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 |