3.2.1 Obtaining HPAanalyze

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

devtools::install_github("trannhatanh89/HPAanalyze")

3.2.2 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.

3.2.3 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.

3.2.4 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.

3.2.5 Compatibility with hpar Bioconductor package

3.3.1 Download and import data with hpaDownload()

downloadedData <- hpaDownload(downloadList='all')
summary(downloadedData)

#>                          Length Class  Mode
#> normal_tissue             6     tbl_df list
#> pathology                11     tbl_df list
#> subcellular_location     11     tbl_df list
#> rna_tissue                5     tbl_df list
#> rna_cell_line             5     tbl_df list
#> transcript_rna_tissue     4     tbl_df list
#> transcript_rna_cell_line  4     tbl_df list

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).

downloadedData <- hpaDownload(downloadList='histology', version='example')
tibble::glimpse(downloadedData$normal_tissue, give.attr=FALSE)
#> Observations: 1,053,330
#> Variables: 5
#> $ ensembl   <chr> "ENSG00000000003", "ENSG00000000003", "ENSG00000000003...
#> $ gene      <chr> "TSPAN6", "TSPAN6", "TSPAN6", "TSPAN6", "TSPAN6", "TSP...
#> $ tissue    <chr> "adrenal gland", "appendix", "appendix", "bone marrow"...
#> $ cell_type <chr> "glandular cells", "glandular cells", "lymphoid tissue...
#> $ level     <chr> "Not detected", "Medium", "Not detected", "Not detecte...

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)
#> Observations: 392,260
#> Variables: 7
#> $ ensembl      <chr> "ENSG00000000003", "ENSG00000000003", "ENSG00000000...
#> $ gene         <chr> "TSPAN6", "TSPAN6", "TSPAN6", "TSPAN6", "TSPAN6", "...
#> $ cancer       <chr> "breast cancer", "carcinoid", "cervical cancer", "c...
#> $ high         <int> 1, 0, 11, 0, 10, 0, 0, 4, 8, 0, 0, 8, 11, 5, 2, 9, ...
#> $ medium       <int> 7, 1, 1, 6, 2, 0, 3, 5, 4, 0, 1, 3, 1, 6, 4, 2, 1, ...
#> $ low          <int> 2, 1, 0, 2, 0, 0, 1, 1, 0, 0, 2, 0, 0, 0, 1, 0, 0, ...
#> $ not_detected <int> 2, 2, 0, 2, 0, 11, 0, 0, 0, 11, 9, 0, 0, 0, 5, 0, 0...

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)
#> Observations: 12,073
#> Variables: 8
#> $ ensembl     <chr> "ENSG00000000003", "ENSG00000000457", "ENSG000000004...
#> $ gene        <chr> "TSPAN6", "SCYL3", "C1orf112", "FGR", "CFH", "GCLC",...
#> $ reliability <chr> "Approved", "Uncertain", "Approved", "Approved", "Ap...
#> $ enhanced    <chr> NA, NA, NA, NA, NA, NA, "Nucleoplasm", NA, NA, NA, N...
#> $ supported   <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, "Nucleoplasm", "...
#> $ approved    <chr> "Cytosol", NA, "Mitochondria", "Aggresome;Plasma mem...
#> $ uncertain   <chr> NA, "Microtubules;Nuclear bodies", NA, NA, NA, NA, N...
#> $ go_id       <chr> "Cytosol (GO:0005829)", "Microtubules (GO:0015630);N...

The rna_tissue and rna_cell_line datasets contain RNA expression levels of 37 tissues and 64 cell lines based on RNA-seq. These datasets contain four columns each: ensembl (Ensembl gene identifier); gene (HGNC symbol); tissue/cell_line (type of sample); value + unit (expression level measured by transcripts per million).

downloadedData <- hpaDownload(downloadList='rna', version='hpar')

tibble::glimpse(downloadedData$rna_tissue, give.attr=FALSE)
#> Observations: 725,681
#> Variables: 5
#> $ ensembl <fct> ENSG00000000003, ENSG00000000003, ENSG00000000003, ENSG0...
#> $ gene    <fct> TSPAN6, TSPAN6, TSPAN6, TSPAN6, TSPAN6, TSPAN6, TSPAN6, ...
#> $ tissue  <fct> adipose tissue, adrenal gland, appendix, bone marrow, br...
#> $ value   <dbl> 31.5, 26.4, 9.2, 0.7, 53.4, 18.5, 54.2, 48.5, 27.1, 38.5...
#> $ unit    <fct> TPM, TPM, TPM, TPM, TPM, TPM, TPM, TPM, TPM, TPM, TPM, T...

