Human Protein Atlas in R (2024)

LaurentGatto

de Duve Institute, UCLouvain, Belgium

hpar.Rmd

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.

Introduction

The HPA project

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

The Swedish Human Protein Atlas project, funded by the Knut and AliceWallenberg Foundation, has been set up to allow for a systematicexploration of the human proteome using Antibody-Based Proteomics. Thisis accomplished by combining high-throughput generation ofaffinity-purified antibodies with protein profiling in a multitude oftissues and cells assembled in tissue microarrays. Confocal microscopyanalysis using human cell lines is performed for more detailed proteinlocalisation. The program hosts the Human Protein Atlas portal withexpression profiles of human proteins in tissues and cells.

The hpar packageprovides access to HPA data from the R interface. It also distributesthe following data sets:

Several flat files are distributed by the HPA project and availablewithin the package as data.frames, other datasets are available througha search query on the HPA website. The description below is taken fromthe HPA site:

hpaNormalTissue: Normal tissue data. Expression profiles forproteins in human tissues based on immunohistochemisty using tissuemicro 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 expressionvalue (“Reliability”).
hpaNormalTissue16.1: Same as above, for version 16.1.
hpaCancer: Pathology data. Staining profiles for proteins inhuman tumor tissue based on immunohistochemisty using tissue microarrays and log-rank P value for Kaplan-Meier analysis of correlationbetween mRNA expression level and patient survival. The tab-separatedfile includes Ensembl gene identifier (“Gene”), gene name (“Gene name”),tumor name (“Cancer”), the number of patients annotated for differentstaining levels (“High”, “Medium”, “Low” & “Not detected”) andlog-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
rnaConsensusTissue: RNA consensus tissue gene data. Consensustranscript expression levels summarized per gene in 54 tissues based ontranscriptomics data from HPA and GTEx. The consensus normalizedexpression (“nTPM”) value is calculated as the maximum nTPM value foreach gene in the two data sources. For tissues with multiple sub-tissues(brain regions, lymphoid tissues and intestine) the maximum of allsub-tissues is used for the tissue type. The tab-separated file includesEnsembl gene identifier (“Gene”), analysed sample (“Tissue”) andnormalized expression (“nTPM”).
rnaHpaTissue: RNA HPA tissue gene data. Transcript expressionlevels summarized per gene in 256 tissues based on RNA-seq. Thetab-separated file includes Ensembl gene identifier (“Gene”), analysedsample (“Tissue”), transcripts per million (“TPM”), protein-transcriptsper million (“pTPM”) and normalized expression (“nTPM”).
rnaGtexTissue: RNA GTEx tissue gene data. Transcript expressionlevels summarized per gene in 37 tissues based on RNA-seq. Thetab-separated file includes Ensembl gene identifier (“Gene”), analysedsample (“Tissue”), transcripts per million (“TPM”), protein-transcriptsper million (“pTPM”) and normalized expression (“nTPM”). The data wasobtained from GTEx.
rnaFantomTissue: RNA FANTOM tissue gene data. Transcriptexpression levels summarized per gene in 60 tissues based on CAGE data.The tab-separated file includes Ensembl gene identifier (“Gene”),analysed sample (“Tissue”), tags per million (“Tags per million”),scaled-tags per million (“Scaled tags per million”) and normalizedexpression (“nTPM”). The data was obtained from FANTOM5.
See Also
Human Protein Atlas in R
rnaGeneTissue21.0: RNA HPA tissue gene data. Transcriptexpression levels summarized per gene in 37 tissues based on RNA-seq,for hpa version 21.0. The tab-separated file includes Ensembl geneidentifier (“Gene”), analysed sample (“Tissue”), transcripts per million(“TPM”), protein-transcripts per million (“pTPM”).
rnaGeneCellLine: RNA HPA cell line gene data. Transcriptexpression levels summarized per gene in 69 cell lines. Thetab-separated file includes Ensembl gene identifier (“Gene”), analysedsample (“Cell line”), transcripts per million (“TPM”), protein-codingtranscripts per million (“pTPM”) and normalized expression(“nTPM”).
rnaGeneCellLine16.1: Same as above, for version 16.1.
hpaSubcellularLoc: Subcellular location data. Subcellularlocation of proteins based on immunofluorescently stained cells. Thetab-separated file includes the following columns: Ensembl geneidentifier (“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-cellvariation (“Single-cell variation spatial”), locations with observedcell cycle dependency (type can be one or more of biological definition,custom data or correlation) (“Cell cycle dependency”), Gene OntologyCellular Component term identifier (“GO id”).
hpaSubcellularLoc16.1: Same as above, for version 16.1.
hpaSubcellularLoc14: Same as above, for version 14.
hpaSecretome: Secretome data. The human secretome is here definedas all Ensembl genes with at least one predicted secreted transcriptaccording to HPA predictions. The complete information about the HPASecretome data is given on https://www.proteinatlas.org/humanproteome/blood/secretome.This dataset has 315 columns and includes the Ensembl gene identifier(“Gene”). Information about the additionnal variables can be found here by clicking onShow/hide columns.

