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ConstructNetwork

Source: R/ConstructNetwork.R
ConstructNetwork.Rd

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,
  calibrationQuantile = 0.8,
  sampleForCalibration = TRUE,
  sampleForCalibrationFactor = 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

Value

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

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.