## ----style-knitr, eval=TRUE, echo=FALSE, results="asis"-------------------- BiocStyle::latex() ## ----setup, include=FALSE, cache=FALSE------------------------------------- library(knitr) opts_chunk$set(out.width="0.7\\maxwidth",fig.align="center") ## ----class-loadlibs, message=FALSE----------------------------------------- library(messina) library(antiProfilesData) data(apColonData) apColonData ## ----class-prepdata-------------------------------------------------------- x = exprs(apColonData) y = pData(apColonData)$SubType sel = y %in% c("normal", "tumor") x = x[,sel] y = y[sel] ## ----class-fit------------------------------------------------------------- fit.apColon = messina(x, y == "tumor", min_sens = 0.95, min_spec = 0.85, seed = 1234, silent = TRUE) ## ----class-show------------------------------------------------------------ fit.apColon ## ----class-plot------------------------------------------------------------ plot(fit.apColon, i = 1, plot_type = "point") ## ----class-ranked-plots,out.width="0.4\\maxwidth",fig.show="hold"---------- plot(fit.apColon, i = c(1,2,10,50), plot_type = "point") ## ----de-outlier-example,echo=FALSE----------------------------------------- library(ggplot2) temp.x = rep(c(0, 1), each = 6) set.seed(12345) temp.y = c(rnorm(6, 4, 0.5), rnorm(1, 4, 0.5), rnorm(5, 9, 0.5)) temp.data = data.frame(y = temp.y, x = 1:length(temp.y), Group = factor(ifelse(temp.x, "Cancer", "Normal"))) ggplot(temp.data, aes(y = y, x = x, fill = Group)) + geom_bar(stat = "identity") + ggtitle("Example of outlier expression") + xlab("Sample") + ylab("Expression in Sample") + theme(axis.text.x = element_blank(), axis.ticks.x = element_blank(), axis.text.y = element_blank()) ## ----surv-load------------------------------------------------------------- library(messina) library(survival) data(tcga_kirc_example) ## The data are present as a matrix of expression values, and a Surv object of ## survival times dim(kirc.exprs) kirc.surv ## ----surv-fit-------------------------------------------------------------- fit.surv = messinaSurv(kirc.exprs, kirc.surv, obj_func = "tau", obj_min = 0.6, parallel = FALSE, silent = TRUE) fit.surv ## ----surv-plot------------------------------------------------------------- plot(fit.surv, bootstrap_type = "ci") ## ----sessionInfo, eval=TRUE------------------------------------------------ sessionInfo()