Package index • HDAnalyzeR

Preprocessing Data

Preprocessing

Functions that help you prepare your data for analysis.

clean_data(): Preprocess data

clean_metadata(): Preprocess metadata

Data Normalization and Imputation

Functions that normalize data, remove batch effects, and impute missing values.

normalize_data(): Normalize data and remove batch effects

impute_median(): Impute via Median

impute_knn(): Impute via k-nearest neighbors

impute_missForest(): Impute via missForest

impute_mice(): Impute via MICE

Data Quality Control

Quality Control

Functions that perform quality control check on your data.

na_search(): Summary of missing values

qc_summary_data(): Summarize the quality control results of Olink data

qc_summary_metadata(): Summarize the quality control results of metadata

Correlation and Clustering

Functions that perform protein-protein correlation and can order data based on their hierarchical clustering.

create_corr_heatmap(): Plot correlation heatmap

cluster_data(): Cluster data

Dimensionality Reduction

Functions that perform dimensionality reduction on your data.

do_pca(): Run PCA analysis

do_umap(): Run UMAP analysis

Main Proteomics Analysis

Differential Expression Analysis

Functions that perform protein differential expression analysis.

do_limma(): Run differential expression analysis with limma

do_limma_continuous(): Run differential expression analysis with limma for continuous variable

do_ttest(): Run differential expression analysis with t-test

Classification Models

Functions that run Machine Learning classification model pipelines.

do_rreg(): Regularized classification model pipeline

do_rreg_multi(): Regularized multiclassification model pipeline

do_rf(): Random forest classification model pipeline

do_rf_multi(): Random forest multiclassification model pipeline

do_xgboost(): XGBoost classification model pipeline

do_xgboost_multi(): XGBoost multiclassification model pipeline

do_lreg(): Fit logistic regression model for single predictors

Visualize Results

Functions that visualize the results of your analysis.

plot_de_summary(): Plot summary visualizations for the differential expression results

plot_features_summary(): Plot features summary visualizations

plot_biomarkers_summary_heatmap(): Plot a summary heatmap of the combined differential expression and classification models results

plot_protein_boxplot(): Plot protein boxplots

plot_scatter_with_regression(): Plot a scatter plot with regression line

Post Analysis

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

literature_search(): Automated PubMed literature search

do_ora(): Perform over-representation analysis

plot_ora(): Plot the results of the over-representation analysis

do_gsea(): Perform gene set enrichment analysis

plot_gsea(): Plot the results of the gene set enrichment analysis

Generic Utilities

Functions that perform general tasks.

create_dir(): Create directory

save_df(): Save tibble as CSV, TSV, Excel or RDA file

import_df(): Import dataframe from file

widen_data(): Widen Olink data

Palettes and Themes

Functions that customize the appearance of your plots.

get_hpa_palettes(): HPA color palettes

scale_color_hpa(): HPA color scales

scale_fill_hpa(): HPA fill scales

theme_hpa(): HPA theme

Built in datasets

example_data: Cancer cohort Olink data

example_metadata: Cancer cohort metadata