## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(
collapse=TRUE,
comment="#>",
warning=FALSE,
message=FALSE,
error=FALSE,
crop = NULL
)
## ----library------------------------------------------------------------------
library(BiocStyle)
library(HPAanalyze)
library(tibble)
library(dplyr)
library(ggplot2)
## ----echo=FALSE, fig.cap="HPAanalyze workflow.", out.width = '100%'-----------
knitr::include_graphics("figures/workflow.png")
## ----downloadedData, eval=FALSE-----------------------------------------------
# # This should give you the latest of everything, but unless you have a lot of RAM and processing power, I would not recomend it.
# downloadedData <- hpaDownload(downloadList='all')
# summary(downloadedData)
#
# #> Length Class Mode
# #> normal_tissue 6 tbl_df list
# #> pathology 11 tbl_df list
# #> subcellular_location 14 tbl_df list
# #> ...
## ----histology----------------------------------------------------------------
downloadedData <- hpaDownload(downloadList='histology', version='example')
# version = "example" will load the built-in dataset. That's sufficient for normal usage, and save you some time.
## ----normal_tissue------------------------------------------------------------
tibble::glimpse(downloadedData$normal_tissue, give.attr=FALSE)
## ----pathology----------------------------------------------------------------
tibble::glimpse(downloadedData$pathology, give.attr=FALSE)
## ----subcellular_location-----------------------------------------------------
tibble::glimpse(downloadedData$subcellular_location, give.attr=FALSE)
