params <- list(seed = 29221) ## ---- eval=FALSE-------------------------------------------------------------- # if (!require("BiocManager")) # install.packages("BiocManager") # BiocManager::install("glmSparseNet") ## ----packages, message=FALSE, warning=FALSE----------------------------------- library(dplyr) library(ggplot2) library(survival) library(futile.logger) library(curatedTCGAData) library(TCGAutils) # library(glmSparseNet) # # Some general options for futile.logger the debugging package .Last.value <- flog.layout(layout.format('[~l] ~m')) .Last.value <- glmSparseNet:::show.message(FALSE) # Setting ggplot2 default theme as minimal theme_set(ggplot2::theme_minimal()) ## ----include=TRUE,results="hide",message=FALSE,warning=FALSE------------------ brca <- curatedTCGAData(diseaseCode = "BRCA", assays = "RNASeq2GeneNorm", version = "1.1.38", dry.run = FALSE ) ## ----curated_data_non_eval, eval=FALSE---------------------------------------- # brca <- curatedTCGAData(diseaseCode = "BRCA", assays = "RNASeq2GeneNorm", # version = "1.1.38", dry.run = FALSE) ## ----data.show, warning=FALSE, error=FALSE------------------------------------ brca <- TCGAutils::TCGAsplitAssays(brca, c('01','11')) xdata.raw <- t(cbind(assay(brca[[1]]), assay(brca[[2]]))) # Get matches between survival and assay data class.v <- TCGAbiospec(rownames(xdata.raw))$sample_definition %>% factor names(class.v) <- rownames(xdata.raw) # keep features with standard deviation > 0 xdata.raw <- xdata.raw %>% { (apply(., 2, sd) != 0) } %>% { xdata.raw[, .] } %>% scale set.seed(params$seed) small.subset <- c('CD5', 'CSF2RB', 'HSF1', 'IRGC', 'LRRC37A6P', 'NEUROG2', 'NLRC4', 'PDE11A', 'PIK3CB', 'QARS', 'RPGRIP1L', 'SDC1', 'TMEM31', 'YME1L1', 'ZBTB11', sample(colnames(xdata.raw), 100)) xdata <- xdata.raw[, small.subset[small.subset %in% colnames(xdata.raw)]] ydata <- class.v ## ----fit.show----------------------------------------------------------------- fitted <- cv.glmHub(xdata, ydata, family = 'binomial', network = 'correlation', nlambda = 1000, network.options = networkOptions(cutoff = .6, min.degree = .2)) ## ----results------------------------------------------------------------------ plot(fitted) ## ----show_coefs--------------------------------------------------------------- coefs.v <- coef(fitted, s = 'lambda.min')[,1] %>% { .[. != 0]} coefs.v %>% { data.frame(ensembl.id = names(.), gene.name = geneNames(names(.))$external_gene_name, coefficient = ., stringsAsFactors = FALSE) } %>% arrange(gene.name) %>% knitr::kable() ## ----hallmarks---------------------------------------------------------------- geneNames(names(coefs.v)) %>% { hallmarks(.$external_gene_name)$heatmap } ## ----accuracy, echo=FALSE----------------------------------------------------- resp <- predict(fitted, s = 'lambda.min', newx = xdata, type = 'class') flog.info('Misclassified (%d)', sum(ydata != resp)) flog.info(' * False primary solid tumour: %d', sum(resp != ydata & resp == 'Primary Solid Tumor')) flog.info(' * False normal : %d', sum(resp != ydata & resp == 'Solid Tissue Normal')) ## ----predict, echo=FALSE, warning=FALSE--------------------------------------- response <- predict(fitted, s = 'lambda.min', newx = xdata, type = 'response') qplot(response, bins = 100) ## ----roc, echo=FALSE---------------------------------------------------------- roc_obj <- pROC::roc(ydata, as.vector(response)) data.frame(TPR = roc_obj$sensitivities, FPR = 1 - roc_obj$specificities) %>% ggplot() +geom_line(aes(FPR,TPR), color = 2, size = 1, alpha = 0.7)+ labs(title= sprintf("ROC curve (AUC = %f)", pROC::auc(roc_obj)), x = "False Positive Rate (1-Specificity)", y = "True Positive Rate (Sensitivity)") ## ----sessionInfo-------------------------------------------------------------- sessionInfo()