## ----style, echo=FALSE, results='asis'---------------------------------------- BiocStyle::markdown() ## ----setup, echo=FALSE, message=FALSE----------------------------------------- library(CardinalWorkflows) setCardinalBPPARAM(SerialParam()) setCardinalVerbose(FALSE) RNGkind("L'Ecuyer-CMRG") ## ----------------------------------------------------------------------------- library(Cardinal) ## ----load-rcc----------------------------------------------------------------- data(rcc, package="CardinalWorkflows") rcc <- as(rcc, "MSImagingExperiment") ## ----show-rcc----------------------------------------------------------------- rcc ## ----normalize-rcc------------------------------------------------------------ rcc <- rcc %>% subsetPixels(!is.na(diagnosis)) %>% normalize(method="tic") %>% process() rcc ## ----rcc-mean----------------------------------------------------------------- rcc_mean <- summarizeFeatures(rcc, "mean") ## ----rcc-peak-process--------------------------------------------------------- rcc_ref <- rcc_mean %>% peakPick(SNR=3) %>% peakAlign(ref="mean", tolerance=0.5, units="mz") %>% peakFilter() %>% process() ## ----rcc-peak-bin------------------------------------------------------------- rcc_peaks <- rcc %>% peakBin(ref=mz(rcc_ref), tolerance=0.5, units="mz") %>% process() rcc_peaks ## ----mz-810, fig.height=10---------------------------------------------------- image(rcc_peaks, mz=810, layout=c(4,2), contrast.enhance="suppress", normalize.image="linear") ## ----rcc-pca------------------------------------------------------------------ rcc_pca <- PCA(rcc_peaks, ncomp=2) ## ----pca-image, fig.height=10------------------------------------------------- image(rcc_pca, layout=c(4,2), normalize.image="linear") ## ----pca-scores--------------------------------------------------------------- pc_scores <- DataFrame(resultData(rcc_pca, 1, "scores")) ## ----pca-scoreplot-1---------------------------------------------------------- plot(pc_scores, PC1 ~ PC2, groups=rcc$diagnosis) ## ----pca-scoreplot-2---------------------------------------------------------- plot(pc_scores, PC1 ~ PC2, groups=run(rcc)) ## ----rcc-cv, message=FALSE---------------------------------------------------- rcc_ssc_cv <- crossValidate(rcc, rcc$diagnosis, .fun="spatialShrunkenCentroids", r=1, s=c(0,3,6,9,12,15), .fold=run(rcc), .process=TRUE, .processControl=list(SNR=3, tolerance=0.5, units="mz")) summary(rcc_ssc_cv) ## ----rcc-cv-plot-------------------------------------------------------------- plot(summary(rcc_ssc_cv), Accuracy ~ s, type='b') abline(v=9, lty=2, col="red") ## ----rcc-ssc------------------------------------------------------------------ rcc_ssc <- spatialShrunkenCentroids(rcc_peaks, rcc$diagnosis, r=1, s=9) summary(rcc_ssc) ## ----rcc-ssc-image, fig.height=10--------------------------------------------- image(rcc_ssc, layout=c(4,2)) ## ----rcc-ssc-mean------------------------------------------------------------- setup.layout(c(2,1)) plot(rcc_ssc, column=1, col=discrete.colors(2)[1], lwd=2, layout=NULL) plot(rcc_ssc, column=2, col=discrete.colors(2)[2], lwd=2, layout=NULL) ## ----rcc-statistic------------------------------------------------------------ plot(rcc_ssc, values="statistic", lwd=2) ## ----top-cancer--------------------------------------------------------------- topFeatures(rcc_ssc, class=="cancer") ## ----mz-885, fig.height=10---------------------------------------------------- image(rcc_peaks, mz=885, contrast.enhance="suppress", layout=c(4,2)) ## ----top-normal--------------------------------------------------------------- topFeatures(rcc_ssc, class=="normal") ## ----mz-215, fig.height=10---------------------------------------------------- image(rcc_peaks, mz=215, contrast.enhance="suppress", layout=c(4,2)) ## ----session-info------------------------------------------------------------- sessionInfo()