phemd.R
In phemd: Phenotypic EMD for comparison of single-cell samples

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knitr::opts_chunk$set(echo = TRUE)

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#  BiocManager::install("phemd")

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#  library(devtools)
#  install_github("wschen/phemd")

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library('phemd')
library('monocle')

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load('melanomaData.RData')
myobj <- createDataObj(all_expn_data, all_genes, as.character(snames))

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myobj <- removeTinySamples(myobj, min_sz = 20)

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myobj <- aggregateSamples(myobj, max_cells=12000)

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myobj <- selectFeatures(myobj, selected_genes)

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# 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)

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cmap <- plotEmbeddings(myobj, cell_model='monocle2')

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plotHeatmaps(myobj, cell_model='monocle2', selected_genes=heatmap_genes)

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# Determine cell subtype breakdown of each sample
myobj <- clusterIndividualSamples(myobj)

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# Determine (dis)similarity of different cell subtypes
myobj <- generateGDM(myobj)

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# Perform inter-sample comparisons using EMD
my_distmat <- compareSamples(myobj)

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## Identify similar groups of inhibitors
group_assignments <- groupSamples(my_distmat, distfun = 'hclust', ncluster=5)

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dmap_obj <- plotGroupedSamplesDmap(my_distmat, group_assignments, pt_sz = 1.5)

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plotGroupedSamplesDmap(my_distmat, group_assignments, pt_sz = 1.5)

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plotGroupedSamplesDmap(my_distmat, group_assignments, pt_sz = 1.5)

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# Plot cell subtype distribution (histogram) for each sample
sample.cellfreqs <- getSampleHistsByCluster(myobj, group_assignments, cell_model='monocle2')

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# 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)

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# Plot cell yield of each experimental condition
plotCellYield(myobj, group_assignments, font_sz = 0.7, w=8, h=5)

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sessionInfo()