The hpar::allHparData() returns a list of all datasets(see below).

HPA data usage policy

The use of data and images from the HPA in publications andpresentations is permitted provided that the following conditions aremet:

The publication and/or presentation are solely for informational andnon-commercial purposes.
The source of the data and/or image is referred to the HPA site1 and/or oneor more of our publications are cited.

Installation

hpar isavailable through the Bioconductor project. Details about the packageand the installation procedure can be found on its landingpage. To install using the dedicated Bioconductor infrastructure,run :

## install BiocManager only oneinstall.packages("BiocManager")## install hparBiocManager::install("hpar")

After installation, hpar willhave to be explicitly loaded with

library("hpar")

## snapshotDate(): 2022-11-29

## This is hpar version 1.41.1,## based on the Human Protein Atlas## Version: 21.1## Release data: 2022.05.31## Ensembl build: 103.38## See '?hpar' or 'vignette('hpar')' for details.

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

The hparpackage

Data sets

A table descibing all dataset available in the package can beaccessed with the allHparData() function.

hpa_data <- allHparData()

The Title variable corresponds to names of the data that canbe downloaded localled and cached as part of the ExperimentHubinfrastructure.

head(normtissue <- hpaNormalTissue())

## see ?hpar and browseVignettes('hpar') for documentation

## loading from cache

## 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

Note that given that the hpa data is distributed as parthe ExperimentHub infrastructure, it is also possible to query itdirectly for relevant datasets.

library("ExperimentHub")eh <- ExperimentHub()query(eh, "hpar")

## ExperimentHub with 15 records## # snapshotDate(): 2022-11-29## # $dataprovider: Human Protein Atlas## # $species: hom*o sapiens## # $rdataclass: data.frame## # additional mcols(): taxonomyid, genome, description,## # coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,## # rdatapath, sourceurl, sourcetype ## # retrieve records with, e.g., 'object[["EH7765"]]' ## ## title ## EH7765 | hpaCancer16.1 ## EH7766 | hpaCancer ## EH7767 | hpaNormalTissue16.1## EH7768 | hpaNormalTissue ## EH7769 | hpaSecretome ## ... ... ## EH7775 | rnaGeneCellLine16.1## EH7776 | rnaGeneCellLine ## EH7777 | rnaGeneTissue21.0 ## EH7778 | rnaGtexTissue ## EH7779 | rnaHpaTissue

HPA interface

Each data described above is a data.frame and can beeasily manipulated using standard R orBiocStyle::CRANpkg("tidyverse") tidyversefunctionality.

names(normtissue)

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

## Number of geneslength(unique(normtissue$Gene))

## [1] 15323

## Number of cell typeslength(unique(normtissue$Cell.type))

## [1] 141

## Number of tissueslength(unique(normtissue$Tissue))

## [1] 63

## Number of genes highlighly and reliably expressed in each cell type## in each tissue.library("dplyr")normtissue |> filter(Reliability == "Approved", Level == "High") |> count(Cell.type, Tissue) |> arrange(desc(n)) |> head()

## Cell.type Tissue n## 1 glandular cells gallbladder 1578## 2 glandular cells duodenum 1551## 3 glandular cells small intestine 1532## 4 glandular cells rectum 1490## 5 glandular cells colon 1404## 6 cells in tubules kidney 1357

We will illustrate additional datasets using the TSPAN6 (tetraspanin6) gene (ENSG00000000003) as example.

