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Infer gene coexpression network from gene expression in a blockwise manner

Source: R/gcn_inference.R
exp2gcn_blockwise.Rd

Infer gene coexpression network from gene expression in a blockwise manner

Usage

exp2gcn_blockwise(
  exp,
  net_type = "signed",
  module_merging_threshold = 0.8,
  SFTpower = NULL,
  cor_method = "pearson",
  TOM_type = NULL,
  max_block_size = 5000,
  min_module_size = 30,
  ...
)

Arguments

exp

Either a `SummarizedExperiment` object, or a gene expression matrix/data frame with genes in row names and samples in column names.

net_type

Character indicating the type of network to infer. One of 'signed', 'signed hybrid' or 'unsigned'. Default: 'signed'.

module_merging_threshold

Numeric indicating the minimum correlation threshold to merge similar modules into a single one. Default: 0.8.

SFTpower

Numeric scalar indicating the value of the \(\beta\) power to which correlation coefficients will be raised to ensure scale-free topology fit. This value can be obtained with the function SFT_fit().

cor_method

Character with correlation method to use. One of "pearson" or "biweight". Default: "pearson".

TOM_type

Character specifying the method to use to calculate a topological overlap matrix (TOM). If NULL, TOM type will be automatically inferred from network type specified in net_type. Default: NULL.

max_block_size

Numeric indicating the maximum block size for module detection.

min_module_size

Numeric indicating the minimum module size. Default: 30.

...

Additional arguments to WGCNA::blockwiseModules().

Value

A list containing the following elements:

  • MEs Data frame of module eigengenes, with samples in rows, and module eigengenes in columns.

  • genes_and_modules Data frame with columns 'Genes' (character) and 'Modules' (character) indicating the genes and the modules to which they belong.

  • params List with network inference parameters passed as input.

  • dendro_plot_objects List with objects to plot the dendrogram in plot_dendro_and_colors. Elements are named 'tree' (an hclust object with gene dendrogram), 'Unmerged' (character with per-gene module assignments before merging similar modules), and 'Merged' (character with per-gene module assignments after merging similar modules).

Author

Fabricio Almeida-Silva

Examples

data(filt.se)
# The SFT fit was previously calculated and the optimal power was 16
cor <- WGCNA::cor
gcn <- exp2gcn_blockwise(
    exp = filt.se, SFTpower = 18, cor_method = "pearson"
)