tibble::glimpse(downloadedData$rna_cell_line, give.attr=FALSE)
#> Observations: 1,255,232
#> Variables: 5
#> $ ensembl   <fct> ENSG00000000003, ENSG00000000003, ENSG00000000003, ENS...
#> $ gene      <fct> TSPAN6, TSPAN6, TSPAN6, TSPAN6, TSPAN6, TSPAN6, TSPAN6...
#> $ cell_line <fct> A-431, A549, AF22, AN3-CA, ASC diff, ASC TERT1, BEWO, ...
#> $ value     <dbl> 27.8, 37.6, 108.1, 51.8, 32.3, 17.7, 42.7, 14.9, 22.4,...
#> $ unit      <fct> TPM, TPM, TPM, TPM, TPM, TPM, TPM, TPM, TPM, TPM, TPM,...

Similarly, the transcript_rna_tissue and transcript_rna_cell_line datasets contain RNA isoform levels. These datasets contain four columns each: ensembl (Ensembl gene identifier); transcript (Ensembl transcript identifier); tissue/cell_line (type of sample); value (expression level measured by transcripts per million). Note that these datasets are significantly larger than others and should only be downloaded when necessary.

downloadedData <- hpaDownload(downloadList='isoform', version='v18')

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

#> Observations: 27,535,996
#> Variables: 4
#> $ ensembl    <chr> "ENSG00000000003", "ENSG00000000003", "ENSG0000000...
#> $ transcript <chr> "ENST00000373020", "ENST00000494424", "ENST0000049...
#> $ tissue     <chr> "adipose tissue.V1", "adipose tissue.V1", "adipose...
#> $ value      <dbl> 27.3577003, 0.0000000, 1.9341500, 1.6059300, 0.000...

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

#> Observations: 20,972,183
#> Variables: 4
#> $ ensembl    <chr> "ENSG00000000003", "ENSG00000000003", "ENSG0000000...
#> $ transcript <chr> "ENST00000373020", "ENST00000494424", "ENST0000049...
#> $ cell_line  <chr> "A-431.C35", "A-431.C35", "A-431.C35", "A-431.C35"...
#> $ value      <dbl> 29.406799, 0.000000, 0.992916, 0.398387, 0.239204,...

Optionally, data can be imported from the hpar package. Please note that the hpar package does not contain the RNA isoform datasets, which are very large and should be downloaded only when necessary.

For data from hpar, release information can be accessed with getHpaVersion, getHpaDate and getHpaEnsembl.

hpaDownload('all', 'hpar')

hpar::getHpaVersion()
#> version 
#>    "18"

hpar::getHpaDate()
#>         date 
#> "2017.12.01"

hpar::getHpaEnsembl()
#> ensembl 
#> "88.38"

3.3.2 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.

downloadedData <- hpaDownload(downloadList='all')
str(hpaListParam(downloadedData))

#> List of 6
#>  $ normal_tissue       : chr [1:58] "adrenal gland" "appendix" "bone marrow" "breast" ...
#>  $ normal_cell         : chr [1:82] "glandular cells" "lymphoid tissue" "hematopoietic cells" "adipocytes" ...
#>  $ cancer              : chr [1:20] "breast cancer" "carcinoid" "cervical cancer" "colorectal cancer" ...
#>  $ subcellular_location: chr [1:32] "Cytosol" "Mitochondria" "Aggresome" "Plasma membrane" ...
#>  $ normal_tissue_rna   : chr [1:37] "adipose tissue" "adrenal gland" "appendix" "bone marrow" ...
#>  $ cell_line_rna       : chr [1:64] "A-431" "A549" "AF22" "AN3-CA" ...

