Human Protein Atlas in R

Laurent Gatto1

1de Duve Institute, UCLouvain, Belgium

26 October 2021

Abstract

The Human Protein Atlas (HPA) is a systematic study oh the human proteome using antibody-based proteomics. Multiple tissues and cell lines are systematically assayed affinity-purified antibodies and confocal microscopy. The hpar package is an R interface to the HPA project. It distributes three data sets, provides functionality to query these and to access detailed information pages, including confocal microscopy images available on the HPA web page.

Package

hpar 1.36.0

1 Introduction

1.1 The HPA project

From the Human Protein Atlas (Uhlén et al. 2005; Uhlen et al. 2010) site:

The Swedish Human Protein Atlas project, funded by the Knut and Alice Wallenberg Foundation, has been set up to allow for a systematic exploration of the human proteome using Antibody-Based Proteomics. This is accomplished by combining high-throughput generation of affinity-purified antibodies with protein profiling in a multitude of tissues and cells assembled in tissue microarrays. Confocal microscopy analysis using human cell lines is performed for more detailed protein localisation. The program hosts the Human Protein Atlas portal with expression profiles of human proteins in tissues and cells.

The hpar package provides access to HPA data from the R interface. It also distributes the following data sets:

hpaNormalTissue Normal tissue data: Expression profiles for proteins in human tissues based on immunohistochemisty using tissue micro arrays. The tab-separated file includes Ensembl gene identifier (“Gene”), tissue name (“Tissue”), annotated cell type (“Cell type”), expression value (“Level”), and the gene reliability of the expression value (“Reliability”).}
hpaNormalTissue16.1: Same as above, for version 16.1.
hpaCancer Pathology data: Staining profiles for proteins in human tumor tissue based on immunohistochemisty using tissue micro arrays and log-rank P value for Kaplan-Meier analysis of correlation between mRNA expression level and patient survival. The tab-separated file includes Ensembl gene identifier (“Gene”), gene name (“Gene name”), tumor name (“Cancer”), the number of patients annotated for different staining levels (“High”, “Medium”, “Low” & “Not detected”) and log-rank p values for patient survival and mRNA correlation (“prognostic - favourable”, “unprognostic - favourable”, “prognostic - unfavourable”, “unprognostic - unfavourable”). }
hpaCancer16.1: Same as above, for version 16.1.
rnaGeneTissue RNA HPA tissue gene data: Transcript expression levels summarized per gene in 37 tissues based on RNA-seq. The tab-separated file includes Ensembl gene identifier (“Gene”), analysed sample (“Tissue”), transcripts per million (“TPM”), protein-transcripts per million (“pTPM”) and normalized expression (“NX”). }
rnaGeneCellLine RNA HPA cell line gene data: Transcript expression levels summarized per gene in 64 cell lines. The tab-separated file includes Ensembl gene identifier (“Gene”), analysed sample (“Cell line”), transcripts per million (“TPM”), protein-coding transcripts per million (“pTPM”) and normalized expression (“NX”). }
rnaGeneCellLine16.1: Same as above, for version 16.1.
hpaSubcellularLoc Subcellular location data: Subcellular location of proteins based on immunofluorescently stained cells. The tab-separated file includes the following columns: Ensembl gene identifier (“Gene”), name of gene (“Gene name”), gene reliability score (“Reliability”), enhanced locations (“Enhanced”), supported locations (“Supported”), Approved locations (“Approved”), uncertain locations (“Uncertain”), locations with single-cell variation in intensity (“Single-cell variation intensity”), locations with spatial single-cell variation (“Single-cell variation spatial”), locations with observed cell cycle dependency (type can be one or more of biological definition, custom data or correlation) (“Cell cycle dependency”), Gene Ontology Cellular Component term identifier (“GO id”).}
hpaSubcellularLoc14 and *16.1: Same as above, for versions 14 and 16.1.
hpaSecretome Secretome data: The human secretome is here defined as all Ensembl genes with at least one predicted secreted transcript according to HPA predictions. The complete information about the HPA Secretomedata is given on . This dataset has 230 columns and includes the Ensembl gene identifier (“Gene”). Information about the additionnal variables can be found by clicking on .

1.2 HPA data usage policy

The use of data and images from the HPA in publications and presentations is permitted provided that the following conditions are met:

The publication and/or presentation are solely for informational and non-commercial purposes.
The source of the data and/or image is referred to the HPA site111 www.proteinatlas.org and/or one or more of our publications are cited.

1.3 Installation

hpar is available through the Bioconductor project. Details about the package and the installation procedure can be found on its landing page. To install using the dedicated Bioconductor infrastructure, run :

## install BiocManager only one
install.packages("BiocManager")
## install hpar
BiocManager::install("hpar")

After installation, hpar will have to be explicitly loaded with

library("hpar")

## This is hpar version 1.36.0,
## based on the Human Protein Atlas
##   Version: 20.1
##   Release data: 2021.02.24
##   Ensembl build: 92.38
## See '?hpar' or 'vignette('hpar')' for details.

so that all the package’s functionality and data is available to the user.

