Introduction to the edgynode package

2023-11-15

Table of Contents

1. Why edgynode?
2. Installation
3. Performing an Example Workflow for edgynode analysis

Why edgynode?

With the maturation of (single-cell) sequencing technologies across modalities, the inference of gene regulatory networks (GRN) is increasingly feasible. Yet, we lack a simple and powerful statistical package to compare GRN inference outcomes.

The edgynode package imports inferred gene regulatory networks and performs network statistics and network simulation procedures to investigate the topology and structure of the GRN at hand.

Installation

# install edgynode from GitHub
devtools::install_github("drostlab/edgynode")

Performing an Example Workflow for edgynode analysis

Small example with internal dataset

# library(edgynode)

# Benchmark GENIE3 inferred networks with raw, no_noise, and quantile_norm combinations
genie3_49_raw <- as.matrix(read.csv(
  system.file("data/network_raw_49_placenta_development.csv",
              package = "edgynode"), row.names = 1))

genie3_49_noNoiseCM_raw <- as.matrix(read.csv(
  system.file("data/network_noNoiseCM_raw_49_placenta_development.csv",
              package = "edgynode"), row.names = 1))

genie3_49_qnorm_no_noise_removed <- as.matrix(read.csv(
  system.file("data/network_qnorm_49_placenta_development.csv",
              package = "edgynode"), row.names = 1))

genie3_49_noNoiseCM_qnorm <- as.matrix(read.csv(
  system.file("data/network_noNoiseCM_qnorm_49_placenta_development.csv",
              package = "edgynode"), row.names = 1))

# Run Benchmark using Hamming distance
benchmark_hamming <- 
  edgynode::network_benchmark_noise_filtering(
    genie3_49_raw,
    genie3_49_noNoiseCM_raw,
    genie3_49_qnorm_no_noise_removed,
    genie3_49_noNoiseCM_qnorm,
    dist_type = "hamming",
    grn_tool = "GENIE3")

# visualize at results
edgynode::plot_network_benchmark_noise_filtering(
  benchmark_hamming,
  dist_type = "hamming", 
  title = "Network Inference Tool: GENIE3")