## ----knitr-options, echo=FALSE, message=FALSE, warning=FALSE------------------ library(knitr) opts_chunk$set(fig.align = 'center', fig.width = 6, fig.height = 5, dev = 'png') ## ----eval = FALSE------------------------------------------------------------- # if (!require("BiocManager")) # install.packages("BiocManager") # BiocManager::install("FuseSOM") ## ----message=FALSE, warning=FALSE--------------------------------------------- # load FuseSOM library(FuseSOM) ## ----------------------------------------------------------------------------- # load in the data data("risom_dat") # define the markers of interest risomMarkers <- c('CD45','SMA','CK7','CK5','VIM','CD31','PanKRT','ECAD', 'Tryptase','MPO','CD20','CD3','CD8','CD4','CD14','CD68','FAP', 'CD36','CD11c','HLADRDPDQ','P63','CD44') # we will be using the manual_gating_phenotype as the true cell type to gauge # performance names(risom_dat)[names(risom_dat) == 'manual_gating_phenotype'] <- 'CellType' ## ----------------------------------------------------------------------------- risomRes <- runFuseSOM(data = risom_dat, markers = risomMarkers, numClusters = 23) ## ----------------------------------------------------------------------------- # get the distribution of the clusters table(risomRes$clusters)/sum(table(risomRes$clusters)) ## ----------------------------------------------------------------------------- risomHeat <- FuseSOM::markerHeatmap(data = risom_dat, markers = risomMarkers, clusters = risomRes$clusters, clusterMarkers = TRUE) ## ----------------------------------------------------------------------------- # lets estimate the number of clusters using all the methods # original clustering has 23 clusters so we will set kseq from 2:25 # we pass it the som model generated in the previous step risomKest <- estimateNumCluster(data = risomRes$model, kSeq = 2:25, method = c("Discriminant", "Distance")) ## ----------------------------------------------------------------------------- # what is the best number of clusters determined by the discriminant method? # optimal number of clusters according to the discriminant method is 7 risomKest$Discriminant # we can plot the results using the optiplot function pSlope <- optiPlot(risomKest, method = 'slope') pSlope pJump <- optiPlot(risomKest, method = 'jump') pJump pWcd <- optiPlot(risomKest, method = 'wcd') pWcd pGap <- optiPlot(risomKest, method = 'gap') pGap pSil <- optiPlot(risomKest, method = 'silhouette') pSil ## ----message=FALSE, warning=FALSE--------------------------------------------- library(SingleCellExperiment) # create a singelcellexperiment object colDat <- risom_dat[, setdiff(colnames(risom_dat), risomMarkers)] sce <- SingleCellExperiment(assays = list(counts = t(risom_dat)), colData = colDat) sce ## ----------------------------------------------------------------------------- risomRessce <- runFuseSOM(sce, markers = risomMarkers, assay = 'counts', numClusters = 23, verbose = FALSE) colnames(colData(risomRessce)) names(metadata(risomRessce)) ## ----------------------------------------------------------------------------- data <- risom_dat[, risomMarkers] # get the original data used clusters <- colData(risomRessce)$clusters # extract the clusters from the sce object # generate the heatmap risomHeatsce <- markerHeatmap(data = risom_dat, markers = risomMarkers, clusters = clusters, clusterMarkers = TRUE) ## ----------------------------------------------------------------------------- # lets estimate the number of clusters using all the methods # original clustering has 23 clusters so we will set kseq from 2:25 # now we pass it a singlecellexperiment object instead of the som model as before # this will return a singelcellexperiment object where the metatdata contains the # cluster estimation information risomRessce <- estimateNumCluster(data = risomRessce, kSeq = 2:25, method = c("Discriminant", "Distance")) names(metadata(risomRessce)) ## ----------------------------------------------------------------------------- # what is the best number of clusters determined by the discriminant method? # optimal number of clusters according to the discriminant method is 8 metadata(risomRessce)$clusterEstimation$Discriminant # we can plot the results using the optiplot function pSlope <- optiPlot(risomRessce, method = 'slope') pSlope pJump <- optiPlot(risomRessce, method = 'jump') pJump pWcd <- optiPlot(risomRessce, method = 'wcd') pWcd pGap <- optiPlot(risomRessce, method = 'gap') pGap pSil <- optiPlot(risomRessce, method = 'silhouette') pSil ## ----------------------------------------------------------------------------- sessionInfo()