## ----style, echo=FALSE, results='asis'---------------------------------------- BiocStyle::markdown() ## ----setup, echo=FALSE, message=FALSE----------------------------------------- library(Cardinal) setCardinalVerbose(FALSE) ## ----eda-data, fig.height=4, fig.width=5, fig.align='center'------------------ set.seed(2020) mse <- simulateImage(preset=2, dim=c(32,32), sdnoise=0.5, peakheight=c(2,4), representation="centroid") mse$design <- makeFactor(circle=mse$circle, square=mse$square, bg=!(mse$circle | mse$square)) image(mse, "design") ## ----eda-image, fig.height=4, fig.width=9------------------------------------- image(mse, i=c(5, 13, 21), layout=c(1,3)) ## ----pca---------------------------------------------------------------------- pca <- PCA(mse, ncomp=3) pca ## ----pca-image, fig.height=4, fig.width=9------------------------------------- image(pca, type="x", superpose=FALSE, layout=c(1,3), scale=TRUE) ## ----pca-loadings, fig.height=3, fig.width=9---------------------------------- plot(pca, type="rotation", superpose=FALSE, layout=c(1,3), linewidth=2) ## ----pca-scores, fig.height=4, fig.width=9------------------------------------ plot(pca, type="x", groups=mse$design, linewidth=2) ## ----nmf---------------------------------------------------------------------- nmf <- NMF(mse, ncomp=3) nmf ## ----nmf-image, fig.height=4, fig.width=9------------------------------------- image(nmf, type="x", superpose=FALSE, layout=c(1,3), scale=TRUE) ## ----nmf-loadings, fig.height=3, fig.width=9---------------------------------- plot(nmf, type="activation", superpose=FALSE, layout=c(1,3), linewidth=2) ## ----nmf-scores, fig.height=4, fig.width=9------------------------------------ plot(nmf, type="x", groups=mse$design, linewidth=2) ## ----colocalized-------------------------------------------------------------- coloc <- colocalized(mse, mz=1116) coloc ## ----colocalized-images, fig.height=4, fig.width=9---------------------------- image(mse, mz=coloc$mz[1:3], layout=c(1,3)) ## ----ssc-clustering----------------------------------------------------------- set.seed(2020) ssc <- spatialShrunkenCentroids(mse, r=1, k=3, s=c(0,6,12,18)) ssc ## ----ssc-image, fig.height=4, fig.width=9------------------------------------- image(ssc, i=2:4, type="probability", layout=c(1,3)) ## ----ssc-statistic, fig.height=3, fig.width=9--------------------------------- plot(ssc, i=2:4, type="statistic", layout=c(1,3), linewidth=2, annPeaks="circle") ## ----ssc-top, fig.height=4, fig.width=9--------------------------------------- ssc_top <- topFeatures(ssc[[4L]]) ssc_top ssc_top_cl3 <- subset(ssc_top, class==3) image(mse, mz=ssc_top_cl3$mz[1:3], layout=c(1,3)) ## ----dgmm--------------------------------------------------------------------- set.seed(2020) dgmm <- spatialDGMM(mse, r=1, k=3, weights="adaptive") dgmm ## ----dgmm-image, fig.height=4, fig.width=9------------------------------------ image(dgmm, i=c(5, 13, 21), layout=c(1,3)) ## ----dgmm-plot, fig.height=3, fig.width=9------------------------------------- plot(dgmm, i=c(5, 13, 21), layout=c(1,3), linewidth=2) ## ----dgmm-colocalized, fig.height=4, fig.width=9------------------------------ coloc2 <- colocalized(dgmm, mse$square) coloc2 image(mse, mz=coloc2$mz[1:3], layout=c(1,3)) ## ----classification-data, fig.height=4, fig.width=9--------------------------- set.seed(2020) mse2 <- simulateImage(preset=7, dim=c(32,32), sdnoise=0.3, nrun=3, peakdiff=2, representation="centroid") mse2$class <- makeFactor(A=mse2$circleA, B=mse2$circleB) image(mse2, "class", layout=c(1,3)) ## ----classification-images, fig.height=4, fig.width=9------------------------- image(mse2, i=1, layout=c(1,3)) ## ----pls-cv------------------------------------------------------------------- cv_pls <- crossValidate(PLS, x=mse2, y=mse2$class, ncomp=1:15, folds=run(mse2)) cv_pls ## ----pls---------------------------------------------------------------------- pls <- PLS(mse2, y=mse2$class, ncomp=11) pls ## ----pls-image, fig.height=4, fig.width=9------------------------------------- image(pls, type="response", layout=c(1,3), scale=TRUE) ## ----pls-coefficients, fig.height=3, fig.width=9------------------------------ plot(pls, type="coefficients", linewidth=2, annPeaks="circle") ## ----pls-scores, fig.height=4, fig.width=9------------------------------------ plot(pls, type="scores", groups=mse2$class, linewidth=2) ## ----ssc-cv------------------------------------------------------------------- cv_ssc <- crossValidate(spatialShrunkenCentroids, x=mse2, y=mse2$class, r=2, s=c(0,3,6,9,12,15,18), folds=run(mse2)) cv_ssc ## ----ssc-classification------------------------------------------------------- ssc2 <- spatialShrunkenCentroids(mse2, y=mse2$class, r=2, s=9) ssc2 ## ----ssc-image-2, fig.height=4, fig.width=9----------------------------------- image(ssc2, type="probability", layout=c(1,3), subset=mse2$circleA | mse2$circleB) ## ----ssc-statistic-2, fig.height=4, fig.width=9------------------------------- plot(ssc2, type="statistic", linewidth=2, annPeaks="circle") ## ----ssc-top-2---------------------------------------------------------------- ssc2_top <- topFeatures(ssc2) subset(ssc2_top, class == "B") ## ----test-data, fig.height=7, fig.width=9------------------------------------- set.seed(2020) mse3 <- simulateImage(preset=4, npeaks=10, dim=c(32,32), sdnoise=0.3, nrun=3, peakdiff=1, representation="centroid") mse3$trt <- makeFactor(A=mse3$circleA, B=mse3$circleB) image(mse3, "trt", layout=c(2,3)) ## ----test-image, fig.height=7, fig.width=9------------------------------------ image(mse3, i=1, layout=c(2,3)) ## ----test-diff---------------------------------------------------------------- featureData(mse3) ## ----test-mean-test----------------------------------------------------------- mtest <- meansTest(mse3, ~ condition, samples=run(mse3)) mtest ## ----test-mean-plot, fig.height=5, fig.width=9-------------------------------- plot(mtest, i=1:10, layout=c(2,5), ylab="Intensity", fill=TRUE) ## ----test-mean-top------------------------------------------------------------ mtest_top <- topFeatures(mtest) subset(mtest_top, fdr < 0.05) ## ----test-segment-dgmm-------------------------------------------------------- dgmm2 <- spatialDGMM(mse3, r=2, k=3, groups=run(mse3)) ## ----test-segment-test-------------------------------------------------------- stest <- meansTest(dgmm2, ~ condition) stest ## ----test-segment-plot, fig.height=5, fig.width=9----------------------------- plot(stest, i=1:10, layout=c(2,5), ylab="Intensity", fill=TRUE) ## ----test-segment-top--------------------------------------------------------- stest_top <- topFeatures(stest) subset(stest_top, fdr < 0.05) ## ----session-info------------------------------------------------------------- sessionInfo()