ConstructNetwork: ConstructNetwork

In smorabit/scWGCNA: hdWGCNA

ConstructNetwork

Description

Constructs a co-expression network and groups genes into modules given a Seurat object that has been prepared for network analysis.

Usage

ConstructNetwork(
  seurat_obj,
  soft_power = NULL,
  min_power = 3,
  tom_outdir = "TOM",
  tom_name = NULL,
  consensus = FALSE,
  overwrite_tom = FALSE,
  wgcna_name = NULL,
  blocks = NULL,
  maxBlockSize = 30000,
  randomSeed = 12345,
  corType = "pearson",
  consensusQuantile = 0.3,
  networkType = "signed",
  TOMType = "signed",
  TOMDenom = "min",
  scaleTOMs = TRUE,
  scaleQuantile = 0.8,
  sampleForScaling = TRUE,
  sampleForScalingFactor = 1000,
  useDiskCache = TRUE,
  chunkSize = NULL,
  deepSplit = 4,
  pamStage = FALSE,
  detectCutHeight = 0.995,
  minModuleSize = 50,
  mergeCutHeight = 0.2,
  saveConsensusTOMs = TRUE,
  ...
)

Arguments

seurat_obj	A Seurat object
soft_power	the soft power used for network construction. Automatically selected by default.
min_power	the smallest soft power to be selected if soft_power=NULL
tom_outdir	path to the directory where the TOM will be written
tom_name	prefix name given to the TOM output file
consensus	flag indicating whether or not to perform Consensus network analysis
wgcna_name	name of the WGCNA experiment
...	additional parameters passed to blockwiseConsensusModules

Details

ConstructNetwork builds a co-expression network and identifies clusters of highly co-expressed genes (modules) from the metacell or metaspot expression matrix stored in the Seurat object. Before running this function, the following functions must be run on the input Seurat object:

1. SetupForWGCNA

2. MetacellsByGroups or MetaspotsByGroups, and NormalizeMetacells

3. SetDatExpr or SetMultiExpr

4. TestSoftPowers or TestSoftPowersConsensus

This function can also be used to perform consensus network analysis if consensus=TRUE is selected. ConstructNetwork calls the WGCNA function blockwiseConsensusModules to compute the adjacency matrix, topological overlap matrix, and to run the Dynamic Tree Cut algorithm to identify gene modules. blockwiseConsensusModules has numerous parameters but here we have selected default parameters that we have found to provide reasonable results on a variety of single-cell and spatial transcriptomic datasets.

Value

seurat_obj with the co-expression network and gene modules computed for the selected wgcna experiment

smorabit/scWGCNA documentation built on April 4, 2024, 10:32 a.m.