3.3.3 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.

downloadedData <- hpaDownload(downloadList='histology', version='example')
sapply(downloadedData, nrow)
#>        normal_tissue            pathology subcellular_location 
#>              1053330               392260                12073

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 
#>                   70                   21                    7

To subset by Ensemble gene id, use hpar::getHPA().

id <- c("ENSG00000000003", "ENSG00000000005")
hpar::getHpa(id, hpadata="hpaNormalTissue") %>%
    tibble::glimpse()
#> Observations: 80
#> Variables: 6
#> $ Gene        <fct> ENSG00000000003, ENSG00000000003, ENSG00000000003, E...
#> $ Gene.name   <fct> TSPAN6, TSPAN6, TSPAN6, TSPAN6, TSPAN6, TSPAN6, TSPA...
#> $ Tissue      <fct> adrenal gland, appendix, appendix, bone marrow, brea...
#> $ Cell.type   <fct> glandular cells, glandular cells, lymphoid tissue, h...
#> $ Level       <fct> Not detected, Medium, Not detected, Not detected, No...
#> $ Reliability <fct> Approved, Approved, Approved, Approved, Approved, Ap...

3.3.4 Export data with hpaExport()

As the name suggests, hpaExport() exports the output of hpaSubset() to one .xlsx file. Each dataset is placed in a separate sheet. More formats such as .csv and .tsv might be added in future release; hence, the fileType argument is included.

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

3.4.1 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)

3.4.2 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)

3.4.3 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()

3.5.1 Import xml file with hpaXmlGet()

The hoaXmlGet() function takes a 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.

CCNB1xml <- hpaXmlGet('ENSG00000134057')

To view the web page for the protein of interest, which contains similar information in a pretty presentation, use hpar function getHPA() with argument type="details".

hpar::getHpa('ENSG00000134057', type="details")

3.5.2 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(CCNB1xml)
#> # A tibble: 10 x 4
#>    id    name                               parent_id source                
#>    <chr> <chr>                              <chr>     <chr>                 
#>  1 Ja    Transporters                       <NA>      TCDB                  
#>  2 Jb    Transporter channels and pores     Ja        TCDB                  
#>  3 Mi    SPOCTOPUS predicted membrane prot~ <NA>      SPOCTOPUS             
#>  4 Za    Predicted intracellular proteins   <NA>      HPA                   
#>  5 Pp    Plasma proteins                    <NA>      Plasma Proteome Datab~
#>  6 Ca    Cancer-related genes               <NA>      <NA>                  
#>  7 Cb    Candidate cancer biomarkers        Ca        Plasma Proteome Insti~
#>  8 Ua    UniProt - Evidence at protein lev~ <NA>      UniProt               
#>  9 Ea    Protein evidence (Kim et al 2014)  <NA>      Kim et al 2014        
#> 10 Eb    Protein evidence (Ezkurdia et al ~ <NA>      Ezkurdia et al 2014

3.5.3 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(CCNB1xml)
#> $summary
#> [1] "Cytoplasmic expression in proliferating cells."
#> 
#> $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/3804/10580_B_8_5_rna_selected.jpg
#> 2 http://v18.proteinatlas.org/images/3804/10580_A_8_8_rna_selected.jpg
#> 3 http://v18.proteinatlas.org/images/3804/10580_A_7_4_rna_selected.jpg
#> 4 http://v18.proteinatlas.org/images/3804/10580_A_9_3_rna_selected.jpg
#> 5 http://v18.proteinatlas.org/images/3804/10580_A_9_5_rna_selected.jpg
#> 6 http://v18.proteinatlas.org/images/3804/10580_A_4_6_rna_selected.jpg
#> 7 http://v18.proteinatlas.org/images/3804/10580_A_2_7_rna_selected.jpg

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

3.5.4 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(CCNB1xml)
#> # A tibble: 4 x 4
#>   RRID       id        releaseDate releaseVersion
#>   <chr>      <chr>     <chr>       <chr>         
#> 1 <NA>       CAB000115 2006-03-13  1.2           
#> 2 AB_562272  CAB003804 2006-10-30  2             
#> 3 AB_2673586 HPA030741 2013-12-05  12            
#> 4 AB_2684522 HPA061448 2016-12-04  16

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(CCNB1xml)
summary(tissueExpression)
#>      Length Class  Mode
#> [1,] 18     tbl_df list
#> [2,] 18     tbl_df list
#> [3,]  0     tbl_df list
#> [4,] 16     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: 331 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://~
#>  4 598       7     Male  <NA>     <NA>      <NA>     <NA>     http://~
#>  5 666       82    Fema~ <NA>     <NA>      <NA>     <NA>     http://~
#>  6 2637      36    Fema~ <NA>     <NA>      <NA>     <NA>     http://~
#>  7 2667      75    Male  <NA>     <NA>      <NA>     <NA>     http://~
#>  8 2671      72    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 321 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>