Nothing
inst/doc/b_HPAanalyze_indepth.R
In HPAanalyze: Retrieve and analyze data from the Human Protein Atlas
## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(
collapse=TRUE,
comment="#>",
warning=FALSE,
message=FALSE,
error=FALSE,
crop = NULL
)
## ----library------------------------------------------------------------------
library(BiocStyle)
library(HPAanalyze)
library(tibble)
library(dplyr)
library(ggplot2)
## ----echo=FALSE, fig.cap="HPAanalyze workflow.", out.width = '100%'-----------
knitr::include_graphics("figures/workflow.png")
## ----downloadedData, eval=FALSE-----------------------------------------------
# # this gives you the latest everything, which is nice to keep but not really necessary
# downloadedData <- hpaDownload(downloadList='all')
# summary(downloadedData)
#
# #> Length Class Mode
# #> normal_tissue 6 tbl_df list
# #> pathology 11 tbl_df list
# #> subcellular_location 11 tbl_df list
# #> rna_tissue 5 tbl_df list
# #> rna_cell_line 5 tbl_df list
# #> transcript_rna_tissue 4 tbl_df list
# #> transcript_rna_cell_line 4 tbl_df list
## ----histology----------------------------------------------------------------
downloadedData <- hpaDownload(downloadList='histology', version='example')
# version = "example" will load the HPA v18 datasets came with this package. That's sufficient for normal usage, and save you some time.
## ----normal_tissue------------------------------------------------------------
tibble::glimpse(downloadedData$normal_tissue, give.attr=FALSE)
## ----pathology----------------------------------------------------------------
tibble::glimpse(downloadedData$pathology, give.attr=FALSE)
## ----subcellular_location-----------------------------------------------------
tibble::glimpse(downloadedData$subcellular_location, give.attr=FALSE)
## ----rna1, warning=FALSE, message=FALSE---------------------------------------
downloadedData <- hpaDownload(downloadList='rna', version='v18')
tibble::glimpse(downloadedData$rna_tissue, give.attr=FALSE)
## ----rna2, warning=FALSE, message=FALSE---------------------------------------
tibble::glimpse(downloadedData$rna_cell_line, give.attr=FALSE)
## ----isoform1, eval=FALSE-----------------------------------------------------
# downloadedData <- hpaDownload(downloadList='isoform', version='v18')
# # version = "v18" is an example of how you may download different versions of the HPA datasets. Just change the number. Note that not all versions are available from the HPA website.
#
# tibble::glimpse(downloadedData$transcript_rna_tissue, give.attr=FALSE)
#
# #> Observations: 27,535,996
# #> Variables: 4
# #> $ ensembl <chr> "ENSG00000000003", "ENSG00000000003", "ENSG0000000...
# #> $ transcript <chr> "ENST00000373020", "ENST00000494424", "ENST0000049...
# #> $ tissue <chr> "adipose tissue.V1", "adipose tissue.V1", "adipose...
# #> $ value <dbl> 27.3577003, 0.0000000, 1.9341500, 1.6059300, 0.000...
## ----isoform2, eval=FALSE-----------------------------------------------------
# tibble::glimpse(downloadedData$transcript_rna_cell_line, give.attr=FALSE)
#
# #> Observations: 20,972,183
# #> Variables: 4
# #> $ ensembl <chr> "ENSG00000000003", "ENSG00000000003", "ENSG0000000...
# #> $ transcript <chr> "ENST00000373020", "ENST00000494424", "ENST0000049...
# #> $ cell_line <chr> "A-431.C35", "A-431.C35", "A-431.C35", "A-431.C35"...
# #> $ value <dbl> 29.406799, 0.000000, 0.992916, 0.398387, 0.239204,...
## ----list_param, eval=FALSE---------------------------------------------------
# ## If you use the output from hpaDownload()
# downloadedData <- hpaDownload(downloadList='all')
# str(hpaListParam(downloadedData))
#
# #> List of 6
# #> $ normal_tissue : chr [1:58] "adrenal gland" "appendix" "bone marrow" "breast" ...
# #> $ normal_cell : chr [1:82] "glandular cells" "lymphoid tissue" "hematopoietic cells" "adipocytes" ...
# #> $ cancer : chr [1:20] "breast cancer" "carcinoid" "cervical cancer" "colorectal cancer" ...
# #> $ subcellular_location: chr [1:32] "Cytosol" "Mitochondria" "Aggresome" "Plasma membrane" ...
# #> $ normal_tissue_rna : chr [1:37] "adipose tissue" "adrenal gland" "appendix" "bone marrow" ...
# #> $ cell_line_rna : chr [1:64] "A-431" "A549" "AF22" "AN3-CA" ...
## ----list_param_2-------------------------------------------------------------
## If you use leave the argument blank
str(hpaListParam())
## ----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(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>
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HPAanalyze documentation built on Nov. 26, 2020, 2:01 a.m.
