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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_by_sex()
Filter data and metadata by sex
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_literature_search()
PubMed literature search
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

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