## ----rna-help, eval = FALSE---------------------------------------------------
# ?hpaDownload
## ----rna1, warning=FALSE, message=FALSE, eval = FALSE-------------------------
# downloadedData <- hpaDownload(downloadList='rna tissue')
#
# tibble::glimpse(downloadedData, give.attr=FALSE)
#
# #> List of 4
# #> $ rna_tissue_consensus: tibble [1,180,464 x 4] (S3: tbl_df/tbl/data.frame)
# #> ..$ ensembl: chr [1:1180464] "ENSG00000000003" "ENSG00000000003" "ENSG00000000003" "ENSG00000000003" ...
# #> ..$ gene : chr [1:1180464] "TSPAN6" "TSPAN6" "TSPAN6" "TSPAN6" ...
# #> ..$ tissue : chr [1:1180464] "adipose tissue" "adrenal gland" "amygdala" "appendix" ...
# #> ..$ nx : chr [1:1180464] "27.0" "9.8" "7.0" "4.4" ...
# #> $ rna_tissue_hpa : tibble [845,810 x 6] (S3: tbl_df/tbl/data.frame)
# #> ..$ ensembl: chr [1:845810] "ENSG00000000003" "ENSG00000000003" "ENSG00000000003" "ENSG00000000003" ...
# #> ..$ gene : chr [1:845810] "TSPAN6" "TSPAN6" "TSPAN6" "TSPAN6" ...
# #> ..$ tissue : chr [1:845810] "adipose tissue" "adrenal gland" "appendix" "B-cells" ...
# #> ..$ tpm : chr [1:845810] "31.5" "26.4" "9.2" "0.1" ...
# #> ..$ ptpm : chr [1:845810] "37.7" "32.7" "14.5" "0.2" ...
# #> ..$ nx : chr [1:845810] "9.8" "7.6" "2.1" "0.3" ...
# #> $ rna_tissue_gtex : tibble [639,744 x 6] (S3: tbl_df/tbl/data.frame)
# #> ..$ ensembl: chr [1:639744] "ENSG00000000003" "ENSG00000000003" "ENSG00000000003" "ENSG00000000003" ...
# #> ..$ gene : chr [1:639744] "TSPAN6" "TSPAN6" "TSPAN6" "TSPAN6" ...
# #> ..$ tissue : chr [1:639744] "adipose tissue" "adrenal gland" "amygdala" "basal ganglia" ...
# #> ..$ tpm : chr [1:639744] "27.4" "15.5" "7.3" "7.7" ...
# #> ..$ ptpm : chr [1:639744] "34.2" "18.8" "9.0" "9.4" ...
# #> ..$ nx : chr [1:639744] "13.4" "9.8" "7.0" "6.6" ...
# #> $ rna_tissue_fantom : tibble [797,265 x 6] (S3: tbl_df/tbl/data.frame)
# #> ..$ ensembl : chr [1:797265] "ENSG00000000003" "ENSG00000000003" "ENSG00000000003" "ENSG00000000003" ...
# #> ..$ gene : chr [1:797265] "TSPAN6" "TSPAN6" "TSPAN6" "TSPAN6" ...
# #> ..$ tissue : chr [1:797265] "adipose tissue" "amygdala" "appendix" "basal ganglia" ...
# #> ..$ tags_per_million : chr [1:797265] "60.1" "10.7" "13.5" "16.2" ...
# #> ..$ scaled_tags_per_million: chr [1:797265] "87.4" "12.4" "17.8" "18.0" ...
# #> ..$ nx : chr [1:797265] "27.0" "3.5" "4.4" "5.5" ...
## ----list_param, eval=FALSE---------------------------------------------------
# ## If you use the output from hpaDownload()
# downloadedData <- hpaDownload(downloadList=c("Normal tissue", "Pathology", "RNA HPA tissue", "RNA HPA cell line"))
# str(hpaListParam(downloadedData))
#
# # List of 4
# # $ normal_tissue :List of 2
# # ..$ tissue : chr [1:63] "adipose tissue" "adrenal gland" "appendix" "bone marrow" ...
# # ..$ cell_type: chr [1:120] "adipocytes" "glandular cells" "lymphoid tissue" "hematopoietic cells" ...
# # $ pathology :List of 1
# # ..$ cancer: chr [1:20] "breast cancer" "carcinoid" "cervical cancer" "colorectal cancer" ...
# # $ rna_tissue_hpa:List of 1
# # ..$ tissue: chr [1:43] "adipose tissue" "adrenal gland" "appendix" "B-cells" ...
# # $ rna_celline :List of 1
# # ..$ cell_line: chr [1:69] "A-431" "A549" "AF22" "AN3-CA" ...
## ----subset1, message=FALSE, warning=FALSE------------------------------------
downloadedData <- hpaDownload(downloadList='histology', version='example')
sapply(downloadedData, nrow)
## ----subset2, message=FALSE, warning=FALSE------------------------------------
geneList <- c('TP53', 'EGFR', 'CD44', 'PTEN', 'IDH1', 'IDH2', 'CYCS')
tissueList <- c('breast', 'cerebellum', 'skin 1')
cancerList <- c('breast cancer', 'glioma', 'melanoma')
cellLineList <- c('A-431', 'A549', 'AF22', 'AN3-CA')
subsetData <- hpaSubset(data=downloadedData,
targetGene=geneList,
targetTissue=tissueList,
targetCancer=cancerList,
targetCellLine=cellLineList)
sapply(subsetData, nrow)
## ----eval=FALSE---------------------------------------------------------------
# hpaExport(subsetData, fileName='subset.xlsx', fileType='xlsx')
## ----visData, echo=FALSE, warning=FALSE, message=FALSE------------------------
downloadedData <- hpaDownload('histology', 'example')
## ----hpaVis_eg----------------------------------------------------------------
hpaVis(downloadedData,
targetGene = c("GCH1", "PTS", "SPR", "DHFR"),
targetTissue = c("cerebellum", "cerebral cortex", "hippocampus"),
targetCancer = c("glioma"))
## ----visTissue----------------------------------------------------------------
geneList <- c('TP53', 'EGFR', 'CD44', 'PTEN', 'IDH1', 'IDH2', 'CYCS')
tissueList <- c('breast', 'cerebellum', 'skin 1')
hpaVisTissue(downloadedData,
targetGene=geneList,
targetTissue=tissueList)
## ----visPatho-----------------------------------------------------------------
geneList <- c('TP53', 'EGFR', 'CD44', 'PTEN', 'IDH1', 'IDH2', 'CYCS')
cancerList <- c('breast cancer', 'glioma', 'lymphoma', 'prostate cancer')
colorGray <- c('slategray1', 'slategray2', 'slategray3', 'slategray4')
hpaVisPatho(downloadedData,
targetGene=geneList,
targetCancer=cancerList,
color=colorGray)
## ----visSubcell---------------------------------------------------------------
geneList <- c('TP53', 'EGFR', 'CD44', 'PTEN', 'IDH1', 'IDH2', 'CYCS')
hpaVisSubcell(downloadedData,
targetGene=geneList,
customTheme=TRUE) +
ggplot2::theme_minimal() +
ggplot2::ylab('Subcellular locations') +
ggplot2::xlab('Protein') +
ggplot2::theme(axis.text.x=element_text(angle=45, hjust=1)) +
ggplot2::theme(legend.position="none") +
ggplot2::coord_equal()
