Package index • BioNERO

All functions

PC_correction(): Apply Principal Component (PC)-based correction for confounding artifacts

SFT_fit(): Pick power to fit network to a scale-free topology

ZKfiltering(): Filter outlying samples based on the standardized connectivity (Zk) method

check_SFT(): Check scale-free topology fit for a given network

consensus_SFT_fit(): Pick power to fit networks to scale-free topology

consensus_modules(): Identify consensus modules across independent data sets

consensus_trait_cor(): Correlate set-specific modules and consensus modules to sample information

cor2adj(): Calculate an adjacency matrix from a correlation matrix

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 overrepresentation analysis for a set of genes

exp2cor(): Calculate pairwise correlations between genes in a matrix

exp2gcn(): Infer gene coexpression network from gene expression

exp2gcn_blockwise(): Infer gene coexpression network from gene expression in a blockwise manner

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_HK(): Get housekeeping genes from global expression profile

get_edge_list(): Get edge list from an adjacency matrix for a group of genes

get_hubs_gcn(): Get GCN hubs

get_hubs_grn() 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_WGCNA(): Calculate module preservation between two expression data sets using WGCNA's algorithm

modPres_netrep(): Calculate module preservation between two expression data sets using NetRep'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

plot_PCA(): Plot Principal Component Analysis (PCA) of samples

plot_dendro_and_colors(): Plot dendrogram of genes and 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_gene_significance(): Plot a heatmap of gene significance

plot_grn(): Plot gene regulatory network from edge list

plot_heatmap(): Plot heatmap of hierarchically clustered sample correlations or gene expression

plot_module_trait_cor(): Plot a heatmap of module-trait correlations

plot_ngenes_per_module(): Plot number of genes per module

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

zma.interpro: Maize Interpro annotation

zma.se: Maize gene expression data from Shin et al., 2021.

zma.tfs: Maize transcription factors