new file mode 100644

@@ -0,0 +1,34 @@

+library(cBioPortalData)

+library(dplyr)

+

+cbio <- cBioPortal()

+studies <- getStudies(cbio)

+

+#get studyIds

+study_names <-

+  data.frame(cbind(gsub(" \\(TCGA, PanCancer Atlas\\)", "",studies$name[grepl("tcga_pan_can_atlas", studies$studyId)]),

+                   studies$studyId[grepl("tcga_pan_can_atlas", studies$studyId)]))

+colnames(study_names) <- c("Cancer", "studyId")

+

+#loop through studyIds to get all_tcga2018_data

+all_tcga2018_data <- data.frame()

+for(study in study_names$studyId){

+  cbio_table <- getDataByGenePanel(cbio, study, genePanelId = "IMPACT468",

+                          molecularProfileId = paste0(study, "_gistic"),

+                     sampleListId = paste0(study, "_cna"))

+  cbio_dat <- data.frame(cbio_table[[1]], stringsAsFactors = FALSE)

+  cbio_summ <- cbio_dat %>% group_by(hugoGeneSymbol) %>%

+    summarise(Gain = sum(value ==1)/n(),

+              Amplification = sum(value == 2)/n(),

+              ShallowDeletion = sum(value == -1)/n(),

+              DeepDeletion = sum(value == -2)/n())

+  cbio_summ$sample_size <- rep(paste0(" (N = ", length(unique(cbio_dat$uniquePatientKey)), ")"), nrow(cbio_summ))

+  cbio_summ$studyId <- rep(study, nrow(cbio_summ))

+  all_tcga2018_data <- rbind(all_tcga2018_data, cbio_summ)

+}

+

+#add sample sizes to get cbio_studies

+ss <- all_tcga2018_data %>% group_by(studyId, sample_size) %>% summarise()

+cbio_studies <- inner_join(study_names, ss, by = c("studyId"))

+cbio_studies$Cancer <- paste0(cbio_studies$Cancer, cbio_studies$sample_size)

+cbio_studies <- dplyr::select(cbio_studies, Cancer, studyId)