## ----setup, include=FALSE----------------------------------------------------- knitr::opts_chunk$set(echo = TRUE) ## ----echo=T, results = 'hide',message=F, warning=F, eval=F-------------------- # BiocManager::install("phemd") ## ----echo=T, results = 'hide',message=F, warning=F, eval=F-------------------- # library(devtools) # install_github("wschen/phemd") ## ----echo=T, results = 'hide', message=F, warning=F--------------------------- library('phemd') library('monocle') ## ----echo=T, results = 'hide', message=F, warning=F--------------------------- load('melanomaData.RData') myobj <- createDataObj(all_expn_data, all_genes, as.character(snames)) ## ----echo=T------------------------------------------------------------------- myobj <- removeTinySamples(myobj, min_sz = 20) ## ----echo=T, results = 'hide'------------------------------------------------- myobj <- aggregateSamples(myobj, max_cells=12000) ## ----echo=T, results = 'hide'------------------------------------------------- myobj <- selectFeatures(myobj, selected_genes) ## ----echo=T, results = 'hide', message=F, warning=F--------------------------- # generate 2D cell embedding and cell subtype assignments myobj <- embedCells(myobj, data_model = 'gaussianff', pseudo_expr=0, sigma=0.02) # generate pseudotime ordering of cells myobj <- orderCellsMonocle(myobj) ## ----echo=T, results = 'hide', fig.width=8, fig.height=4---------------------- cmap <- plotEmbeddings(myobj, cell_model='monocle2') ## ----echo=T, results = 'hide', fig.width=8, fig.height=6---------------------- plotHeatmaps(myobj, cell_model='monocle2', selected_genes=heatmap_genes) ## ----echo=T, results = 'hide'------------------------------------------------- # Determine cell subtype breakdown of each sample myobj <- clusterIndividualSamples(myobj) ## ----echo=T, results = 'hide'------------------------------------------------- # Determine (dis)similarity of different cell subtypes myobj <- generateGDM(myobj) ## ----echo=T, results = 'hide'------------------------------------------------- # Perform inter-sample comparisons using EMD my_distmat <- compareSamples(myobj) ## ----echo=T, results = 'hide'------------------------------------------------- ## Identify similar groups of inhibitors group_assignments <- groupSamples(my_distmat, distfun = 'hclust', ncluster=5) ## ----echo=T, results = 'hide', fig.width=8, fig.height=7, fig.keep='none'----- dmap_obj <- plotGroupedSamplesDmap(my_distmat, group_assignments, pt_sz = 1.5) ## ----echo=F, results = 'hide', fig.keep='none'-------------------------------- plotGroupedSamplesDmap(my_distmat, group_assignments, pt_sz = 1.5) ## ----echo=F, results = 'hide', fig.width=8, fig.height=7---------------------- plotGroupedSamplesDmap(my_distmat, group_assignments, pt_sz = 1.5) ## ----echo=T, results = 'hide'------------------------------------------------- # Plot cell subtype distribution (histogram) for each sample sample.cellfreqs <- getSampleHistsByCluster(myobj, group_assignments, cell_model='monocle2') ## ----echo=T, results = 'hide', fig.width=10, fig.height=2.5------------------- # Plot representative cell subtype distribution for each group of samples plotSummaryHistograms(myobj, group_assignments, cell_model='monocle2', cmap, ncol.plot = 5, ax.lab.sz=1.3, title.sz=2) ## ----echo=T, results='hide', fig.width=8, fig.height=5.5---------------------- # Plot cell yield of each experimental condition plotCellYield(myobj, group_assignments, font_sz = 0.7, w=8, h=5) ## ----echo=T------------------------------------------------------------------- sessionInfo()