2 The hpar package

2.1 Data sets

The data sets described above can be loaded with the data function, as illustrated below for hpaNormalTissue below. Each data set is a data.frame and can be easily manipulated using standard R functionality. The code chunk below illustrates some of its properties.

data(hpaNormalTissue)
dim(hpaNormalTissue)

## [1] 1118517       6

names(hpaNormalTissue)

## [1] "Gene"        "Gene.name"   "Tissue"      "Cell.type"   "Level"      
## [6] "Reliability"

## Number of genes
length(unique(hpaNormalTissue$Gene))

## [1] 15320

## Number of cell types
length(unique(hpaNormalTissue$Cell.type))

## [1] 120

head(levels(hpaNormalTissue$Cell.type))

## NULL

## Number of tissues
length(unique(hpaNormalTissue$Tissue))

## [1] 63

head(levels(hpaNormalTissue$Tissue))

## NULL

2.2 HPA interface

The package provides a interface to the HPA data. The getHpa allows to query the data sets described above. It takes three arguments, id, hpadata and type, that control the query, what data set to interrogate and how to report results respectively. The HPA data uses Ensembl gene identifiers and id must be a valid identifier. hpadata must be one of available dataset. type can be either "data" or "details". The former is the default and returns a data.frame containing the information relevant to id. It is also possible to obtained detailed information, (including cell images) as web pages, directly from the HPA web page, using "details".

We will illustrate this functionality with using the TSPAN6 (tetraspanin 6) gene (ENSG00000000003) as example.

id <- "ENSG00000000003"
head(getHpa(id, hpadata = "hpaNormalTissue"))

##              Gene Gene.name         Tissue           Cell.type        Level
## 1 ENSG00000000003    TSPAN6 adipose tissue          adipocytes Not detected
## 2 ENSG00000000003    TSPAN6  adrenal gland     glandular cells Not detected
## 3 ENSG00000000003    TSPAN6       appendix     glandular cells       Medium
## 4 ENSG00000000003    TSPAN6       appendix     lymphoid tissue Not detected
## 5 ENSG00000000003    TSPAN6    bone marrow hematopoietic cells Not detected
## 6 ENSG00000000003    TSPAN6         breast          adipocytes Not detected
##   Reliability
## 1    Approved
## 2    Approved
## 3    Approved
## 4    Approved
## 5    Approved
## 6    Approved

getHpa(id, hpadata = "hpaSubcellularLoc")

##              Gene Gene.name Reliability          Main.location
## 1 ENSG00000000003    TSPAN6    Approved Cell Junctions;Cytosol
##         Additional.location Extracellular.location Enhanced Supported
## 1 Nucleoli fibrillar center                                          
##                                           Approved Uncertain
## 1 Cell Junctions;Cytosol;Nucleoli fibrillar center          
##   Single.cell.variation.intensity Single.cell.variation.spatial
## 1                         Cytosol                              
##   Cell.cycle.dependency
## 1                      
##                                                                                     GO.id
## 1 Cell Junctions (GO:0030054);Cytosol (GO:0005829);Nucleoli fibrillar center (GO:0001650)

head(getHpa(id, hpadata = "rnaGeneCellLine"))

##              Gene Gene.name Cell.line   TPM  pTPM   NX
## 1 ENSG00000000003    TSPAN6     A-431  27.8  33.9  7.9
## 2 ENSG00000000003    TSPAN6      A549  37.6  45.5 10.6
## 3 ENSG00000000003    TSPAN6      AF22 108.1 134.5 28.7
## 4 ENSG00000000003    TSPAN6    AN3-CA  51.8  64.4 14.5
## 5 ENSG00000000003    TSPAN6  ASC diff  32.3  37.4 12.6
## 6 ENSG00000000003    TSPAN6 ASC TERT1  17.7  20.8  6.8

If we ask for "detail", a browser page pointing to the relevant page is open (see figure below)

getHpa(id, type = "details")

The HPA web page for the tetraspanin 6 gene (ENSG00000000003).

If a user is interested specifically in one data set, it is possible to set hpadata globally and omit it in getHpa. This is done by setting the hpar options hpardata with the setHparOptions function. The current default data set can be tested with getHparOptions.

getHparOptions()

## $hpar
## $hpar$hpadata
## [1] "hpaNormalTissue"

setHparOptions(hpadata = "hpaSubcellularLoc")
getHparOptions()

## $hpar
## $hpar$hpadata
## [1] "hpaSubcellularLoc"

getHpa(id)

