DE_between_group        DE between groups
DE_between_time         DE between time points
WGCNA_module            Convert WGCNA output to feature-module data
                        frame
calc_feature_property   Calculate feature property
calc_mean_sd            Calculate mean and SD
create_input            Create object
decomp_variance         Variance decomposition
enrichGO_list           GO enrichment with gene sets
enrichGO_rank           GO enrichment with ranked gene list
enrichR_list            Gene-set enrichment via enrichR
enrich_msigdb           Gene set enrichment via MSigDB
example_go              'enrichGO_list()' output object for runnable
                        examples
example_net             'run_WGCNA()' output object for runnable
                        examples
example_obj             SummarizedExperiment object for runnable
                        examples
example_res_list        'run_Trendy' output object for runnable
                        examples
extract_hubs            Extract hub features from WGCNA modules
extract_segment_trends
                        Extract feature trends
flatten_DE              Flatten nested differential expression results
flatten_enrich          Flatten nested enrichment results
get_custom_palette      Get custom color palette
group_specific_features
                        Group specific features
impute_groups           Impute missing values
merge_groups            Merge groups into one object
merge_replicates        Merge replicates
normalise_to_start      Normalise to starting time point
plot_DE_between_group   DE number between groups
plot_DE_between_time    DE number between time points
plot_GO                 Plot GO enrichment
plot_ID                 Plot number of identified features
plot_WGCNA              Plot WGCNA results
plot_breakpoints        Plot breakpoint distribution
plot_cor_matrix         Plot correlation matrix
plot_cv                 Plot coefficient of variation (CV)
plot_distribution       Abundance distribution plot
plot_missing            Plot missing rate
plot_modules_h          Plot modules (horizontal layout)
plot_modules_v          Plot modules (vertical layout)
plot_pca                Plot PCA
plot_pca_3D             Plot PCA in 3D
plot_pca_arrows         Plot PCA with arrows
plot_pca_by_group       Plot PCA by group
plot_segments           Plot segmented regression
plot_trend              Plot feature abundance over time
plot_umap               Plot UMAP
plot_umap_by_group      Plot UMAP by group
plot_variance           Plot variance decomposition
plot_volcano            Volcano plot of DE results
prepare_WGCNA           Prepare data and choose power for WGCNA
prepare_tide            Prepare TiDEomics input
run_Trendy              Segmented regression analysis
run_WGCNA               Weighted gene co-expression network analysis
set_custom_palette      Set custom color palette
split_groups            Split groups
summarise_Trendy        Summarise Trendy results
summarise_feature_property
                        Summarise feature properties
summarise_module_metrics
                        Summarise WGCNA module metrics
summarise_module_pattern
                        Summarise module patterns
theme_custom            Custom ggplot2 theme
tutorial_data           Dataset for TiDEomics tutorial, expression
                        matrix
tutorial_sample_info    Dataset for TiDEomics tutorial, sample
                        information
