library(BiocStyle)
library(HPAanalyze)
library(tibble)
library(dplyr)
library(ggplot2)

1 Summary

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.

Keywords: Human Protein Atlas, Proteomics, Homo Sapiens, Visualization, Software

2 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 diagnostic and research.

Data from the HPA are freely available via proteinatlas.org, allowing scientists to access and incorporate the data into their research. Previously, the R package hpar has been created for fast and easy programmatic access of HPA data. Here, we introduce HPAanalyze, an R package aims to simplify exploratory data analysis from those data, as well as provide other complementary functionality to hpar.

2.1 The different HPA data formats

The Human Protein Atlas project provides data via two main mechanisms: Full datasets in the form of downloadable compressed tab-separated files (.tsv) and individual entries in XML, RDF and TSV formats. The full downloadable datasets includes normal tissue, pathology (cancer), subcellular location and RNA expression data. For individual entries, the XML format is the most comprehensive, providing information on the target protein, antibodies, summary for each tissue and detailed data from each sample including clinical data, IHC scoring and image download links.

2.2 `HPAanalyze` 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.

Figure 1: HPAanalyze workflow

2.2.1 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")

Please cite: Tran AN, Dussaq AM, Kennell T, Willey C, Hjelmeland A. HPAanalyze: An R Package that Facilitates the Retrieval and Analysis of The Human Protein Atlas Data. bioRxiv 355032; doi: https://doi.org/10.1101/355032

3 Full dataset import, subsetting and export

The hpaDownload() function downloads full datasets from HPA (specifically, the .tsv format described above) 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 .xlsx 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.

You can skip this whole section if you only care about visualization, unless you need a specific version of the HPA datasets, or the RNA expression datasets.

3.1 Download and import data with `hpaDownload()`

This function should be the first thing you use. It give you a list of data frames containing the datasets you specified, which you can then feed into other functions in this package.

# This should give you the latest of everything, but unless you have a lot of RAM and processing power, I would not recomend it.
downloadedData <- hpaDownload(downloadList='all')
summary(downloadedData)

#>                          Length Class  Mode
#> normal_tissue             6     tbl_df list
#> pathology                11     tbl_df list
#> subcellular_location     14     tbl_df list
#> ...

3.1.1 The “histology” datasets

Most of the time, you will only need the “histology” datasets, which contain normal_tissue, pathology (basically cancers) and subcellular_location.

downloadedData <- hpaDownload(downloadList='histology', version='example')
# version = "example" will load the built-in dataset. That's sufficient for normal usage, and save you some time.

The normal_tissue dataset contains information about protein expression profiles in human tissues based on IHC staining. The datasets contain six columns: ensembl (Ensembl gene identifier); gene (HGNC symbol), tissue (tissue name); cell_type (annotated cell type); level (expression value); reliability (the gene reliability of the expression value).

tibble::glimpse(downloadedData$normal_tissue, give.attr=FALSE)
#> Rows: 1,193,218
#> Columns: 6
#> $ ensembl     <chr> "ENSG00000000003", "ENSG00000000003", "ENSG00000000003", "…
#> $ gene        <chr> "TSPAN6", "TSPAN6", "TSPAN6", "TSPAN6", "TSPAN6", "TSPAN6"…
#> $ tissue      <chr> "adipose tissue", "adrenal gland", "appendix", "appendix",…
#> $ cell_type   <chr> "adipocytes", "glandular cells", "glandular cells", "lymph…
#> $ level       <chr> "Not detected", "Not detected", "Medium", "Not detected", …
#> $ reliability <chr> "Approved", "Approved", "Approved", "Approved", "Approved"…

The pathology dataset contains information about protein expression profiles in human tumor tissue based on IHC staining. The datasets contain eleven columns: ensembl (Ensembl gene identifier); gene (HGNC symbol); cancer (cancer type); high, medium, low, not_detected (number of patients annotated for different staining levels); prognostic_favorable, unprognostic_favorable, prognostic_unfavorable, unprognostic_unfavorable (log-rank p values for patient survival and mRNA correlation).

