ModuleConnectivity: ModuleConnectivity
In smorabit/hdWGCNA: hdWGCNA

ModuleConnectivity

R Documentation

ModuleConnectivity

Description

Computes eigengene-based connectivity (kME) based on module eigengenes and the feature expression in selected cell populations / spatial regions.

Usage

ModuleConnectivity(
  seurat_obj,
  group.by = NULL,
  group_name = NULL,
  corFnc = "bicor",
  corOptions = "use='p'",
  harmonized = TRUE,
  assay = NULL,
  slot = "data",
  layer = "data",
  sparse = TRUE,
  reassign_modules = TRUE,
  TOM_use = NULL,
  wgcna_name = NULL,
  ...
)

Arguments

`seurat_obj`	A Seurat object
`group.by`	column in seurat_obj@meta.data containing grouping info, ie clusters or celltypes
`group_name`	name of the group(s) in group.by to use for kME calculation
`corFnc`	character string specifying the function to be used to calculate co-expression similarity. Defaults to bicor. Any function returning values
`corOptions`	character string specifying additional arguments to be passed to the function given by corFnc. Use "use = 'p', method = 'spearman'" to obtain Spearman correlation. Use "use = 'p'" to obtain Pearson correlation.
`harmonized`	logical determining whether to use harmonized MEs for kME calculation
`assay`	Assay in seurat_obj containing expression information.
`slot`	Slot in seurat_obj, default to normalized 'data' slot.
`sparse`	logical indicating whether or not to run the correlation using a sparse matrix.
`reassign_modules`	logical indicating whether or not to reassign genes to different co-expression modules if their kME value in the assigned module is negative.
`TOM_use`	name of the hdWGCNA experiment that contains the TOM which will be used to compute the intramodular degree of each gene. If the TOM is not found, this calculation will be skipped.
`wgcna_name`	The name of the hdWGCNA experiment in the seurat_obj@misc slot

Details

ModuleConnectivity computes the eigengene-based connectivity (kME) of each feature and each module. This is done by correlating the gene expression signal with the module eigengenes for each module. Features with high kME values have greater network connectivity within that module, and we can identify module hub genes by taking the top genes for each module by kME values.

We recommend using the group.by and group_name parameters that were previously used in the SetDatExpr function, so only the relevant cell populations / spatial regions are considered for computing kMEs.

Depending on the parameters for network analysis, sometimes a feature has a negative kME in the module that it was originally assigned. This discrepancy arises since the network construction is done on the metacell/metaspot matrix but the kME calculation is done on the cell/spot matrix. By default, we account for this by reassigning features with negative kMEs in their assigned module to the module that had the highest kME for that feature.