Compute modules relationship — compute.modules.relationship • CellTFusion

Performs linear correlation between two matrices (e.g., TF modules and external features such as deconvolution estimates or pathway activities) across the same set of samples, visualizing only significant associations.

Usage

# S3 method for class 'modules.relationship'
compute(
  matA,
  matB,
  file_name,
  batch = NULL,
  width = 8,
  height = 8,
  par_mar = NULL,
  pval = 0.05,
  padj = F,
  cor_type = "p",
  return = F,
  vertical = F,
  plot = T,
  plot.grid = F,
  width.grid = 12,
  height.grid = 10,
  ncol.grid = NULL
)

Arguments

matA: A numeric matrix or data frame of features (samples x features).
matB: A numeric matrix or data frame of features to correlate with (samples x features).
file_name: A string indicating the base name (without extension) of the figure to be saved in the "Results/" folder.
batch: Optional vector indicating batch assignment for samples. If provided, partial correlations are computed controlling for batch.
width: An integer indicating the width (in inches) of the output PDF figure. Default is 8.
height: An integer indicating the height (in inches) of the output PDF figure. Default is 8.
par_mar: A numeric vector of length 4 specifying the margin sizes (bottom, left, top, right) for the heatmap. If NULL (default), reasonable defaults are chosen based on plot orientation.
pval: A numeric threshold for the p-value to define significance. Default is 0.05.
padj: Logical; if TRUE, applies Bonferroni correction for multiple testing. Default is FALSE.
cor_type: Type of correlation to compute: "p" (Pearson), "s" (Spearman), or "k" (Kendall). Default is "p".
return: Logical; if TRUE, the function returns a list containing the correlation matrix and a named list of significant feature names per module. Default is FALSE.
vertical: Logical; if TRUE, produces a vertical heatmap (traits on x-axis, modules on y-axis). Otherwise, a horizontal layout is used. Default is FALSE.
plot: Logical; if TRUE, saves the heatmap plot as a PDF. Default is TRUE.
plot.grid: Logical; if TRUE, generates per-pair scatter grid plots for significant associations.
width.grid: Numeric width of the scatter grid output.
height.grid: Numeric height of the scatter grid output.
ncol.grid: Integer number of columns used in scatter grid layout.

Value

If return = TRUE, returns a list with:

A correlation matrix between modules and external features.
A named list with significant features per module (after p-value or adjusted p-value thresholding).

If return = FALSE, the function saves a heatmap of significant correlations to "Results/file_name.pdf".

Details

The function assumes that matA and matB share the same rownames (i.e., samples in the same order). The correlation is computed using WGCNA's cor() and corPvalueStudent() functions. Insignificant correlations (based on p-value or adjusted p-value) are excluded from the visualization.

Examples

data("counts.norm.tuto")
data("network.tuto")

pathways <- compute.pathway.activity(counts.norm.tuto)
#> Warning: 'OmnipathR::get_annotation_resources' is deprecated.
#> Use 'annotation_resources' instead.
#> See help("Deprecated")
#> Warning: 'OmnipathR::import_omnipath_annotations' is deprecated.
#> Use 'annotations' instead.
#> See help("Deprecated")
compute.modules.relationship(network.tuto[[1]],
                             pathways,
                             "Pathways_Progeny-TFs_Modules",
                             width = 15)
#> agg_record_215d4a885b06 
#>                       2 

data("deconv_subgroups.tuto")
corr = compute.modules.relationship(network.tuto[[1]],
                                    deconv_subgroups.tuto[[1]],
                                    "Deconvolution-TFs_Modules",
                                    plot = FALSE,
                                    return = TRUE,
                                    pval = 0.01)