tibble::glimpse(downloadedData$pathology, give.attr=FALSE)
#> Rows: 401,800
#> Columns: 11
#> $ ensembl                  <chr> "ENSG00000000003", "ENSG00000000003", "ENSG00…
#> $ gene                     <chr> "TSPAN6", "TSPAN6", "TSPAN6", "TSPAN6", "TSPA…
#> $ cancer                   <chr> "breast cancer", "carcinoid", "cervical cance…
#> $ high                     <int> 1, 0, 11, 0, 10, 0, 0, 4, 8, 0, 0, 8, 11, 5, …
#> $ medium                   <int> 7, 1, 1, 6, 2, 0, 3, 5, 4, 0, 1, 3, 1, 6, 4, …
#> $ low                      <int> 2, 1, 0, 2, 0, 0, 1, 1, 0, 0, 2, 0, 0, 0, 1, …
#> $ not_detected             <int> 2, 2, 0, 2, 0, 11, 0, 0, 0, 11, 9, 0, 0, 0, 5…
#> $ prognostic_favorable     <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ unprognostic_favorable   <chr> "7.712e-2", NA, "8.967e-2", "3.562e-2", NA, "…
#> $ prognostic_unfavorable   <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ unprognostic_unfavorable <chr> NA, NA, NA, NA, "2.567e-1", NA, "5.932e-2", N…

The subcellular_location dataset contains information about subcellular localization of proteins based on IF stanings of normal cells. The datasets contain eleven columns: ensembl (Ensembl gene identifier); gene (HGNC symbol); reliability (gene reliability score); enhanced (enhanced locations); supported (supported locations); approved (approved locations); uncertain (uncertain locations); single_cell_var_intensity (locations with single-cell variation in intensity); single_cell_var_spatial (locations with spatial single-cell variation); cell_cycle_dependency (locations with observed cell cycle dependency); go_id (Gene Ontology Cellular Component term identifier).

tibble::glimpse(downloadedData$subcellular_location, give.attr=FALSE)
#> Rows: 13,041
#> Columns: 14
#> $ ensembl                   <chr> "ENSG00000000003", "ENSG00000000457", "ENSG0…
#> $ gene                      <chr> "TSPAN6", "SCYL3", "C1orf112", "FGR", "CFH",…
#> $ reliability               <chr> "Approved", "Uncertain", "Approved", "Approv…
#> $ main_location             <chr> "Cell Junctions;Cytosol", "Microtubules", "M…
#> $ additional_location       <chr> "Nucleoli fibrillar center", "Nuclear bodies…
#> $ extracellular_location    <chr> NA, NA, NA, NA, "Predicted to be secreted", …
#> $ enhanced                  <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
#> $ supported                 <chr> NA, NA, NA, NA, NA, NA, "Nucleoplasm", NA, N…
#> $ approved                  <chr> "Cell Junctions;Cytosol;Nucleoli fibrillar c…
#> $ uncertain                 <chr> NA, "Microtubules;Nuclear bodies", NA, NA, N…
#> $ single_cell_var_intensity <chr> "Cytosol", NA, NA, NA, NA, "Cytosol;Nucleopl…
#> $ single_cell_var_spatial   <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
#> $ cell_cycle_dependency     <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
#> $ go_id                     <chr> "Cell Junctions (GO:0030054);Cytosol (GO:000…

3.1.2 The RNA datasets

HPA provide a large number of RNA expression datasets. More details can be find at https://www.proteinatlas.org/about/download. To see how to download these datasets, please see the help page for hpaDownload:

?hpaDownload

There are shortcuts to download multiple RNA datasets, for example:

downloadedData <- hpaDownload(downloadList='rna tissue')

tibble::glimpse(downloadedData, give.attr=FALSE)

