## ----style, echo=FALSE, results='hide', message=FALSE------------------------- library(BiocStyle) library(knitr) opts_chunk$set(error=FALSE, message=FALSE, warning=FALSE) knitr::opts_chunk$set(echo = TRUE) ## ----eval = FALSE------------------------------------------------------------- # if(!requireNamespace("BiocManager", quietly = TRUE)) # install.packages("BiocManager") # BiocManager::install("DepecheR") ## ----------------------------------------------------------------------------- library(DepecheR) data('testData') str(testData) ## ----eval=FALSE--------------------------------------------------------------- # testDataDepeche <- depeche(testData[, 2:15]) ## ----------------------------------------------------------------------------- ## [1] "Files will be saved to ~/Desktop" ## [1] "As the dataset has less than 100 columns, peak centering is applied." ## [1] "Set 1 with 7 iterations completed in 14 seconds." ## [1] "Set 2 with 7 iterations completed in 6 seconds." ## [1] "Set 3 with 7 iterations completed in 6 seconds." ## [1] "The optimization was iterated 21 times." ## ----echo=FALSE--------------------------------------------------------------- data("testDataDepeche") ## ----------------------------------------------------------------------------- str(testDataDepeche) ## ----eval=FALSE--------------------------------------------------------------- # library(Rtsne) # testDataSNE <- Rtsne(testData[,2:15], pca=FALSE) ## ----echo=FALSE--------------------------------------------------------------- data("testDataSNE") ## ----------------------------------------------------------------------------- dColorPlot(colorData = testDataDepeche$clusterVector, xYData = testDataSNE$Y, colorScale = "dark_rainbow", plotName = "Cluster") ## ----------------------------------------------------------------------------- dColorPlot(colorData = testData[2], xYData = testDataSNE$Y) ## ----------------------------------------------------------------------------- densContour <- dContours(testDataSNE$Y) dDensityPlot(xYData = testDataSNE$Y, plotName = 'All_events', colorScale="purple3", densContour = densContour) #Here the data for the first group is plotted dDensityPlot(xYData = testDataSNE$Y[testData$label==0,], plotName = 'Group_0', colorScale="blue", densContour = densContour) #And here comes the second group dDensityPlot(xYData = testDataSNE$Y[testData$label==1,], plotName = 'Group_1', colorScale="red", densContour = densContour) ## ----------------------------------------------------------------------------- dResidualPlot( xYData = testDataSNE$Y, groupVector = testData$label, clusterVector = testDataDepeche$clusterVector) ## ----------------------------------------------------------------------------- dWilcoxResult <- dWilcox( xYData = testDataSNE$Y, idsVector = testData$ids, groupVector = testData$label, clusterVector = testDataDepeche$clusterVector) ## ----eval=FALSE--------------------------------------------------------------- # sPLSDAObject <- dSplsda(xYData = testDataSNE$Y, idsVector = testData$ids, # groupVector = testData$label, # clusterVector = testDataDepeche$clusterVector) # ## Saving 3 x 3 in image # # ## [1] "The separation of the datasets was perfect, with no overlap between # ## the groups" # # ## [1] "Files were saved at /Users/jakthe/Labbet/GitHub/DepecheR/vignettes" # ## ----eval--------------------------------------------------------------------- dViolins(testDataDepeche$clusterVector, inDataFrame = testData, plotClusters = 3, plotElements = testDataDepeche$essenceElementList) ## ----------------------------------------------------------------------------- sessionInfo()