Human Protein Atlas in R

Laurent Gatto1

1Computational Proteomics Unit, Cambridge, UK

30 October 2017

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 1.20.0

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 comma-separated file includes Ensembl gene identifier (“Gene”), tissue name (“Tissue”), annotated cell type (“Cell type”), expression value (“Level”), the type of annotation (annotated protein expression (APE), based on more than one antibody, or staining, based on one antibody only) (“Expression type”), and the reliability or validation of the expression value (“Reliability”).}
hpaCancer Cancer tumor data: Staining profiles for proteins in human tumor tissue based on immunohistochemisty using tissue micro arrays. The comma-separated file includes Ensembl gene identifier (“Gene”), tumor name (“Tumor”), staining value (“Level”), the number of patients that stain for this staining value (“Count patients”), the total amount of patients for this tumor type (“Total patients”) and the type of annotation staining (“Expression type”). }
rnaGeneTissue RNA gene data: RNA levels in 45 cell lines and 32 tissues based on RNA-seq. The comma-separated file includes Ensembl gene identifier (“Gene”), analysed sample (“Sample”), fragments per kilobase of transcript per million fragments mapped (“Value” and “Unit”), and abundance class (“Abundance”). }
rnaGeneCellLine RNA gene data: RNA levels in 45 cell lines and 32 tissues based on RNA-seq. The comma-separated file includes Ensembl gene identifier (“Gene”), analysed sample (“Sample”), fragments per kilobase of transcript per million fragments mapped (“Value” and “Unit”), and abundance class (“Abundance”). }
hpaSubcellularLoc Subcellular location data: Subcellular localization of proteins based on immunofluorescently stained cells. The comma-separated file includes Ensembl gene identifier (“Gene”), main subcellular location of the protein (“Main location”), other locations (“Other location”), the type of annotation (annotated protein expression (APE), based on more than one antibody, or staining, based on one antibody only) (“Expression type”), and the reliability or validation of the expression value (“Reliability”). }
hpaSubcellularLoc14: Same as above, for version 14.

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 site11 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 :

source("http://bioconductor.org/biocLite.R")  
## or, if you have already used the above before 
library("BiocInstaller") ## and to install the package 
biocLite("hpar")

After installation, hpar will have to be explicitly loaded with

library("hpar")

## This is hpar version 1.20.0,
## based on the Human Protein Atlas
##   Version: 16.1
##   Release data: 2017.01.31
##   Ensembl build: 83.38
## 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] 1031835       6

names(hpaNormalTissue)

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

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

## [1] 12983

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

## [1] 67

head(levels(hpaNormalTissue$Cell.type))

## [1] "adipocytes"                  "bile duct cells"            
## [3] "cells in anterior"           "cells in cortex/medulla"    
## [5] "cells in cuticle"            "cells in endometrial stroma"

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

## [1] 55

head(levels(hpaNormalTissue$Tissue))

## [1] "adrenal gland" "appendix"      "bone marrow"   "breast"       
## [5] "bronchus"      "caudate"

table(hpaNormalTissue$Expression.type)

## < table of extent 0 >

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 adrenal gland     glandular cells Not detected
## 2 ENSG00000000003    TSPAN6      appendix     glandular cells       Medium
## 3 ENSG00000000003    TSPAN6      appendix     lymphoid tissue Not detected
## 4 ENSG00000000003    TSPAN6   bone marrow hematopoietic cells Not detected
## 5 ENSG00000000003    TSPAN6        breast          adipocytes Not detected
## 6 ENSG00000000003    TSPAN6        breast     glandular cells         High
##   Reliability
## 1    Approved
## 2    Approved
## 3    Approved
## 4    Approved
## 5    Approved
## 6    Approved

getHpa(id, hpadata = "hpaSubcellularLoc")

##              Gene Gene.name Reliability Validated Supported Approved
## 1 ENSG00000000003    TSPAN6    Approved                      Cytosol
##   Uncertain Cell.to.cell.variation.spatial Cell.to.cell.variation.intensity
## 1                                                                          
##   Cell.cycle.dependency                GO.id
## 1                       Cytosol (GO:0005829)

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

##              Gene Gene.name    Sample Value Unit
## 1 ENSG00000000003    TSPAN6     A-431  27.8  TPM
## 2 ENSG00000000003    TSPAN6      A549  37.6  TPM
## 3 ENSG00000000003    TSPAN6      AF22 108.2  TPM
## 4 ENSG00000000003    TSPAN6    AN3-CA  51.8  TPM
## 5 ENSG00000000003    TSPAN6 ASC TERT1  17.8  TPM
## 6 ENSG00000000003    TSPAN6      BEWO  42.8  TPM

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 Validated Supported Approved
## 1 ENSG00000000003    TSPAN6    Approved                      Cytosol
##   Uncertain Cell.to.cell.variation.spatial Cell.to.cell.variation.intensity
## 1                                                                          
##   Cell.cycle.dependency                GO.id
## 1                       Cytosol (GO:0005829)

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 
##  "16.1"

getHpaDate()

##         date 
## "2017.01.31"

getHpaEnsembl()

## ensembl 
## "83.38"

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 Validated Supported    Approved
## 8 ENSG00000001460     STPG1    Approved                     Nucleoplasm
##   Uncertain Cell.to.cell.variation.spatial Cell.to.cell.variation.intensity
## 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
## 1 ENSG00000001460 GO:0005634      IEA       CC
## 2 ENSG00000001460 GO:0005737      IEA       CC

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

##  GO:0005634  GO:0005737 
##   "nucleus" "cytoplasm"

4 Session information}

## R version 3.4.2 (2017-09-28)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.3 LTS
## 
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.6-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.6-bioc/R/lib/libRlapack.so
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        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] parallel  stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
## [1] hpar_1.20.0          GO.db_3.4.2          org.Hs.eg.db_3.4.2  
## [4] AnnotationDbi_1.40.0 IRanges_2.12.0       S4Vectors_0.16.0    
## [7] Biobase_2.38.0       BiocGenerics_0.24.0  BiocStyle_2.6.0     
## 
## loaded via a namespace (and not attached):
##  [1] Rcpp_0.12.13    knitr_1.17      magrittr_1.5    bit_1.1-12     
##  [5] rlang_0.1.2     stringr_1.2.0   blob_1.1.0      tools_3.4.2    
##  [9] DBI_0.7         htmltools_0.3.6 yaml_2.1.14     bit64_0.9-7    
## [13] rprojroot_1.2   digest_0.6.12   tibble_1.3.4    bookdown_0.5   
## [17] memoise_1.1.0   evaluate_0.10.1 RSQLite_2.0     rmarkdown_1.6  
## [21] stringi_1.1.5   compiler_3.4.2  backports_1.1.1 pkgconfig_2.0.1

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). Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.: 1248–50. doi:10.1038/nbt1210-1248.

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). Department of Biotechnology, AlbaNova University Center, Royal Institute of Technology (KTH), SE-106 91 Stockholm, Sweden. [email protected]: 1920–32. doi:10.1074/mcp.M500279-MCP200.