#> List of 4
#>  $ rna_tissue_consensus: tibble [1,180,464 x 4] (S3: tbl_df/tbl/data.frame)
#>   ..$ ensembl: chr [1:1180464] "ENSG00000000003" "ENSG00000000003" "ENSG00000000003" "ENSG00000000003" ...
#>   ..$ gene   : chr [1:1180464] "TSPAN6" "TSPAN6" "TSPAN6" "TSPAN6" ...
#>   ..$ tissue : chr [1:1180464] "adipose tissue" "adrenal gland" "amygdala" "appendix" ...
#>   ..$ nx     : chr [1:1180464] "27.0" "9.8" "7.0" "4.4" ...
#>  $ rna_tissue_hpa      : tibble [845,810 x 6] (S3: tbl_df/tbl/data.frame)
#>   ..$ ensembl: chr [1:845810] "ENSG00000000003" "ENSG00000000003" "ENSG00000000003" "ENSG00000000003" ...
#>   ..$ gene   : chr [1:845810] "TSPAN6" "TSPAN6" "TSPAN6" "TSPAN6" ...
#>   ..$ tissue : chr [1:845810] "adipose tissue" "adrenal gland" "appendix" "B-cells" ...
#>   ..$ tpm    : chr [1:845810] "31.5" "26.4" "9.2" "0.1" ...
#>   ..$ ptpm   : chr [1:845810] "37.7" "32.7" "14.5" "0.2" ...
#>   ..$ nx     : chr [1:845810] "9.8" "7.6" "2.1" "0.3" ...
#>  $ rna_tissue_gtex     : tibble [639,744 x 6] (S3: tbl_df/tbl/data.frame)
#>   ..$ ensembl: chr [1:639744] "ENSG00000000003" "ENSG00000000003" "ENSG00000000003" "ENSG00000000003" ...
#>   ..$ gene   : chr [1:639744] "TSPAN6" "TSPAN6" "TSPAN6" "TSPAN6" ...
#>   ..$ tissue : chr [1:639744] "adipose tissue" "adrenal gland" "amygdala" "basal ganglia" ...
#>   ..$ tpm    : chr [1:639744] "27.4" "15.5" "7.3" "7.7" ...
#>   ..$ ptpm   : chr [1:639744] "34.2" "18.8" "9.0" "9.4" ...
#>   ..$ nx     : chr [1:639744] "13.4" "9.8" "7.0" "6.6" ...
#>  $ rna_tissue_fantom   : tibble [797,265 x 6] (S3: tbl_df/tbl/data.frame)
#>   ..$ ensembl                : chr [1:797265] "ENSG00000000003" "ENSG00000000003" "ENSG00000000003" "ENSG00000000003" ...
#>   ..$ gene                   : chr [1:797265] "TSPAN6" "TSPAN6" "TSPAN6" "TSPAN6" ...
#>   ..$ tissue                 : chr [1:797265] "adipose tissue" "amygdala" "appendix" "basal ganglia" ...
#>   ..$ tags_per_million       : chr [1:797265] "60.1" "10.7" "13.5" "16.2" ...
#>   ..$ scaled_tags_per_million: chr [1:797265] "87.4" "12.4" "17.8" "18.0" ...
#>   ..$ nx                     : chr [1:797265] "27.0" "3.5" "4.4" "5.5" ...

3.2 List available parameter for subsetting with `hpaListParam()`

To see what parameters are available for subsequent subsetting/visualizing, HPAanalyze includes the function hpaListParam(). The input for this function is the output of hpaDownload.

If you leave the argument blank, this function will give you the results for the built-in datasets.

## If you use the output from hpaDownload()
downloadedData <- hpaDownload(downloadList=c("Normal tissue", "Pathology", "RNA HPA tissue", "RNA HPA cell line"))
str(hpaListParam(downloadedData))

# List of 4
#  $ normal_tissue :List of 2
#   ..$ tissue   : chr [1:63] "adipose tissue" "adrenal gland" "appendix" "bone marrow" ...
#   ..$ cell_type: chr [1:120] "adipocytes" "glandular cells" "lymphoid tissue" "hematopoietic cells" ...
#  $ pathology     :List of 1
#   ..$ cancer: chr [1:20] "breast cancer" "carcinoid" "cervical cancer" "colorectal cancer" ...
#  $ rna_tissue_hpa:List of 1
#   ..$ tissue: chr [1:43] "adipose tissue" "adrenal gland" "appendix" "B-cells" ...
#  $ rna_celline   :List of 1
#   ..$ cell_line: chr [1:69] "A-431" "A549" "AF22" "AN3-CA" ...

