## ----setup, include=FALSE----------------------------------------------------- knitr::opts_chunk$set( collapse=TRUE, comment="#>", warning=FALSE, error=FALSE, eval=FALSE ) ## ----library, message=FALSE, warning=FALSE, error=FALSE----------------------- # library(BiocStyle) # library(HPAanalyze) # library(dplyr) # library(ggplot2) ## ----------------------------------------------------------------------------- # data <- hpaDownload(downloadList = "histology", # version = "v18") ## ----------------------------------------------------------------------------- # gene_list_2 <- c("TP53", "EGFR", "CD44", "PTEN", "IDH1", "IDH2", "CYCS") # # # Panel 2A # tissue_list_2 <- c("skin 1", "cerebellum", "breast") # # plot_2a <- # hpaVisTissue(data = data, # targetGene = gene_list_2, # targetTissue = tissue_list_2, # color = c("#eff3ff", "#bdd7e7","#6baed6", "#2171b5")) # # ggsave(filename = "plot_2a.pdf", # plot = plot_2a, # device = "pdf") # # # Panel 2B # cancer_list_2 <- c("breast cancer", "glioma", "lymphoma", "prostate cancer") # # plot_2b <- # hpaVisPatho(data = data, # targetGene = gene_list_2, # targetCancer = cancer_list_2) # # ggsave(filename = "plot_2b.pdf", # plot = plot_2b, # device = "pdf", # width = 7, # height = 5) # # # Panel 2C # plot_2c <- # hpaVisSubcell(data = data, # targetGene = gene_list_2, # color = c("white", "black"), # reliability = c("enhanced", "supported", "approved")) # # ggsave(filename = "plot_2c.pdf", # plot = plot_2c, # device = "pdf") ## ----------------------------------------------------------------------------- # gene_list_3 <- # c("GFAP", "EGFR", "PDGFRA", "PIK3CA", "PTEN", "BRAF", "MDM2", "MDM4", "CDK4") # # # Panel 3A # tissue_list_3 <- c("hippocampus", "cerebral cortex") # # plot_3a <- # hpaVisTissue(data = data, # targetGene = gene_list_3, # targetTissue = tissue_list_3, # color = c("#eff3ff", "#bdd7e7","#6baed6", "#2171b5")) # # ggsave(filename = "plot_3a.pdf", # plot = plot_3a, # device = "pdf", # width = 7, # height = 5) # # # Panel 3B # plot_3b <- # hpaVisPatho(data = data, # targetGene = gene_list_3, # targetCancer = "glioma") # # ggsave(filename = "plot_3b.pdf", # plot = plot_3b, # device = "pdf", # width = 7, # height = 5) # # # Panel 3C # gene_list_3c <- c("PTEN", "H3F3A", "DAXX", "PML") # # plot_3c <- # hpaVisSubcell(data = data, # targetGene = gene_list_3c, # color = c("white", "black"), # reliability = c("enhanced", "supported", "approved")) # # ggsave(filename = "plot_3c.pdf", # plot = plot_3c, # device = "pdf", # width = 4, # height = 3) ## ----------------------------------------------------------------------------- # gene_list_4 <- c("GCH1", "PTS", "SPR", "DHFR") # # # Panel 4A # tissue_list_4 <- c("hippocampus", "cerebral cortex", "caudate") # # plot_4a <- # hpaVisTissue(data = data, # targetGene = gene_list_4, # targetTissue = tissue_list_4, # color = c("#eff3ff", "#bdd7e7","#6baed6", "#2171b5")) # # ggsave(filename = "plot_4a.pdf", # plot = plot_4a, # device = "pdf", # width = 5, # height = 4) # # # Panel 4B # plot_4b <- # hpaVisPatho(data = data, # targetGene = gene_list_4, # targetCancer = "glioma") # # ggsave(filename = "plot_4b.pdf", # plot = plot_4b, # device = "pdf", # width = 5, # height = 4) # # # Panel 4C # # Figure was generated with the GlioVis portal http://gliovis.bioinfo.cnio.es/ # # Accessed: June 19, 2019 # # # # Plotting: # # Navigate through tabs: Explore > Survival > Kaplan-Meier > Plot # # # # Parameters: # # - Dataset: Adult Rembrandt # # - Gene: SPR or DHFR # # - Histology: All # # - Subtype: All # # - Cutoff: Median # # - Plot options: use default options # # - Download: use default options # # # # Retrieving plotting data: (same parameters) # # Navigate through tabs: Explore > Survival > Kaplan-Meier > Plot # # Buttons: Download > CSV # # # Panel 4D # plot_4d <- # hpaVisSubcell(data = data, # targetGene = gene_list_4, # color = c("white", "black"), # reliability = c("enhanced", "supported", "approved")) # # ggsave(filename = "plot_4d.pdf", # plot = plot_4d, # device = "pdf", # width = 4, # height = 3) ## ----------------------------------------------------------------------------- # hpaSubset(data = data, # targetGene = "SLC2A3", # targetTissue = c("hippocampus", "cerebral cortex", "caudate"), # targetCellType = "glial cells", # targetCancer = "glioma") # # # $normal_tissue # # # A tibble: 3 x 6 # # ensembl gene tissue cell_type level reliability # # # # 1 ENSG00000059804 SLC2A3 caudate glial cells Not detected Approved # # 2 ENSG00000059804 SLC2A3 cerebral cortex glial cells Not detected Approved # # 3 ENSG00000059804 SLC2A3 hippocampus glial cells Not detected Approved # # # # $pathology # # # A tibble: 1 x 11 # # ensembl gene cancer high medium low not_detected prognostic_favo~ # # # # 1 ENSG00~ SLC2~ glioma 1 2 1 8 NA # # # ... with 3 more variables: unprognostic_favorable , # # # prognostic_unfavorable , unprognostic_unfavorable # # # # $subcellular_location # # # A tibble: 1 x 11 # # ensembl gene reliability enhanced supported approved uncertain single_cell_var~ # # # # 1 ENSG00~ SLC2~ Approved NA NA Plasma ~ NA NA # # # ... with 3 more variables: single_cell_var_spatial , # # # cell_cycle_dependency , go_id # # # SLC2A3xml <- hpaXmlGet("SLC2A3", version = "v18") # # SLC2A3_ab <- hpaXmlAntibody(SLC2A3xml) # SLC2A3_ab # # id releaseDate releaseVersion RRID # # # # 1 CAB002763 2006-03-13 1.2 NA # # 2 HPA006539 2008-02-15 3.1 AB_1078984 # # SLC2A3_expr <- hpaXmlTissueExpr(SLC2A3xml) # str(SLC2A3_expr[[1]]) # # Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 330 obs. of 18 variables: # # $ patientId : chr "2212" "2374" "2068" "2154" ... # # $ age : chr "35" "44" "38" "66" ... # # $ sex : chr "Male" "Female" "Male" "Female" ... # # $ staining : chr NA NA NA NA ... # # $ intensity : chr NA NA NA NA ... # # $ quantity : chr NA NA NA NA ... # # $ location : chr NA NA NA NA ... # # $ imageUrl : chr "http://v18.proteinatlas.org/images/2763/6778_B_4_5.jpg" "http://v18.proteinatlas.org/images/2763/6778_B_5_5.jpg" "http://v18.proteinatlas.org/images/2763/6778_A_3_2.jpg" "http://v18.proteinatlas.org/images/2763/6778_A_1_2.jpg" ... # # $ snomedCode1 : chr "M-00100" "M-00100" "M-00100" "M-00100" ... # # $ snomedCode2 : chr "T-93000" "T-93000" "T-66000" "T-66000" ... # # $ snomedCode3 : chr NA NA NA NA ... # # $ snomedCode4 : chr NA NA NA NA ... # # $ snomedCode5 : chr NA NA NA NA ... # # $ tissueDescription1: chr "Normal tissue, NOS" "Normal tissue, NOS" "Normal tissue, NOS" "Normal tissue, NOS" ... # # $ tissueDescription2: chr "Adrenal gland" "Adrenal gland" "Appendix" "Appendix" ... # # $ tissueDescription3: chr NA NA NA NA ... # # $ tissueDescription4: chr NA NA NA NA ... # # $ tissueDescription5: chr NA NA NA NA ... # # dir.create("img") # # SLC2A3_norm <- # SLC2A3_expr[[1]] %>% # filter(tissueDescription1 == "Normal tissue, NOS") %>% # filter(tissueDescription2 %in% c("Cerebral cortex", "Hippocampus", "Lateral ventricle wall")) # # for (i in 1:nrow(SLC2A3_norm)) { # download.file(SLC2A3_norm$imageUrl[i], # destfile = paste0("img/", SLC2A3_ab$id[1], "_", # SLC2A3_norm$patientId[i], "_", # SLC2A3_norm$tissueDescription2[i], "_", # SLC2A3_norm$staining[i], # ".jpg"), # mode = "wb") # } # # SLC2A3_glioma <- # SLC2A3_expr[[1]] %>% # filter(tissueDescription1 %in% c("Glioma, malignant, High grade", "Glioma, malignant, Low grade", "Glioma, malignant, NOS")) # # for (i in 1:nrow(SLC2A3_glioma)) { # download.file(SLC2A3_glioma$imageUrl[i], # destfile = paste0("img/", SLC2A3_ab$id[1], "_", # SLC2A3_glioma$patientId[i], "_", # SLC2A3_glioma$tissueDescription1[i], "_", # SLC2A3_glioma$staining[i], # ".jpg"), # mode = "wb") # }