# # - - - - -设置,包括= FALSE - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - knitr:: opts_chunk设置美元(崩溃= TRUE, = " # > "发表评论,警告= FALSE,错误= FALSE, eval = FALSE) # #——图书馆,消息= FALSE,警告= FALSE,错误= FALSE - - - - - - - - - - - - - - - - - - - - - - - - #库(BiocStyle) #库(HPAanalyze) #库(dplyr) #库(ggplot2) # # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #数据< - hpaDownload (downloadList =“组织学”,# version =“v18”) # # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # gene_list_2 < - c (TP53,表皮生长因子受体,CD44,“PTEN”、“IDH1”,“IDH2”,“本体”)# # #面板2 # tissue_list_2 < - c(“皮肤1”、“小脑”、“乳房”)# # plot_2a < - # hpaVisTissue (data =数据、# targetGene = gene_list_2 # targetTissue = tissue_list_2 #颜色= c (“# eff3ff”、“# bdd7e7”、“# 6 baed6”、“# 2171 b5”)) # # ggsave(文件名= " plot_2a。pdf”, #情节= plot_2a #设备= " pdf”) # # # # cancer_list_2面板2 b < - c(“乳腺癌”、“神经胶质瘤”、“淋巴瘤”、“前列腺癌”)# # plot_2b < - # hpaVisPatho (data =数据、# targetGene = gene_list_2 targetCancer = cancer_list_2) # # # ggsave(文件名= " plot_2b。pdf”, #情节= plot_2b #设备= " pdf”, #宽度= 7 #身高= 5)# # #面板2 c # plot_2c < - # hpaVisSubcell (data =数据、# targetGene = gene_list_2 #颜色= c(“白色”、“黑色”),#可靠性= c(“强化”、“支持”、“批准”))# # ggsave(文件名= " plot_2c。pdf”, #情节= plot_2c #设备= " pdf”) # # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # gene_list_3 < - # c (“GFAP”、“表皮生长因子受体”,“PDGFRA”、“PIK3CA”、“PTEN”、“BRAF”、“MDM2”、“MDM4”、“到”)3 # # # #面板tissue_list_3 < - c(“海马”,“大脑皮层”)# # plot_3a < - # hpaVisTissue (data =数据、# targetGene = gene_list_3 # targetTissue = tissue_list_3 #颜色= c (“# eff3ff”、“# bdd7e7”、“# 6 baed6”、“# 2171 b5”)) # # ggsave(文件名= " plot_3a。pdf”, #情节= plot_3a #设备= " pdf”, #宽度= 7 #身高= 5)# # #面板3 b # plot_3b < - # hpaVisPatho (data =数据、# targetGene = gene_list_3 # targetCancer =“神经胶质瘤”)# # ggsave(文件名= " plot_3b。pdf”, #情节= plot_3b #设备= " pdf”, #宽度= 7 #身高= 5)# # #面板3 c # gene_list_3c < - c (“PTEN”、“H3F3A”,“DAXX”、“PML”) # # plot_3c < - # hpaVisSubcell (data =数据、# targetGene = gene_list_3c #颜色= c(“白色”、“黑色”),#可靠性= c(“强化”、“支持”、“批准”))# # ggsave(文件名= " plot_3c。pdf”, #情节= plot_3c #设备= " pdf”, #宽度= 4,身高= 3)# # # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # gene_list_4 < - c (“GCH1”、“分”、“SPR”,“DHFR”) 4 # # # #面板tissue_list_4 < - c(“海马”、“大脑皮层”、“尾”)# # plot_4a < - # hpaVisTissue (data =数据、# targetGene = gene_list_4 # targetTissue = tissue_list_4 #颜色= c (“# eff3ff”、“# bdd7e7”、“# 6 baed6”、“# 2171 b5”)) # # ggsave(文件名= " plot_4a。pdf”, #情节= plot_4a #设备= " pdf”, #宽度= 5,#身高= 4)# # #面板4 b # plot_4b < - # hpaVisPatho (data =数据、# targetGene = gene_list_4 # targetCancer =“神经胶质瘤”)# # ggsave(文件名= " plot_4b。pdf”, #情节= plot_4b #设备= " pdf”, #宽度= 5,#身高= 4)# # #面板4 c # #图生成与GlioVis门户http://gliovis.bioinfo.cnio.es/ # #访问:6月19日2019 # # # #策划:# #导航选项卡:探索生存> > kaplan meier >情节# # # #参数:# # -数据集:成人伦勃朗# # -基因:SPR或DHFR # # -组织学:所有# # -亚型:所有# # -截止:# # -情节选项中位数:使用默认选项# # -下载:使用默认选项检索绘图数据:# # # #(相同的参数)# #浏览标签:探索生存> > kaplan meier >情节# #按钮:下载> CSV # # #面板4 d # plot_4d < - # hpaVisSubcell (data =数据、# targetGene = gene_list_4 #颜色= c(“白色”、“黑色”),#可靠性= c(“强化”、“支持”、“批准”))# # ggsave(文件名= " plot_4d。pdf”, #情节= plot_4d #设备= " pdf”, #宽度= 4,身高= 3)# # # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # hpaSubset (data =数据,# targetGene =“SLC2A3”, # targetTissue = c(“海马”、“大脑皮层”、“尾”),# targetCellType =“胶质细胞”,# targetCancer =“神经胶质瘤”)# # # $ normal_tissue # # #一个宠物猫:3 x 6 # #运用基因组织cell_type水平可靠性# # # # 1 ENSG00000059804 SLC2A3尾状胶质细胞没有检测到通过# # 2 ENSG00000059804 SLC2A3大脑皮层神经胶质细胞不被察觉的情况下批准了# # 3 ENSG00000059804 SLC2A3海马区胶质细胞不检测病理批准了# # # # $ # # #一个宠物猫:1 x 11 # #运用基因癌症高中低not_detected prognostic_favo ~ # # # # 1神经胶质瘤ENSG00 ~ SLC2 ~ 1 2 1 8 NA # # #……有三个变量:unprognostic_favorable ,# # # prognostic_unfavorable ,unprognostic_unfavorable subcellular_location美元# # # # # # #一个宠物猫:1 x 11 # #运用基因可靠性增强支持批准确定single_cell_var ~ # # # # 1 ENSG00批准~ SLC2 ~ ~ NA NA NA NA等离子体# # #……有三个变量: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 3.1 2006-03-13 1.2 NA # # 2 HPA006539 2006-03-13 AB_1078984 # # SLC2A3_expr <——hpaXmlTissueExpr (SLC2A3xml) # str (SLC2A3_expr[[1]]) # #类“tbl_df”、“资源”和“data.frame”: 330奥林匹克广播服务公司。18个变量:# # $ patientId:空空的“2212”“2374”“2068”“2154”……# # $年龄:空空的“35”“44”“38”“66”……# # $性:空空的“男”“女”“男”“女”……# # $染色:装备缺缺缺缺……# # $强度:装备缺缺缺缺……# #美元数量:装备缺缺缺缺……# # $位置:装备缺缺缺缺……# # $ imageUrl:空空的“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:空空的“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") # }