3.3 Subset data with `hpaSubset()`

hpaSubset() filters the output of hpaDownload() for desirable target genes, tissues, cell types, cancer, and cell lines. The data will be subset only where applicable (i.e. normal_tissue will not be subset by cancer). The main purpose of hpaSubset is to prepare a manageable set of data to be exported. However, this function may also be useful for other data table manipulation purposes. The input for targetGene argument is a vector of strings of HGNC symbols.

If you leave the data argument blank, this function will automatically subset the built-in dataset, which may not contain all of the columns available if you download the data with hpaDownload().

downloadedData <- hpaDownload(downloadList='histology', version='example')
sapply(downloadedData, nrow)
#>        normal_tissue            pathology subcellular_location 
#>              1193218               401800                13041

geneList <- c('TP53', 'EGFR', 'CD44', 'PTEN', 'IDH1', 'IDH2', 'CYCS')
tissueList <- c('breast', 'cerebellum', 'skin 1')
cancerList <- c('breast cancer', 'glioma', 'melanoma')
cellLineList <- c('A-431', 'A549', 'AF22', 'AN3-CA')

subsetData <- hpaSubset(data=downloadedData,
                         targetGene=geneList,
                         targetTissue=tissueList,
                         targetCancer=cancerList,
                         targetCellLine=cellLineList)
sapply(subsetData, nrow)
#>        normal_tissue            pathology subcellular_location 
#>                  126                   21                    7

3.4 Export data with `hpaExport()`

As the name suggests, hpaExport() exports the output of hpaSubset() to one .xlsx file, or multiple .csv or .tsv files. Each dataset is placed in a separate sheet.

hpaExport(subsetData, fileName='subset.xlsx', fileType='xlsx')

4 Visualization

HPAanalyze provides the ability to quickly visualize data from downloaded HPA datasets with the hpaVis function family. The goal of these functions is to aid exploratory analysis of a group of target genes, which maybe particularly useful for gaining insights into pathways or gene signatures of interest.

The hpaVis functions share a common syntax, where the input (data argument) is the output of hpaDownload() or hpaSubset() (although they do their own subseting so it is not necessary to use hpaSubset() unless you want to reduce the size of your data object). Depending on the function, the target arguments will let you choose to visualize your vectors of genes, tissue, cell types, etc. (See the help files for more details.) All of hpaVis functions generate standard ggplot2 plots, which allow you to further customize colors and themes. Colors maybe changed via the color argument, while the default theme maybe overriden by setting the customTheme argument to FALSE.

Currently, the normal_tissue, pathology and subcellular_location data can be visualized, with more functions planned for future releases.

For all functions in the hpaVis family, if you leave the data argument blank, they will plot the built-in datasets by default.

4.1 Unbrella function `hpaVis()`

hpaVis will plot all available plots by default. See the quick-start vignette for details.

hpaVis(downloadedData,
       targetGene = c("GCH1", "PTS", "SPR", "DHFR"),
       targetTissue = c("cerebellum", "cerebral cortex", "hippocampus"),
       targetCancer = c("glioma"))

4.2 Visualize tissue data with `hpaVisTissue()`

hpaVisTissue() generates a “heatmap”, in which the expression of proteins of interest (quantified IHC staining) are plotted for each cell type of each tissue.

geneList <- c('TP53', 'EGFR', 'CD44', 'PTEN', 'IDH1', 'IDH2', 'CYCS')
tissueList <- c('breast', 'cerebellum', 'skin 1')

hpaVisTissue(downloadedData,
             targetGene=geneList,
             targetTissue=tissueList)

4.3 Visualize expression in cancer with `hpaVisPatho()`

hpaVisPatho() generates an arrays of column graphs showing the expression of proteins of interest in each cancer.

This example also demonstrate how the colors of the graphs could be customized, which is a common functionality of the hpaVis family.

geneList <- c('TP53', 'EGFR', 'CD44', 'PTEN', 'IDH1', 'IDH2', 'CYCS')
cancerList <- c('breast cancer', 'glioma', 'lymphoma', 'prostate cancer')
colorGray <- c('slategray1', 'slategray2', 'slategray3', 'slategray4')

hpaVisPatho(downloadedData,
            targetGene=geneList,
            targetCancer=cancerList,
            color=colorGray)

4.4 Visualize subcellular location data with `hpaVisSubcell()`

hpaVisSubcell() generates a tile chart showing the subcellular locations (approved and supported) of proteins of interest.