##              Gene Gene.name Reliability          Main.location
## 1 ENSG00000000003    TSPAN6    Approved Cell Junctions;Cytosol
##         Additional.location Extracellular.location Enhanced Supported
## 1 Nucleoli fibrillar center                                          
##                                           Approved Uncertain
## 1 Cell Junctions;Cytosol;Nucleoli fibrillar center          
##   Single.cell.variation.intensity Single.cell.variation.spatial
## 1                         Cytosol                              
##   Cell.cycle.dependency
## 1                      
##                                                                                     GO.id
## 1 Cell Junctions (GO:0030054);Cytosol (GO:0005829);Nucleoli fibrillar center (GO:0001650)

2.3 HPA release information

Information about the HPA release used to build the installed

hpar package can be accessed with getHpaVersion, getHpaDate and getHpaEnsembl. Full release details can be found on the HPA release history page.

getHpaVersion()

## version 
##  "20.1"

getHpaDate()

##         date 
## "2021.02.24"

getHpaEnsembl()

## ensembl 
## "92.38"

3 A small use case

Let’s compare the subcellular localisation annotation obtained from the HPA subcellular location data set and the information available in the Bioconductor annotation packages.

id <- "ENSG00000001460"
getHpa(id, "hpaSubcellularLoc")

##              Gene Gene.name Reliability Main.location Additional.location
## 8 ENSG00000001460     STPG1    Approved   Nucleoplasm                    
##   Extracellular.location Enhanced Supported    Approved Uncertain
## 8                                           Nucleoplasm          
##   Single.cell.variation.intensity Single.cell.variation.spatial
## 8                                                              
##   Cell.cycle.dependency                    GO.id
## 8                       Nucleoplasm (GO:0005654)

Below, we first extract all cellular component GO terms available for id from the org.Hs.eg.db human annotation and then retrieve their term definitions using the GO.db database.

library("org.Hs.eg.db")
library("GO.db")
ans <- select(org.Hs.eg.db, keys = id,
              columns = c("ENSEMBL", "GO", "ONTOLOGY"),
              keytype = "ENSEMBL")

## 'select()' returned 1:many mapping between keys and columns

ans <- ans[ans$ONTOLOGY == "CC", ]
ans

##           ENSEMBL         GO EVIDENCE ONTOLOGY
## 2 ENSG00000001460 GO:0005634      IEA       CC
## 3 ENSG00000001460 GO:0005739      IEA       CC

sapply(as.list(GOTERM[ans$GO]), slot, "Term")

##      GO:0005634      GO:0005739 
##       "nucleus" "mitochondrion"

Session information

## R version 4.1.1 (2021-08-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.3 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.14-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.14-bioc/R/lib/libRlapack.so
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB              LC_COLLATE=C              
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
## [1] hpar_1.36.0          GO.db_3.14.0         org.Hs.eg.db_3.14.0 
## [4] AnnotationDbi_1.56.0 IRanges_2.28.0       S4Vectors_0.32.0    
## [7] Biobase_2.54.0       BiocGenerics_0.40.0  BiocStyle_2.22.0    
## 
## loaded via a namespace (and not attached):
##  [1] Rcpp_1.0.7             XVector_0.34.0         GenomeInfoDb_1.30.0   
##  [4] bslib_0.3.1            compiler_4.1.1         BiocManager_1.30.16   
##  [7] jquerylib_0.1.4        zlibbioc_1.40.0        bitops_1.0-7          
## [10] tools_4.1.1            digest_0.6.28          bit_4.0.4             
## [13] jsonlite_1.7.2         evaluate_0.14          RSQLite_2.2.8         
## [16] memoise_2.0.0          pkgconfig_2.0.3        png_0.1-7             
## [19] rlang_0.4.12           DBI_1.1.1              yaml_2.2.1            
## [22] xfun_0.27              fastmap_1.1.0          GenomeInfoDbData_1.2.7
## [25] stringr_1.4.0          httr_1.4.2             knitr_1.36            
## [28] Biostrings_2.62.0      sass_0.4.0             vctrs_0.3.8           
## [31] bit64_4.0.5            R6_2.5.1               rmarkdown_2.11        
## [34] bookdown_0.24          blob_1.2.2             magrittr_2.0.1        
## [37] htmltools_0.5.2        KEGGREST_1.34.0        stringi_1.7.5         
## [40] RCurl_1.98-1.5         cachem_1.0.6           crayon_1.4.1

Uhlén, Mathias, Erik Björling, Charlotta Agaton, Cristina Al-Khalili A. Szigyarto, Bahram Amini, Elisabet Andersen, Ann-Catrin C. Andersson, et al. 2005. “A human protein atlas for normal and cancer tissues based on antibody proteomics.” Molecular & Cellular Proteomics : MCP 4 (12): 1920–32. https://doi.org/10.1074/mcp.M500279-MCP200.

Uhlen, Mathias, Per Oksvold, Linn Fagerberg, Emma Lundberg, Kalle Jonasson, Mattias Forsberg, Martin Zwahlen, et al. 2010. “Towards a knowledge-based Human Protein Atlas.” Nature Biotechnology 28 (12): 1248–50. https://doi.org/10.1038/nbt1210-1248.