_pkgdown.yml

url: https://motrpac.github.io/MotrpacRatTraining6mo/
template:
  bootstrap: 5

home:
  links:
    - text: Publications
      href: https://www.nature.com/collections/cfiiibcebh


reference:

- title: Load data  
  desc: Load data and analysis results used in the publication
  contents:
    - list_available_data

- subtitle: Load sample-level molecular data  
  contents:
    - load_sample_data
    - combine_normalized_data
    - plot_feature_normalized_data
    
- subtitle: Load feature-level annotation
  contents:
    - load_feature_annotation
    - load_atac_feature_annotation
    - load_methyl_feature_annotation

- subtitle: Load differential analysis results 
  contents: 
    - combine_da_results
    - load_training_da
    - load_metab_da
    - plot_feature_logfc

- subtitle: Load epigenetic data
  desc: Load full epigenetic datasets from Google Cloud Storage
  contents: 
    - load_epigen_da
    - load_methyl_raw_data

- subtitle: Load data from GCS
  desc: Download and read in RData or text files from Google Cloud Storage
  contents:
    - get_file_from_url
    - get_rdata_from_url



- title: Perform differential analysis 

- subtitle: Wrapper functions 
  desc: Main functions to perform differential analysis for each data type 
  contents:
    - atac_timewise_da
    - atac_training_da
    - immuno_timewise_da
    - immuno_training_da
    - metab_timewise_da
    - metab_training_da
    - metab_meta_regression
    - proteomics_timewise_da
    - proteomics_training_da
    - rrbs_differential_analysis
    - transcript_timewise_da
    - transcript_training_da

- subtitle: Ancillary functions
  desc: Ancillary functions used within or after the above wrapper functions
  contents:
    - atac_prep_data
    - analyze_tile
    - fix_covariates
    - merge_sites_by_clusters
    - run_deseq
    - transcript_prep_data
    - merge_two_dea_dfs
    - metabolite_meta_regression
    - forest_plot
    
- subtitle: Metabolomics meta-analysis
  desc: >
    Functions used to perform metabolomics meta-analysis, 
    which was abandoned in favor of meta-regression.  
  contents:
    - metab_meta_analysis
    


- title: Graphical clustering 
  desc: >
    Perform and explore the Bayesian graphical clustering 
    analysis effectively used to transforms continuous 
    z-scores (normalized effect sizes) into discrete states 
    to summarize trajectories of differential features in a graph. 

- subtitle: Perform Bayesian graphical clustering  
  contents:
    - bayesian_graphical_clustering
    - repfdr_wrapper

- subtitle: Extract graphical clusters  
  contents:
    - extract_top_trajectories 
    - extract_main_clusters
    - get_all_trajectories
    - extract_tissue_sets
    - filter_edge_sets_by_trajectories
    - get_trajectory_sizes_from_edge_sets
    - limit_sets_by_regex

- subtitle: Plot graphical clusters 
  contents: 
    - get_tree_plot_for_tissue
    - plot_features_per_cluster
    - plot_feature_trajectories



- title: Pathway enrichment   

- subtitle: Perform pathway enrichment analysis  
  contents:
    - cluster_pathway_enrichment
    - custom_cluster_pathway_enrichment
    - gene_pathway_enrichment
    - run_fella
    - make_kegg_db 

- subtitle: Visualize pathway enrichment results 
  contents: 
    - enrichment_network_vis
    - check_cluster_res_format
    
- subtitle: Perform ssGSEA2 and PTM-SEA
  contents: 
    - ssGSEA2_wrapper
    - prepare_gsea_input
    - prepare_ptmsea_input
    - load_uniprot_human_fasta
    - find_flanks



- title: Manipulate data 

- subtitle: Normalize data 
  contents:
    - atac_normalize_counts
    - transcript_normalize_counts

- subtitle: Call sample outliers 
  desc: Call sample outliers in principal component space 
  contents:
    - call_pca_outliers
    - plot_pcs
    - matches("call_outliers")

- subtitle: Miscellaneous  
  contents:
    - get_peak_annotations
    - filter_outliers
    - viallabel_to_pid
    - df_to_numeric



- title: Built-in data objects 
  contents:
    - STOPWORDS