This example also demonstrate the customization of the output plot with ggplot2 functions, which is applicable to all hpaVis functions. Notice that the customTheme argument is set to TRUE.

geneList <- c('TP53', 'EGFR', 'CD44', 'PTEN', 'IDH1', 'IDH2', 'CYCS')

hpaVisSubcell(downloadedData,
              targetGene=geneList,
              customTheme=TRUE) +
    ggplot2::theme_minimal() +
    ggplot2::ylab('Subcellular locations') +
    ggplot2::xlab('Protein') +
    ggplot2::theme(axis.text.x=element_text(angle=45, hjust=1))  +
    ggplot2::theme(legend.position="none") +
    ggplot2::coord_equal()

5 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.

A typical workflow for working with XML files includes the following steps: (1) Download and import XML file with hpaXmlGet(); (2) Extract the desired information with other hpaXml functions; and (3) Download histological staining pictures, which is currently supported by the hpaXmlTissurExpr() and hpaXmlTissueExprSum() functions.

5.1 The umbrella function `hpaXml`

hpaXml will take an Ensembl gene id (start with ENSG) and extract all availble information. You can also feed the ourput of hpaXmlGet to it. See the quick-start vignette for more details.

EGFR <- hpaXml(inputXml='ENSG00000146648')
names(EGFR)

#> [1] "ProtClass"     "TissueExprSum" "Antibody"      "TissueExpr"

5.2 Import xml file with `hpaXmlGet()`

The hoaXmlGet() function takes an Ensembl gene id (start with ENSG) and import the perspective XML file into R. This function calls the xml2::read_xml() under the hood, hence the resulting object may be processed further with xml2 functions if desired.

EGFRxml <- hpaXmlGet('ENSG00000146648')

5.3 View protein classes with `hpaXmlProtClass()`

Protein class of queried protein can be extracted from the imported XML with hpaXmlProtClass(). The output of this function is a tibble of 4 columns: id, name, parent_id and source

hpaXmlProtClass(EGFRxml)

#> # A tibble: 40 x 4
#>    id    name                                   parent_id source    
#>    <chr> <chr>                                  <chr>     <chr>     
#>  1 Ez    Enzymes                                <NA>      <NA>      
#>  2 Ec    ENZYME proteins                        Ez        ENZYME    
#>  3 Et    Transferases                           Ec        ENZYME    
#>  4 Ki    Kinases                                Ez        UniProt   
#>  5 Kt    Tyr protein kinases                    Ki        UniProt   
#>  6 Ma    Predicted membrane proteins            <NA>      MDM       
#>  7 Md    Membrane proteins predicted by MDM     <NA>      MDM       
#>  8 Me    MEMSAT3 predicted membrane proteins    <NA>      MEMSAT3   
#>  9 Mf    MEMSAT-SVM predicted membrane proteins <NA>      MEMSAT-SVM
#> 10 Mg    Phobius predicted membrane proteins    <NA>      Phobius   
#> # ... with 30 more rows

5.4 Get summary and images of tissue expression with `hpaXmlTissueExprSum()`

The function hpaXmlTissueExprSum() extract the summary of expression of protein of interest in normal tissue. The output of this function is a list of (1) a string contains one-sentence summary and (2) a dataframe of all tissues in which the protein was stained positive and a histological stain images of those tissue.

hpaXmlTissueExprSum(EGFRxml)

