Package index • MotrpacRatTraining6mo

Load data

Load data and analysis results used in the publication

list_available_data(): List available data

Load sample-level molecular data

load_sample_data(): Load sample-level data

combine_normalized_data(): Combine normalized sample-level data

plot_feature_normalized_data(): Plot sample-level data for a feature

Load feature-level annotation

load_feature_annotation(): Load feature annotation

load_atac_feature_annotation(): Load ATAC feature annotation

load_methyl_feature_annotation(): Load METHYL feature annotation

Load differential analysis results

combine_da_results(): Combine differential analysis results

load_training_da(): Load training differential analysis results

load_metab_da(): Load metabolomics differential analysis

plot_feature_logfc(): Plot differential analysis results for a feature

Load epigenetic data

Load full epigenetic datasets from Google Cloud Storage

load_epigen_da(): Load epigenetic differential analysis results

load_methyl_raw_data(): Load raw METHYL data

Load data from GCS

Download and read in RData or text files from Google Cloud Storage

get_file_from_url(): Load file from GCS

get_rdata_from_url(): Load RData from GCS

Perform differential analysis

Wrapper functions

Main functions to perform differential analysis for each data type

atac_timewise_da(): ATAC-seq timewise differential analysis

atac_training_da(): ATAC-seq training differential analysis

immuno_timewise_da(): Immunoassay timewise differential analysis

immuno_training_da(): Immunoassay training differential analysis

metab_timewise_da(): Metabolomics timewise differential analysis

metab_training_da(): Metabolomics training differential analysis

metab_meta_regression(): Metabolomics meta-regression

proteomics_timewise_da(): Proteomics timewise differential analysis

proteomics_training_da(): Proteomics training differential analysis

rrbs_differential_analysis(): RRBS differential anlaysis

transcript_timewise_da(): RNA-seq timewise differential analysis

transcript_training_da(): RNA-seq training differential analysis

Ancillary functions

Ancillary functions used within or after the above wrapper functions

atac_prep_data(): Prepare ATAC-seq dataset

analyze_tile(): Analyze genome tiles

fix_covariates(): Format covariates for differential analysis

merge_sites_by_clusters(): Merge sites by cluster

run_deseq(): Wrapper for DESeq2::DESeq()

transcript_prep_data(): Preprocess RNA-seq data

merge_two_dea_dfs(): Concatenate data frames

metabolite_meta_regression(): Meta-regression for a metabolite

forest_plot(): Print forest plot

Metabolomics meta-analysis

Functions used to perform metabolomics meta-analysis, which was abandoned in favor of meta-regression.

metab_meta_analysis(): Metabolomics timewise meta-analysis

Graphical clustering

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.

Perform Bayesian graphical clustering

bayesian_graphical_clustering(): Bayesian graphical clustering

repfdr_wrapper(): repfdr wrapper

Extract graphical clusters

extract_top_trajectories(): Extract top trajectories

extract_main_clusters(): Extract main graphical clusters

get_all_trajectories(): Remove all non-empty trajectories

extract_tissue_sets(): An auxiliary function for getting the top analyte sets for a set of tissues.

filter_edge_sets_by_trajectories(): Filter edge sets to largest trajectories

get_trajectory_sizes_from_edge_sets(): Get trajectory sizes

limit_sets_by_regex(): Reduce a list of sets by a regex

Plot graphical clusters

get_tree_plot_for_tissue(): Graph representation of feature trajectories

plot_features_per_cluster(): Plot feature composition of clusters

plot_feature_trajectories(): Plot feature trajectories

Pathway enrichment

Perform pathway enrichment analysis

cluster_pathway_enrichment(): Pathway enrichment for graphical clusters

custom_cluster_pathway_enrichment(): Custom pathway enrichment for graphical clusters

gene_pathway_enrichment(): Gene pathway enrichment

run_fella(): Metabolomics pathway enrichment

make_kegg_db(): Make KEGG database

Visualize pathway enrichment results

enrichment_network_vis(): Pathway enrichment network

check_cluster_res_format(): Check clustering results format

Perform ssGSEA2 and PTM-SEA

ssGSEA2_wrapper(): Run ssGSEA2 or PTM-SEA

prepare_gsea_input(): Prepare GCT file for GSEA

prepare_ptmsea_input(): Prepare PTM-SEA input

load_uniprot_human_fasta(): Load UniProt human canonical protein FASTA file

find_flanks(): Find human flanks

Manipulate data

Normalize data

atac_normalize_counts(): Filter and normalize raw ATAC-seq counts

transcript_normalize_counts(): Filter and normalize raw RNA-seq counts

Call sample outliers

Call sample outliers in principal component space

call_pca_outliers(): Call PCA outliers

plot_pcs(): Plot 2D scatter plot of principal components

atac_call_outliers(): Call ATAC-seq sample outliers

transcript_call_outliers(): Call RNA-seq sample outliers

Miscellaneous

get_peak_annotations(): Get genomic peak annotations

filter_outliers(): Filter outliers

viallabel_to_pid(): Map vial labels to PIDs

df_to_numeric(): Make data frame numeric only

Built-in data objects

STOPWORDS: Stop words