Package index • HDAnalyzeR

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Generic Utilities

Functions that help you perform general tasks.

hd_initialize(): Initialize HDAnalyzeR object
hd_save_path(): Create directory to save results
hd_save_data(): Save tibble or R object
hd_import_data(): Import data from file
hd_widen_data(): Convert omics data to wide format
hd_long_data(): Convert omics data to long format
hd_bin_columns(): Bin variables
hd_detect_vartype(): Detect variable type
hd_filter(): Filter an HD object based on a variable
hd_log_transform(): Log transform data with base 2

Data Preprocessing & Quality Control

Data Normalization and Imputation

Functions that perform normalizaztion, remove batch effects and impute missing values.

hd_normalize(): Normalize data and remove batch effects
hd_omit_na(): Omit missing values
hd_impute_median(): Impute via Median
hd_impute_knn(): Impute via k-nearest neighbors
hd_impute_missForest(): Impute via missForest

Quality Control

Functions that perform automated data quality control check.

hd_na_search(): Heatmap summary of missing values
hd_qc_summary(): Summarize quality control information

Correlation and Clustering

Functions that perform protein-protein correlation and can order data based on selected clustering method.

hd_correlate(): Correlate data
hd_plot_cor_heatmap(): Plot correlation heatmap
hd_cluster(): Cluster data
hd_cluster_samples(): Cluster samples in k clusters
hd_assess_clusters(): Assess clusters

Dimensionality Reduction

Functions that perform dimensionality reduction.

hd_pca(): Run PCA analysis
hd_umap(): Run UMAP analysis
hd_plot_dim(): Plot PCA or UMAP results on a 2D plane
hd_plot_pca_loadings(): Plot PCA loadings
hd_plot_pca_variance(): Plot PCA variance
hd_auto_pca(): Run PCA analysis and plot the results
hd_auto_umap(): Run UMAP analysis and plot the results

Main Proteomics Analysis

Protein Co-expression Network Analysis (WGCNA)

Functions that perform weighted gene co-expression network analysis.

hd_wgcna(): Weighted gene co-expression network analysis
hd_plot_wgcna(): Plot WGCNA results

Differential Expression Analysis

Functions that perform differential expression analysis.

hd_de_limma(): Differential expression analysis with limma
hd_de_ttest(): Differential expression analysis with t-test
hd_plot_volcano(): Visualize differential expression results

Classification Models

Functions that run machine learning classification model pipelines.

hd_split_data(): Split data
hd_model_lr(): Logistic regression model pipeline
hd_model_rreg(): Regularized regression model pipeline
hd_model_rf(): Random forest model pipeline
hd_model_test(): Validate model on new data

Summary Visualizations

Functions that visually summarize the results.

hd_plot_de_summary(): Summarize differential expression results
hd_plot_model_summary(): Summarize model features
hd_plot_feature_heatmap(): Summary the combined DE and model results

Other Visualizations

Functions that visualize data.

hd_plot_regression(): Regression plot
hd_plot_feature_boxplot(): Feature boxplots
hd_plot_feature_network(): Feature network

Post Analysis

Functions that analyze further the potential biormarkers and their biological impact.

hd_ora(): Over-representation analysis
hd_plot_ora(): Plot over-representation analysis results
hd_show_backgrounds(): Show available background lists
hd_gsea(): Gene set enrichment analysis
hd_plot_gsea(): Plot gene set enrichment analysis results
hd_literature_search(): Fetch PubMed articles for disease-protein biomarker associations

Palettes and Themes

Functions that customize the appearance of your plots.

hd_show_palettes(): Display available palettes
hd_palettes(): HDAnalyzeR palettes
scale_color_hd(): HDAnalyzeR color scales
scale_fill_hd(): HDAnalyzeR fill scales
theme_hd(): HDAnalyzeR theme

Built in datasets

example_data: Cancer cohort Olink data
example_metadata: Cancer cohort metadata