#> $summary
#> [1] "Cytoplasmic and membranous expression in several tissues, most abundant in placenta."
#> 
#> $img
#>            tissue
#> 1 cerebral cortex
#> 2      lymph node
#> 3           liver
#> 4           colon
#> 5          kidney
#> 6          testis
#> 7        placenta
#>                                                                imageUrl
#> 1 http://v18.proteinatlas.org/images/18530/41191_B_7_5_rna_selected.jpg
#> 2 http://v18.proteinatlas.org/images/18530/41191_A_7_8_rna_selected.jpg
#> 3 http://v18.proteinatlas.org/images/18530/41191_A_7_4_rna_selected.jpg
#> 4 http://v18.proteinatlas.org/images/18530/41191_A_9_3_rna_selected.jpg
#> 5 http://v18.proteinatlas.org/images/18530/41191_A_9_5_rna_selected.jpg
#> 6 http://v18.proteinatlas.org/images/18530/41191_A_6_6_rna_selected.jpg
#> 7 http://v18.proteinatlas.org/images/18530/41191_A_1_7_rna_selected.jpg

Those images can be downloaded automatically by setting the downloadImg argument to TRUE. Eg. hpaXmlTissueExprSum(CCNB1xml, downloadImg=TRUE)

5.5 Get details of individual IHC samples with `hpaXmlAntibody()` and `hpaXmlTissueExpr()`

More importantly, the XML files are the only format of HPA programmatically accesible data which contains information about each antibody and each tissue sample used in the project.

hpaXmlAntibody() extract the antibody information and return a tibble with one row for each antibody.

hpaXmlAntibody(EGFRxml)

#> # A tibble: 5 x 4
#>   id        releaseDate releaseVersion RRID      
#>   <chr>     <chr>       <chr>          <chr>     
#> 1 CAB000035 2006-03-13  1.2            <NA>      
#> 2 HPA001200 2008-02-15  3.1            AB_1078723
#> 3 HPA018530 2008-12-03  4.1            AB_1848044
#> 4 CAB068186 2014-11-06  13             AB_2665679
#> 5 CAB073534 2015-10-16  14             <NA>

hpaXmlTissueExpr() extract information about all samples for each antibody above and return a list of tibbles. If antibody has not been used for IHC staining, the returned tibble with be empty.

tissueExpression <- hpaXmlTissueExpr(EGFRxml)
summary(tissueExpression)

#>      Length Class  Mode
#> [1,] 18     tbl_df list
#> [2,] 18     tbl_df list
#> [3,] 18     tbl_df list
#> [4,] 18     tbl_df list
#> [5,] 18     tbl_df list

Each tibble contain clinical data (patientid, age, sex), tissue information (snomedCode, tissueDescription), staining results (staining, intensity, location) and one imageUrl for each sample. However, due to the large amount of data and the relatively large size of each image, hpaXmlTissueExpr does not provide an automated download option.

tissueExpression[[1]]

#> # A tibble: 327 x 18
#>    patientId age   sex   staining intensity quantity location imageUrl
#>    <chr>     <chr> <chr> <chr>    <chr>     <chr>    <chr>    <chr>   
#>  1 1653      53    Male  <NA>     <NA>      <NA>     <NA>     http://~
#>  2 1721      60    Fema~ <NA>     <NA>      <NA>     <NA>     http://~
#>  3 1725      57    Male  <NA>     <NA>      <NA>     <NA>     http://~
#>  4 4         25    Male  <NA>     <NA>      <NA>     <NA>     http://~
#>  5 512       34    Fema~ <NA>     <NA>      <NA>     <NA>     http://~
#>  6 2664      74    Fema~ <NA>     <NA>      <NA>     <NA>     http://~
#>  7 2665      88    Fema~ <NA>     <NA>      <NA>     <NA>     http://~
#>  8 1391      54    Fema~ <NA>     <NA>      <NA>     <NA>     http://~
#>  9 1447      45    Fema~ <NA>     <NA>      <NA>     <NA>     http://~
#> 10 1452      44    Fema~ <NA>     <NA>      <NA>     <NA>     http://~
#> # ... with 317 more rows, and 10 more variables: snomedCode1 <chr>,
#> #   snomedCode2 <chr>, snomedCode3 <chr>, snomedCode4 <chr>,
#> #   snomedCode5 <chr>, tissueDescription1 <chr>, tissueDescription2 <chr>,
#> #   tissueDescription3 <chr>, tissueDescription4 <chr>,
#> #   tissueDescription5 <chr>

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

6 Compatibility with `hpar` Bioconductor package

Table 1: Complementary functionality between hpar and HPAanalyze
Functionality	hpar	HPAanalyze
Datasets	Included in package	Download from server or use built-in dataset
Query	Ensembl id	HGNC symbol for datasets, Ensembl id for XML
Data version	One stable version	Latest by default, option to download older
Release info	Access via functions	N/A
View relevant browser page	Via `getHPA` function	N/A
Visualization	N/A	Exploratory via `hpaVis` functions
XML	N/A	Download and import via `hpaXml` functions
Histology image	View by loading browser page	Extract links via `hpaXml` functions

7 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.

