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Infer gene coexpression network from gene expression

Source: R/gcn_inference.R
exp2gcn.Rd

Infer gene coexpression network from gene expression

Usage

exp2gcn(
  exp,
  net_type = "signed",
  module_merging_threshold = 0.8,
  SFTpower = NULL,
  cor_method = "spearman",
  TOM_type = NULL,
  min_module_size = 30,
  return_cormat = TRUE,
  verbose = FALSE
)

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", "biweight" or "spearman". Default: "spearman".

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.

min_module_size

Numeric indicating the minimum module size. Default: 30.

return_cormat

Logical indicating whether the correlation matrix should be returned. If TRUE (default), an element named `correlation_matrix` containing the correlation matrix will be included in the result list.

verbose

Logical indicating whether to display progress messages or not. Default: FALSE.

Value

A list containing the following elements:

  • adjacency_matrix Numeric matrix with network adjacencies.

  • 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.

  • kIN Data frame of degree centrality for each gene, with columns 'kTotal' (total degree), 'kWithin' (intramodular degree), 'kOut' (extra-modular degree), and 'kDiff' (difference between the intra- and extra-modular degree).

  • correlation_matrix Numeric matrix with pairwise correlation coefficients between genes. If parameter return_cormat is FALSE, this will be NULL.

  • 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
gcn <- exp2gcn(exp = filt.se, SFTpower = 18, cor_method = "pearson")
#> ..connectivity..
#> ..matrix multiplication (system BLAS)..
#> ..normalization..
#> ..done.