Package index • TiDEomics

Data preparation

create_input(): Create object
normalise_to_start(): Normalise to time 0
split_groups(): Split groups
merge_groups(): Merge groups into one object
merge_replicates(): Merge replicates
impute_groups(): Impute missing values

Quality control

plot_distribution(): Abundance distribution plot
plot_missing(): Plot missing rate
plot_ID(): Plot number of identified features
plot_cv(): Plot coefficient of variation (CV)

Feature properties

calc_mean_sd(): Calculate mean and SD
plot_trend(): Plot feature abundance over time
calc_feature_property(): Calculate feature property
summarise_feature_property(): Summarise feature properties
group_specific_features(): Group specific features
decomp_variance(): Variance decomposition
plot_variance(): Plot variance decomposition

Sample relationship

plot_cor_matrix(): Plot correlation matrix
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 (one object)
plot_pca_by_group_list(): Plot PCA by group (list of objects)
plot_umap(): Plot UMAP
plot_umap_by_group(): Plot UMAP by group (one object)
plot_umap_by_group_list(): Plot UMAP by group (list of objects)

Pairwise differential expression

DE_between_group(): DE between groups
DE_between_time(): DE between time points
plot_DE_between_time(): DE number between time points
plot_volcano(): Volcano plot of DE results

Segmented regression with Trendy

run_Trendy(): Segmented regression analysis
summarise_Trendy(): Summarise Trendy results
extract_segment_trends(): Extract feature trends
plot_segments(): Plot segmented regression
plot_breakpoints(): Plot breakpoint distribution

Temporal module identification with WGCNA

prepare_WGCNA(): Prepare data and choose power for WGCNA
run_WGCNA(): Weighted gene co-expression network analysis
plot_WGCNA(): Plot WGCNA results
plot_modules_h(): Plot modules (horizontal layout)
plot_modules_v(): Plot modules (vertical layout)
summarise_module_pattern(): Summarise module patterns

Functional enrichment

enrichGO_rank(): GO enrichment with ranked gene list
enrichGO_list(): GO enrichment with gene sets
enrich_drug_list(): Enrich for drug targets
plot_GO(): Plot GO enrichment

Visualization settings

set_custom_palette(): Set custom color palette
get_custom_palette(): Get custom color palette

Data sets in tutorial and examples

tutorial_data: Dataset for TiDEomics tutorial, expression matrix
tutorial_sample_info: Dataset for TiDEomics tutorial, sample information
example: SummarizedExperiment object for runnable examples
example_res_list: run_Trendy output object for runnable examples
example_net: run_WGCNA() output object for runnable examples