title: “Multiple.featuresplot” author: “Upasna Srivastava” date: “2023-06-30” output: html_document —

library(Seurat)
library(ggplot2)
library(cowplot)
# set up list of canonical cell type markers
canonical_markers <- list(
  'Astrocyte' = c('GFAP', 'AQP4', 'SLC1A2'),
  'Pan-neuronal' = c('SNAP25', 'SYT1'),
  'Excitatory Neuron' = c('SLC17A7', 'SATB2'),
  'Inhibitory Neuron' = c('GAD1', 'GAD2'),
  'Microglia' = c('CSF1R', 'CD74', 'P2RY12'),
  'Oligodendrocyte' = c('MOBP', 'MBP', 'MOG'),
  'Olig. Progenitor' = c('PDGFRA', 'CSPG4')
)

# plot heatmap:
library(viridis)
png('figures/basic_canonical_marker_heatmap.png', width=10, height=10, units='in', res=200)
DoHeatmap(seurat_obj, group.by ="seurat_clusters", features=as.character(unlist(canonical_markers))) # + scale_fill_gradientn(colors=viridis(256))
dev.off()

# create feature plots, cutoff expression values for the 98th and 99th percentile
plot_list <- FeaturePlot(
  seurat_obj,
  features=unlist(canonical_markers),
  combine=FALSE, cols=viridis(256),
  max.cutoff='q98'
)

# apply theme to each feature plot
for(i in 1:length(plot_list)){
  plot_list[[i]] <- plot_list[[i]] + umap_theme + NoLegend()
}

png('figures/basic_canonical_marker_featurePlot.png', width=10, height=10, units='in', res=200)
CombinePlots(plot_list)
dev.off()
for(celltype in names(canonical_markers)){

  print(celltype)
  cur_features <- canonical_markers[[celltype]]

  # plot distributions for marker genes:
  p <- VlnPlot(
    seurat_obj,
    group.by='seurat_clusters',
    features=cur_features,
    pt.size = 0, ncol=1
  )
  png(paste0('figures/basic_canonical_marker_',celltype,'_vlnPlot.png'), width=10, height=3*length(cur_features), units='in', res=200)
  print(p)
  dev.off()

}