HPAanalyze

In Bioconductor: platform build rank dependencies in
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Background: The Human Protein Atlas program aims to map human proteins via multiple technologies including imaging, proteomics and transcriptomics.
Results: HPAanalyze is an R package for retreiving and performing exploratory data analysis from HPA. It provides functionality for importing data tables and xml files from HPA, exporting and visualizing data, as well as download all staining images of interest. The package is free, open source, and available via Github.
Conclusions: HPAanalyze intergrates into the R workflow via the tidyverse philosophy and data structures, and can be used in combination with Bioconductor packages for easy analysis of HPA data.
Citation: Tran, A.N., Dussaq, A.M., Kennell, T. et al. HPAanalyze: an R package that facilitates the retrieval and analysis of the Human Protein Atlas data. BMC Bioinformatics 20, 463 (2019) https://doi.org/10.1186/s12859-019-3059-z

Background

The Human Protein Atlas (HPA) is a comprehensive resource for exploration of human proteome which contains a vast amount of proteomics and transcriptomics data generated from antibody-based tissue micro-array profiling and RNA deep-sequencing.

The program has generated protein expression profiles in human normal tissues with cell type-specific expression patterns, cancer and cell lines via an innovative immunohistochemistry-based approach. These profiles are accompanied by a large collection of high quality histological staining images, annotated with clinical data and quantification. The database also includes classification of protein into both functional classes (such as transcription factors or kinases) and project-related classes (such as candidate genes for cancer). Starting from version 4.0, the HPA includes subcellular location profiles generated based on confocal images of immunofluorescent stained cells. Together, these data provide a detailed picture of protein expression in human cells and tissues, facilitating tissue-based diagnostis and research.

Data from the HPA are freely available via proteinatlas.org, allowing scientists to access and incorporate the data into their research. Here, we introduce HPAanalyze, an R package aims to simplify exploratory data analysis from those data.

Overview

HPAanalyze is designed to fullfill 3 main tasks: (1) Import, subsetting and export downloadable datasets; (2) Visualization of downloadable datasets for exploratory analysis; and (3) Working with the individual XML files. This package aims to serve researchers with little programming experience, but also allow power users to use the imported data as desired.

Obtaining HPAanalyze

The stable version of HPAanalyze should be downloaded from Bioconductor:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("HPAanalyze")

The development version of HPAanalyze is available on Github can be installed with:

devtools::install_github("anhtr/HPAanalyze")

Full dataset import, subsetting and export

The hpaDownload() function downloads full datasets from HPA and imports them into R as a list of tibbles, the standard object of tidyverse, which can subsequently be subset with hpaSubset() and export into .xmlx files with hpaExport(). The standard object allow the imported data to be further processed in a traditional R workflow. The ability to quickly subset and export data gives researchers the option to use other non-R downstream tools, such as GraphPad for creating publication-quality graphics, or share a subset of data containing only proteins of interest.

Visualization

The hpaVis function family take the output of hpaDownload() (or hpaSubset()) provides quick visualization of the data, with the intention of aiding exploratory analysis. Nevertheless, the standard ggplot object output of these functions give users the option to further customize the plots for publication. All hpaVis functions share the same syntax for arguments: subsetting, specifying colors and opting to use custom themes.

The first release of the HPAanalyze package includes three functions: hpaVisTissue() for the normal tissue, hpaVisPatho() for the pathology/cancer, and hpaVisSubcell() for the subcellular location datasets.

Individual xml import and image downloading

The hpaXml function family import and extract data from individual XML entries from HPA. The hpaXmlGet() function downloads and imports data as “xml_document”/“xml_node” object, which can subsequently be processed by other hpaXml functions. The XML format from HPA contains a wealth of information that may not be covered by this package. However, users can extract any data of interest from the imported XML file using the xml2 package.

In the first release, HPAanalyze includes four functions for data extraction from HPA XML files: hpaXmlProtClass() for protein class information, hpaTissueExprSum() for summary of protein expression in tissue, hpaXmlAntibody() for a list of antibody used to stain for the protein of interest, and hpaTissueExpr() for a detailed data from each sample including clinical data and IHC scoring.

hpaTissueExprSum and hpaTissueExpr provide download links to download relevant staining images, with the former function also gives the options to automate the downloading process.

Availability and requirements

Project name: HPAanalyze
Project home page: https://github.com/anhtr/HPAanalyze
Operating system(s): All platforms where R is available, including Windows, Linux, OS X
Other requirements: R 3.5.0 or higher, and the R packages dplyr, openxlsx, ggplot2, tibble, xml2, stats, utils, gridExtra
License: GPL-3

Acknowledgements

We appreciate the support of the National institutes of Health National Cancer Institute R01 CA151522 and funds from the Department of Cell, Developmental and Integrative Biology at the University of Alabama at Birmingham.

Copyright

Anh Tran, 2018-2025

Please cite: Tran, A.N., Dussaq, A.M., Kennell, T. et al. HPAanalyze: an R package that facilitates the retrieval and analysis of the Human Protein Atlas data. BMC Bioinformatics 20, 463 (2019) https://doi.org/10.1186/s12859-019-3059-z