## ----eval=FALSE---------------------------------------------------------------
# EGFR <- hpaXml(inputXml='ENSG00000146648')
# names(EGFR)
#
# #> [1] "ProtClass" "TissueExprSum" "Antibody" "TissueExpr"
## ----XmlGet, eval=FALSE-------------------------------------------------------
# EGFRxml <- hpaXmlGet('ENSG00000146648')
## ----XmlProtClass, eval=FALSE-------------------------------------------------
# hpaXmlProtClass(EGFRxml)
#
# #> # A tibble: 40 x 4
# #> id name parent_id source
# #> <chr> <chr> <chr> <chr>
# #> 1 Ez Enzymes <NA> <NA>
# #> 2 Ec ENZYME proteins Ez ENZYME
# #> 3 Et Transferases Ec ENZYME
# #> 4 Ki Kinases Ez UniProt
# #> 5 Kt Tyr protein kinases Ki UniProt
# #> 6 Ma Predicted membrane proteins <NA> MDM
# #> 7 Md Membrane proteins predicted by MDM <NA> MDM
# #> 8 Me MEMSAT3 predicted membrane proteins <NA> MEMSAT3
# #> 9 Mf MEMSAT-SVM predicted membrane proteins <NA> MEMSAT-SVM
# #> 10 Mg Phobius predicted membrane proteins <NA> Phobius
# #> # ... with 30 more rows
## ----XmlTissueExprSum, eval=FALSE---------------------------------------------
# hpaXmlTissueExprSum(EGFRxml)
#
# #> $summary
# #> [1] "Cytoplasmic and membranous expression in several tissues, most abundant in placenta."
# #>
# #> $img
# #> tissue
# #> 1 cerebral cortex
# #> 2 lymph node
# #> 3 liver
# #> 4 colon
# #> 5 kidney
# #> 6 testis
# #> 7 placenta
# #> imageUrl
# #> 1 http://v18.proteinatlas.org/images/18530/41191_B_7_5_rna_selected.jpg
# #> 2 http://v18.proteinatlas.org/images/18530/41191_A_7_8_rna_selected.jpg
# #> 3 http://v18.proteinatlas.org/images/18530/41191_A_7_4_rna_selected.jpg
# #> 4 http://v18.proteinatlas.org/images/18530/41191_A_9_3_rna_selected.jpg
# #> 5 http://v18.proteinatlas.org/images/18530/41191_A_9_5_rna_selected.jpg
# #> 6 http://v18.proteinatlas.org/images/18530/41191_A_6_6_rna_selected.jpg
# #> 7 http://v18.proteinatlas.org/images/18530/41191_A_1_7_rna_selected.jpg
## ----XmlAntibody, eval=FALSE--------------------------------------------------
# hpaXmlAntibody(EGFRxml)
#
# #> # A tibble: 5 x 4
# #> id releaseDate releaseVersion RRID
# #> <chr> <chr> <chr> <chr>
# #> 1 CAB000035 2006-03-13 1.2 <NA>
# #> 2 HPA001200 2008-02-15 3.1 AB_1078723
# #> 3 HPA018530 2008-12-03 4.1 AB_1848044
# #> 4 CAB068186 2014-11-06 13 AB_2665679
# #> 5 CAB073534 2015-10-16 14 <NA>
## ----XmlTissueExpr1, eval = FALSE---------------------------------------------
# tissueExpression <- hpaXmlTissueExpr(EGFRxml)
# summary(tissueExpression)
#
# #> Length Class Mode
# #> [1,] 18 tbl_df list
# #> [2,] 18 tbl_df list
# #> [3,] 18 tbl_df list
# #> [4,] 18 tbl_df list
# #> [5,] 18 tbl_df list
## ----XmlTissueExpr2, eval = FALSE---------------------------------------------
# tissueExpression[[1]]
#
# #> # A tibble: 327 x 18
# #> patientId age sex staining intensity quantity location imageUrl
# #> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
# #> 1 1653 53 Male <NA> <NA> <NA> <NA> http://~
# #> 2 1721 60 Fema~ <NA> <NA> <NA> <NA> http://~
# #> 3 1725 57 Male <NA> <NA> <NA> <NA> http://~
# #> 4 4 25 Male <NA> <NA> <NA> <NA> http://~
# #> 5 512 34 Fema~ <NA> <NA> <NA> <NA> http://~
# #> 6 2664 74 Fema~ <NA> <NA> <NA> <NA> http://~
# #> 7 2665 88 Fema~ <NA> <NA> <NA> <NA> http://~
# #> 8 1391 54 Fema~ <NA> <NA> <NA> <NA> http://~
# #> 9 1447 45 Fema~ <NA> <NA> <NA> <NA> http://~
# #> 10 1452 44 Fema~ <NA> <NA> <NA> <NA> http://~
# #> # ... with 317 more rows, and 10 more variables: snomedCode1 <chr>,
# #> # snomedCode2 <chr>, snomedCode3 <chr>, snomedCode4 <chr>,
# #> # snomedCode5 <chr>, tissueDescription1 <chr>, tissueDescription2 <chr>,
# #> # tissueDescription3 <chr>, tissueDescription4 <chr>,
# #> # tissueDescription5 <chr>