot.

Value

A correlation matrix between the cell type deconvolution combinations and the real cell proportions.

Examples


data("deconvolution")
data("cells_groundtruth")

corr_matrix = compute.benchmark(deconvolution, cells_groundtruth, cells_extra = "Myeloid.cells",
                                corr_type = "spearman", scatter = FALSE)
#> No id variables; using all as measure variables
#> No id variables; using all as measure variables