id <- "ENSG00000000003"subcell <- hpaSubcellularLoc()

## see ?hpar and browseVignettes('hpar') for documentation

## loading from cache

rna <- rnaGeneCellLine()

## see ?hpar and browseVignettes('hpar') for documentation## loading from cache

## Compine protein immunohistochemisty data, with the subcellular## location and RNA expression levels.filter(normtissue, Gene == id) |> full_join(filter(subcell, Gene == id)) |> full_join(filter(rna, Gene == id)) |> head()

## Joining, by = c("Gene", "Gene.name", "Reliability")

## Joining, by = c("Gene", "Gene.name")

## Gene Gene.name Tissue Cell.type Level Reliability## 1 ENSG00000000003 TSPAN6 adipose tissue adipocytes Not detected Approved## 2 ENSG00000000003 TSPAN6 adipose tissue adipocytes Not detected Approved## 3 ENSG00000000003 TSPAN6 adipose tissue adipocytes Not detected Approved## 4 ENSG00000000003 TSPAN6 adipose tissue adipocytes Not detected Approved## 5 ENSG00000000003 TSPAN6 adipose tissue adipocytes Not detected Approved## 6 ENSG00000000003 TSPAN6 adipose tissue adipocytes Not detected Approved## Main.location Additional.location Extracellular.location## 1 Cell Junctions;Cytosol Nucleoli fibrillar center ## 2 Cell Junctions;Cytosol Nucleoli fibrillar center ## 3 Cell Junctions;Cytosol Nucleoli fibrillar center ## 4 Cell Junctions;Cytosol Nucleoli fibrillar center ## 5 Cell Junctions;Cytosol Nucleoli fibrillar center ## 6 Cell Junctions;Cytosol Nucleoli fibrillar center ## Enhanced Supported Approved Uncertain## 1 Cell Junctions;Cytosol;Nucleoli fibrillar center ## 2 Cell Junctions;Cytosol;Nucleoli fibrillar center ## 3 Cell Junctions;Cytosol;Nucleoli fibrillar center ## 4 Cell Junctions;Cytosol;Nucleoli fibrillar center ## 5 Cell Junctions;Cytosol;Nucleoli fibrillar center ## 6 Cell Junctions;Cytosol;Nucleoli fibrillar center ## Single.cell.variation.intensity Single.cell.variation.spatial## 1 Cytosol ## 2 Cytosol ## 3 Cytosol ## 4 Cytosol ## 5 Cytosol ## 6 Cytosol ## Cell.cycle.dependency## 1 ## 2 ## 3 ## 4 ## 5 ## 6 ## GO.id## 1 Cell Junctions (GO:0030054);Cytosol (GO:0005829);Nucleoli fibrillar center (GO:0001650)## 2 Cell Junctions (GO:0030054);Cytosol (GO:0005829);Nucleoli fibrillar center (GO:0001650)## 3 Cell Junctions (GO:0030054);Cytosol (GO:0005829);Nucleoli fibrillar center (GO:0001650)## 4 Cell Junctions (GO:0030054);Cytosol (GO:0005829);Nucleoli fibrillar center (GO:0001650)## 5 Cell Junctions (GO:0030054);Cytosol (GO:0005829);Nucleoli fibrillar center (GO:0001650)## 6 Cell Junctions (GO:0030054);Cytosol (GO:0005829);Nucleoli fibrillar center (GO:0001650)## Cell.line TPM pTPM nTPM## 1 A-431 21.3 25.6 24.8## 2 A549 20.5 24.4 23.0## 3 AF22 78.8 96.9 80.6## 4 AN3-CA 38.7 47.1 42.2## 5 ASC diff 19.2 22.1 28.7## 6 ASC TERT1 11.3 13.1 16.6

It is also possible to directly open the HPA page for a specific gene(see figure below).

browseHPA(id)

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

HPA release information

Information about the HPA release used to build the installed hpar packagecan be accessed with getHpaVersion, getHpaDateand getHpaEnsembl. Full release details can be found on theHPA releasehistory page.

getHpaVersion()

## version ## "21.1"

getHpaDate()

## date ## "2022.05.31"

getHpaEnsembl()

