hpar 1.24.0
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
and *16.1
: Same as above, for versions 14
and 16.1.
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 1.24.0,
## based on the Human Protein Atlas
## Version: 18
## Release data: 2017.12.01
## Ensembl build: 88.38
## 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] 1053330 6
names(hpaNormalTissue)
## [1] "Gene" "Gene.name" "Tissue" "Cell.type" "Level"
## [6] "Reliability"
## Number of genes
length(unique(hpaNormalTissue$Gene))
## [1] 13206
## Number of cell types
length(unique(hpaNormalTissue$Cell.type))
## [1] 82
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] 58
head(levels(hpaNormalTissue$Tissue))
## [1] "adrenal gland" "appendix" "bone marrow" "breast"
## [5] "bronchus" "caudate"
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 Enhanced Supported Approved
## 1 ENSG00000000003 TSPAN6 Approved Cytosol
## Uncertain Single.cell.variation.intensity Single.cell.variation.spatial
## 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 108.1 TPM
## 4 ENSG00000000003 TSPAN6 AN3-CA 51.8 TPM
## 5 ENSG00000000003 TSPAN6 ASC diff 32.3 TPM
## 6 ENSG00000000003 TSPAN6 ASC TERT1 17.7 TPM
If we ask for "detail"
, a browser page pointing to the
relevant page is open (see figure below)
getHpa(id, type = "details")
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 Enhanced Supported Approved
## 1 ENSG00000000003 TSPAN6 Approved Cytosol
## Uncertain Single.cell.variation.intensity Single.cell.variation.spatial
## 1
## Cell.cycle.dependency GO.id
## 1 Cytosol (GO:0005829)
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
## "18"
getHpaDate()
## date
## "2017.12.01"
getHpaEnsembl()
## ensembl
## "88.38"
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 Enhanced Supported Approved
## 8 ENSG00000001460 STPG1 Approved Nucleoplasm
## Uncertain 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$ONTOLOGY == "CC", ]
ans
## ENSEMBL GO EVIDENCE ONTOLOGY
## 2 ENSG00000001460 GO:0005622 IMP CC
## 3 ENSG00000001460 GO:0005634 IEA CC
## 4 ENSG00000001460 GO:0005739 IEA CC
sapply(as.list(GOTERM[ans$GO]), slot, "Term")
## GO:0005622 GO:0005634 GO:0005739
## "intracellular" "nucleus" "mitochondrion"
## R version 3.5.1 Patched (2018-07-12 r74967)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.5 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.8-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.8-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_1.24.0 GO.db_3.7.0 org.Hs.eg.db_3.7.0
## [4] AnnotationDbi_1.44.0 IRanges_2.16.0 S4Vectors_0.20.0
## [7] Biobase_2.42.0 BiocGenerics_0.28.0 BiocStyle_2.10.0
##
## loaded via a namespace (and not attached):
## [1] Rcpp_0.12.19 knitr_1.20 magrittr_1.5
## [4] bit_1.1-14 blob_1.1.1 stringr_1.3.1
## [7] tools_3.5.1 xfun_0.4 DBI_1.0.0
## [10] htmltools_0.3.6 yaml_2.2.0 bit64_0.9-7
## [13] rprojroot_1.3-2 digest_0.6.18 bookdown_0.7
## [16] BiocManager_1.30.3 memoise_1.1.0 evaluate_0.12
## [19] RSQLite_2.1.1 rmarkdown_1.10 stringi_1.2.4
## [22] compiler_3.5.1 backports_1.1.2 pkgconfig_2.0.2
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. 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, 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. mathias.uhlen@biotech.kth.se:1920–32. https://doi.org/10.1074/mcp.M500279-MCP200.