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
# <chr> <chr> <chr> <chr> <chr> <chr>
# 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~
# <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 ENSG00~ SLC2~ glioma 1 2 1 8 NA
# # ... with 3 more variables: unprognostic_favorable <dbl>,
# # prognostic_unfavorable <dbl>, unprognostic_unfavorable <dbl>
#
# $subcellular_location
# # A tibble: 1 x 11
# ensembl gene reliability enhanced supported approved uncertain single_cell_var~
# <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
# 1 ENSG00~ SLC2~ Approved NA NA Plasma ~ NA NA
# # ... with 3 more variables: single_cell_var_spatial <chr>,
# # cell_cycle_dependency <chr>, go_id <chr>
SLC2A3xml <- hpaXmlGet("SLC2A3", version = "v18")
SLC2A3_ab <- hpaXmlAntibody(SLC2A3xml)
SLC2A3_ab
# id releaseDate releaseVersion RRID
# <chr> <chr> <chr> <chr>
# 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")
}Anh Tran, 2018-2023
Please cite: Tran, A.N., Dussaq, A.M., Kennell, T. et al. HPAanalyze: an R package that facilitates the retrieval and analysis of the Human Protein Atlas data. BMC Bioinformatics 20, 463 (2019) https://doi.org/10.1186/s12859-019-3059-z