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Infer Gene Regulatory Networks from Expression Matrices

Source: R/infer_networks.R
infer_networks.Rd

Infers weighted gene regulatory networks (GRNs) from one or more expression matrices using different inference methods: "GENIE3", "GRNBoost2", "ZILGM", "JRF", or "PCzinb".

Usage

infer_networks(
  count_matrices_list,
  method = c("GENIE3", "GRNBoost2", "ZILGM", "JRF", "PCzinb"),
  adjm = NULL,
  nCores = 1,
  grnboost_modules = NULL,
  genie3_params = list(),
  grnboost2_params = list(),
  zilgm_params = list(),
  jrf_params = list(),
  pczinb_params = list(),
  verbose = FALSE
)

Arguments

count_matrices_list

A MultiAssayExperiment object containing expression data from multiple experiments or conditions.

method

Character string. Inference method to use. One of: "GENIE3", "GRNBoost2", "ZILGM", "JRF", or "PCzinb".

adjm

Optional. Reference adjacency matrix for matching dimensions when using "ZILGM" or "PCzinb".

nCores

Integer. Number of CPU cores to use for parallelization. Default: 1.

grnboost_modules

Python modules required for GRNBoost2 (created via reticulate).

genie3_params

List of parameters for GENIE3 method:

  • regulators: Vector of regulator gene names (default: all)

  • targets: Vector of target gene names (default: all genes)

  • treeMethod: "RF" or "ET" (default: "RF")

  • K: Number of candidate regulators (default: "sqrt")

  • nTrees: Number of trees per ensemble (default: 1000)

  • seed: Random seed for reproducibility (default: NULL)

grnboost2_params

List of parameters for GRNBoost2 method:

  • tf_names: Vector of transcription factor names (default:all)

  • gene_names: Vector of target gene names (default: all)

  • client_or_address: Dask client or address (default: NULL)

  • seed: Random seed for reproducibility (default: NULL)

zilgm_params

List of parameters for ZILGM method:

  • lambda: Regularization parameter (default: 0.1)

  • alpha: Elastic net mixing parameter (default: 1)

  • max_iter: Maximum iterations (default: 100)

  • tol: Convergence tolerance (default: 1e-4)

jrf_params

List of parameters for JRF method:

  • ntree: Number of trees (default: 1000)

  • mtry: Number of variables to sample at each split (default: sqrt(p))

pczinb_params

List of parameters for PCzinb method:

  • gamma: Regularization parameter (default: 0.1)

  • beta: Beta parameter (default: 0.1)

  • max_iter: Maximum iterations (default: 100)

  • tol: Convergence tolerance (default: 1e-4)

verbose

Logical. If TRUE, display messages. Default: FALSE.

Value

A list of inferred networks:

  • For "GENIE3", "GRNBoost2", "ZILGM", and "PCzinb", a list of inferred network objects (edge lists or adjacency matrices).

  • For "JRF", a list of data frames with inferred edge lists for each condition or dataset.

Details

Each expression matrix is preprocessed automatically depending on its object type (Seurat, SingleCellExperiment, or plain matrix).

Parallelization behavior:

  • GENIE3: No external parallelization; internal nCores parameter controls computation.

  • ZILGM: Uses nCores parameter for internal parallelization.

  • GRNBoost2 and PCzinb: Parallelized across matrices using BiocParallel.

  • JRF: Joint modeling of all matrices together using optimized C implementation.

Methods are based on:

  • GENIE3: Random Forest-based inference (Huynh-Thu et al., 2010).

  • GRNBoost2: Gradient boosting trees using arboreto (Moerman et al., 2019).

  • ZILGM: Zero-Inflated Graphical Models for scRNA-seq (Zhang et al., 2021).

  • JRF: Joint Random Forests across multiple conditions (Petralia et al., 2015).

  • PCzinb: Pairwise correlation under ZINB models (Nguyen et al., 2023).

Examples

data("toy_counts")

# Infer networks (toy_counts is already a MultiAssayExperiment)
networks <- infer_networks(
    count_matrices_list = toy_counts,
    method = "GENIE3",
    nCores = 1
)
head(networks[[1]])
#>   regulatoryGene targetGene    weight
#> 1          HLA-B        FTL 0.2047602
#> 2          HLA-A      HLA-B 0.1663412
#> 3          HLA-B      HLA-A 0.1445653
#> 4            FTL       FTH1 0.1415082
#> 5           CD74      CXCR4 0.1405122
#> 6           FTH1        FTL 0.1360361