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## ----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 gives you the latest everything, which is nice to keep but not really necessary
# downloadedData <- hpaDownload(downloadList='all')
# summary(downloadedData)
#
# #> Length Class Mode
# #> normal_tissue 6 tbl_df list
# #> pathology 11 tbl_df list
# #> subcellular_location 11 tbl_df list
# #> rna_tissue 5 tbl_df list
# #> rna_cell_line 5 tbl_df list
# #> transcript_rna_tissue 4 tbl_df list
# #> transcript_rna_cell_line 4 tbl_df list
## ----histology----------------------------------------------------------------
downloadedData <- hpaDownload(downloadList='histology', version='example')
# version = "example" will load the HPA v18 datasets came with this package. 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$subcellular_location, give.attr=FALSE)
## ----rna1, warning=FALSE, message=FALSE---------------------------------------
downloadedData <- hpaDownload(downloadList='rna', version='v18')
tibble::glimpse(downloadedData$rna_tissue, give.attr=FALSE)
## ----rna2, warning=FALSE, message=FALSE---------------------------------------
tibble::glimpse(downloadedData$rna_cell_line, give.attr=FALSE)
## ----isoform1, eval=FALSE-----------------------------------------------------
# downloadedData <- hpaDownload(downloadList='isoform', version='v18')
# # version = "v18" is an example of how you may download different versions of the HPA datasets. Just change the number. Note that not all versions are available from the HPA website.
#
# tibble::glimpse(downloadedData$transcript_rna_tissue, give.attr=FALSE)
#
# #> Observations: 27,535,996
# #> Variables: 4
# #> $ ensembl <chr> "ENSG00000000003", "ENSG00000000003", "ENSG0000000...
# #> $ transcript <chr> "ENST00000373020", "ENST00000494424", "ENST0000049...
# #> $ tissue <chr> "adipose tissue.V1", "adipose tissue.V1", "adipose...
# #> $ value <dbl> 27.3577003, 0.0000000, 1.9341500, 1.6059300, 0.000...
## ----isoform2, eval=FALSE-----------------------------------------------------
# tibble::glimpse(downloadedData$transcript_rna_cell_line, give.attr=FALSE)
#
# #> Observations: 20,972,183
# #> Variables: 4
# #> $ ensembl <chr> "ENSG00000000003", "ENSG00000000003", "ENSG0000000...
# #> $ transcript <chr> "ENST00000373020", "ENST00000494424", "ENST0000049...
# #> $ cell_line <chr> "A-431.C35", "A-431.C35", "A-431.C35", "A-431.C35"...
# #> $ value <dbl> 29.406799, 0.000000, 0.992916, 0.398387, 0.239204,...
## ----list_param, eval=FALSE---------------------------------------------------
# ## If you use the output from hpaDownload()
# downloadedData <- hpaDownload(downloadList='all')
# str(hpaListParam(downloadedData))
#
# #> List of 6
# #> $ normal_tissue : chr [1:58] "adrenal gland" "appendix" "bone marrow" "breast" ...
# #> $ normal_cell : chr [1:82] "glandular cells" "lymphoid tissue" "hematopoietic cells" "adipocytes" ...
# #> $ cancer : chr [1:20] "breast cancer" "carcinoid" "cervical cancer" "colorectal cancer" ...
# #> $ subcellular_location: chr [1:32] "Cytosol" "Mitochondria" "Aggresome" "Plasma membrane" ...
# #> $ normal_tissue_rna : chr [1:37] "adipose tissue" "adrenal gland" "appendix" "bone marrow" ...
# #> $ cell_line_rna : chr [1:64] "A-431" "A549" "AF22" "AN3-CA" ...
## ----list_param_2-------------------------------------------------------------
## If you use leave the argument blank
str(hpaListParam())
## ----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(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>
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