8 Copyright

Anh Tran, 2018-2022

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

2. In-depth: Working with Human Protein Atlas (HPA) data in R with HPAanalyze

4/2/2021

1 Summary

2 Background

2.1 The different HPA data formats

2.2 `HPAanalyze` overview

2.2.1 Obtaining `HPAanalyze`

3 Full dataset import, subsetting and export

3.1 Download and import data with `hpaDownload()`

3.1.1 The “histology” datasets

3.1.2 The RNA datasets

3.2 List available parameter for subsetting with `hpaListParam()`

3.3 Subset data with `hpaSubset()`

3.4 Export data with `hpaExport()`

4 Visualization

4.1 Unbrella function `hpaVis()`

4.2 Visualize tissue data with `hpaVisTissue()`

4.3 Visualize expression in cancer with `hpaVisPatho()`

4.4 Visualize subcellular location data with `hpaVisSubcell()`

5 Individual xml import and image downloading

5.1 The umbrella function `hpaXml`

5.2 Import xml file with `hpaXmlGet()`

5.3 View protein classes with `hpaXmlProtClass()`

5.4 Get summary and images of tissue expression with `hpaXmlTissueExprSum()`

5.5 Get details of individual IHC samples with `hpaXmlAntibody()` and `hpaXmlTissueExpr()`

6 Compatibility with `hpar` Bioconductor package

7 Acknowledgements

8 Copyright

2. In-depth: Working with Human Protein Atlas (HPA) data in R with HPAanalyze

4/2/2021

1 Summary

2 Background

2.1 The different HPA data formats

2.2 HPAanalyze overview

2.2.1 Obtaining HPAanalyze

3 Full dataset import, subsetting and export

3.1 Download and import data with hpaDownload()

3.1.1 The “histology” datasets

3.1.2 The RNA datasets

3.2 List available parameter for subsetting with hpaListParam()

3.3 Subset data with hpaSubset()

3.4 Export data with hpaExport()

4 Visualization

4.1 Unbrella function hpaVis()

4.2 Visualize tissue data with hpaVisTissue()

4.3 Visualize expression in cancer with hpaVisPatho()

4.4 Visualize subcellular location data with hpaVisSubcell()

5 Individual xml import and image downloading

5.1 The umbrella function hpaXml

5.2 Import xml file with hpaXmlGet()

5.3 View protein classes with hpaXmlProtClass()

5.4 Get summary and images of tissue expression with hpaXmlTissueExprSum()

5.5 Get details of individual IHC samples with hpaXmlAntibody() and hpaXmlTissueExpr()

6 Compatibility with hpar Bioconductor package

7 Acknowledgements

8 Copyright

2.2 `HPAanalyze` overview

2.2.1 Obtaining `HPAanalyze`

3.1 Download and import data with `hpaDownload()`

3.2 List available parameter for subsetting with `hpaListParam()`

3.3 Subset data with `hpaSubset()`

3.4 Export data with `hpaExport()`

4.1 Unbrella function `hpaVis()`

4.2 Visualize tissue data with `hpaVisTissue()`

4.3 Visualize expression in cancer with `hpaVisPatho()`

4.4 Visualize subcellular location data with `hpaVisSubcell()`

5.1 The umbrella function `hpaXml`

5.2 Import xml file with `hpaXmlGet()`

5.3 View protein classes with `hpaXmlProtClass()`

5.4 Get summary and images of tissue expression with `hpaXmlTissueExprSum()`

5.5 Get details of individual IHC samples with `hpaXmlAntibody()` and `hpaXmlTissueExpr()`

6 Compatibility with `hpar` Bioconductor package