# Biological Network Reconstruction Omnibus

## Documentation for package ‘BioNERO’ version 1.4.2

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## Help Pages

check_SFT | Check scale-free topology fit for a given network
consensus_modules | Identify consensus modules across independent data sets
consensus_SFT_fit | Pick power to fit networks to scale-free topology
consensus_trait_cor | Correlate set-specific modules and consensus modules to sample information
cormat_to_edgelist | Transform a correlation matrix to an edge list
detect_communities | Detect communities in a network
dfs2one | Combine multiple expression tables (.tsv) into a single data frame
enrichment_analysis | Perform enrichment analysis for a set of genes
exp2gcn | Reconstruct gene coexpression network from gene expression
exp2grn | Infer gene regulatory network from expression data
exp_genes2orthogroups | Collapse gene-level expression data to orthogroup level
exp_preprocess | Preprocess expression data for network reconstruction
filt.se | Filtered maize gene expression data from Shin et al., 2021.
filter_by_variance | Keep only genes with the highest variances
gene_significance | Calculate gene significance for a given group of genes
get_edge_list | Get edge list from an adjacency matrix for a group of genes
get_HK | Get housekeeping genes from global expression profile
get_hubs_gcn | Get GCN hubs
get_hubs_grn | Get hubs for gene regulatory network
get_hubs_ppi | Get hubs for gene regulatory network
get_neighbors | Get 1st-order neighbors of a given gene or group of genes
grn_average_rank | Rank edge weights for GRNs and calculate average across different methods
grn_combined | Infer gene regulatory network with multiple algorithms and combine results in a list
grn_filter | Filter a gene regulatory network based on optimal scale-free topology fit
grn_infer | Infer gene regulatory network with one of three algorithms
is_singleton | Logical expression to check if gene or gene set is singleton or not
modPres_netrep | Calculate module preservation between two expression data sets using NetRep's algorithm
modPres_WGCNA | Calculate module preservation between two expression data sets using WGCNA's algorithm
module_enrichment | Perform enrichment analysis for coexpression network modules
module_preservation | Calculate network preservation between two expression data sets
module_stability | Perform module stability analysis
module_trait_cor | Correlate module eigengenes to trait
net_stats | Calculate network statistics
og.zma.osa | Orthogroups between maize and rice
osa.se | Rice gene expression data from Shin et al., 2021.
parse_orthofinder | Parse orthogroups identified by OrthoFinder
PC_correction | Apply Principal Component (PC)-based correction for confounding artifacts
plot_dendro_and_colors | Plot dendrogram of genes and modules
plot_dendro_and_cons_colors | Plot dendrogram of genes and consensus modules
plot_eigengene_network | Plot eigengene network
plot_expression_profile | Plot expression profile of given genes across samples
plot_gcn | Plot gene coexpression network from edge list
plot_grn | Plot gene regulatory network from edge list
plot_heatmap | Plot heatmap of hierarchically clustered sample correlations or gene expression
plot_ngenes_per_module | Plot number of genes per module
plot_PCA | Plot Principal Component Analysis (PCA) of samples
plot_ppi | Plot protein-protein interaction network from edge list
q_normalize | Quantile normalize the expression data
remove_nonexp | Remove genes that are not expressed based on a user-defined threshold
replace_na | Remove missing values in a gene expression data frame
SFT_fit | Pick power to fit network to a scale-free topology
ZKfiltering | Filter outlying samples based on the standardized connectivity (Zk) method
zma.interpro | Maize Interpro annotation
zma.se | Maize gene expression data from Shin et al., 2021.
zma.tfs | Maize transcription factors