## ensembl ## "103.38"

A small use case

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

id <- "ENSG00000001460"filter(subcell, Gene == id)

## Gene Gene.name Reliability Main.location Additional.location## 1 ENSG00000001460 STPG1 Approved Nucleoplasm ## Extracellular.location Enhanced Supported Approved Uncertain## 1 Nucleoplasm ## Single.cell.variation.intensity Single.cell.variation.spatial## 1 ## Cell.cycle.dependency GO.id## 1 Nucleoplasm (GO:0005654)

Below, we first extract all cellular component GO terms available forid from the org.Hs.eg.dbhuman annotation and then retrieve their term definitions using theGO.dbdatabase.

library("org.Hs.eg.db")library("GO.db")ans <- AnnotationDbi::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 Under development (unstable) (2022-11-23 r83380)## Platform: x86_64-pc-linux-gnu (64-bit)## Running under: Ubuntu 22.04.1 LTS## ## Matrix products: default## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so## ## locale:## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C ## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 ## [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 ## ## time zone: UTC## tzcode source: system (glibc)## ## attached base packages:## [1] stats4 stats graphics grDevices utils datasets methods ## [8] base ## ## other attached packages:## [1] ExperimentHub_2.7.0 AnnotationHub_3.7.0 BiocFileCache_2.7.0 ## [4] dbplyr_2.2.1 hpar_1.41.1 GO.db_3.16.0 ## [7] org.Hs.eg.db_3.16.0 AnnotationDbi_1.61.0 IRanges_2.33.0 ## [10] S4Vectors_0.37.0 Biobase_2.59.0 BiocGenerics_0.45.0 ## [13] dplyr_1.0.10 BiocStyle_2.27.0 ## ## loaded via a namespace (and not attached):## [1] tidyselect_1.2.0 blob_1.2.3 ## [3] filelock_1.0.2 Biostrings_2.67.0 ## [5] bitops_1.0-7 fastmap_1.1.0 ## [7] RCurl_1.98-1.9 promises_1.2.0.1 ## [9] digest_0.6.30 mime_0.12 ## [11] lifecycle_1.0.3 ellipsis_0.3.2 ## [13] KEGGREST_1.39.0 interactiveDisplayBase_1.37.0## [15] RSQLite_2.2.19 magrittr_2.0.3 ## [17] compiler_4.3.0 rlang_1.0.6 ## [19] sass_0.4.4 tools_4.3.0 ## [21] utf8_1.2.2 yaml_2.3.6 ## [23] knitr_1.41 htmlwidgets_1.5.4 ## [25] bit_4.0.5 curl_4.3.3 ## [27] withr_2.5.0 purrr_0.3.5 ## [29] desc_1.4.2 fansi_1.0.3 ## [31] xtable_1.8-4 cli_3.4.1 ## [33] rmarkdown_2.18 crayon_1.5.2 ## [35] ragg_1.2.4 generics_0.1.3 ## [37] httr_1.4.4 DBI_1.1.3 ## [39] cachem_1.0.6 stringr_1.4.1 ## [41] zlibbioc_1.45.0 assertthat_0.2.1 ## [43] BiocManager_1.30.19 XVector_0.39.0 ## [45] vctrs_0.5.1 jsonlite_1.8.3 ## [47] bookdown_0.30 bit64_4.0.5 ## [49] systemfonts_1.0.4 crosstalk_1.2.0 ## [51] jquerylib_0.1.4 glue_1.6.2 ## [53] pkgdown_2.0.6.9000 DT_0.26 ## [55] stringi_1.7.8 BiocVersion_3.17.1 ## [57] later_1.3.0 GenomeInfoDb_1.35.5 ## [59] tibble_3.1.8 pillar_1.8.1 ## [61] rappdirs_0.3.3 htmltools_0.5.3 ## [63] GenomeInfoDbData_1.2.9 R6_2.5.1 ## [65] textshaping_0.3.6 rprojroot_2.0.3 ## [67] evaluate_0.18 shiny_1.7.3 ## [69] png_0.1-8 memoise_2.0.1 ## [71] httpuv_1.6.6 bslib_0.4.1 ## [73] Rcpp_1.0.9 xfun_0.35 ## [75] fs_1.5.2 pkgconfig_2.0.3

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

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

www.proteinatlas.org↩︎