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.
hpar 1.32.1
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
.
The use of data and images from the HPA in publications and presentations is permitted provided that the following conditions are met:
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 1.32.1,
## based on the Human Protein Atlas
## Version: 19.3
## Release data: 2020.03.06
## 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.
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] 1056061 6names(hpaNormalTissue)## [1] "Gene" "Gene.name" "Tissue" "Cell.type" "Level"
## [6] "Reliability"## Number of genes
length(unique(hpaNormalTissue$Gene))## [1] 15313## Number of cell types
length(unique(hpaNormalTissue$Cell.type))## [1] 84head(levels(hpaNormalTissue$Cell.type))## NULL## Number of tissues
length(unique(hpaNormalTissue$Tissue))## [1] 63head(levels(hpaNormalTissue$Tissue))## NULLThe 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 ApprovedgetHpa(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 7.8
## 2 ENSG00000000003 TSPAN6 A549 37.6 45.5 10.5
## 3 ENSG00000000003 TSPAN6 AF22 108.1 134.5 28.3
## 4 ENSG00000000003 TSPAN6 AN3-CA 51.8 64.4 14.3
## 5 ENSG00000000003 TSPAN6 ASC diff 32.3 37.4 12.4
## 6 ENSG00000000003 TSPAN6 ASC TERT1 17.7 20.8 6.7If we ask for "detail", a browser page pointing to the
relevant page is open (see figure below)
getHpa(id, type = "details")