a b/FigS3L.R 

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GIT_HOME="/research/users/ppolonen/git_home/ImmunogenomicLandscape-BloodCancers/"





  
  

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source(file.path(GIT_HOME, "common_scripts/scRNA/functions.scRNA.analysis.R"))





  
  

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source(file.path(GIT_HOME, "common_scripts/visualisation/plotting_functions.R"))





  
  

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library(Matrix)





  
  

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library(Seurat)





  
  

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library(data.table)





  
  

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library(ComplexHeatmap)





  
  

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library(circlize)





  
  

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library(parallel)





  
  

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library(ggplot2)





  
  

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setwd("/research/groups/sysgen/PROJECTS/HEMAP_IMMUNOLOGY/petri_work/HEMAP_IMMUNOLOGY/Published_data_figures")





  
  

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# Compare MDS in blasts between FIMM AML and then compare to HCA





  
  

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load("HCA_scRNA.Rdata")





  
  

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MDS=get(load("MDS_genesets.Rdata"))





  
  

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Idents(scmat)=scmat[["SingleR.label"]]





  
  

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markers=FindAllMarkers(scmat, features = MDS$MDS_signature_all_filt[MDS$MDS_signature_all_filt%in%rownames(scmat)], only.pos = T)





  
  

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for(i in 1:5){





  
  

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  markers[,i]=prettyNum(signif(markers[,i],2))





  
  

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}





  
  

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write.table(markers[markers$avg_logFC>0,c("gene",   "cluster",  "avg_logFC",    "pct.1",    "pct.2",    "p_val",    "p_val_adj")], "TableS3_MDS_signature_markers_celltype_HCA.txt", quote = F, row.names = F, sep="\t")





  
  

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#********************************** Genes expressed in MDS Blasts vs other blasts **************************************





  
  

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markers=markers[markers$avg_logFC>0,]





  
  

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erythroid=markers$gene[markers$cluster%in%c("Erythrocytes")]





  
  

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HSC=markers$gene[markers$cluster%in%c("HSC", "MPP", "MEP")]





  
  

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HSC=HSC[!HSC%in%erythroid]





  
  

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scmat[["SingleR.label2"]]=factor(scmat[["SingleR.label"]][,1], levels=c("HSC","MPP","MEP", "CLP","CMP","GMP","Monocytes","DC","Macrophages","Macrophages M1","Macrophages M2","CD4+ Tn","CD4+ T-cells", "CD4+ Tcm", "CD4+ Tem","CD8+ Tn","CD8+ T-cells","CD8+ Tcm","CD8+ Tem","NK cells","Tregs","naive B-cells","Memory B-cells","Class-switched memory B-cells","Plasma cells","Endothelial cells","Neutrophils","Eosinophils","Fibroblasts","Smooth muscle","Erythrocytes","Megakaryocytes"))





  
  

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pdf("HCA_HSC_genes.pdf", width = 5, height = 4)





  
  

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plot.DotPlot(scmat[,grepl("HSC|MPP|MEP|CMP|GMP|Mono|Eryth", scmat[["SingleR.label2"]][,1])], group.by = "SingleR.label2", features = unique(HSC), cols = c("white", "red"), dot.scale = 5)





  
  

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dev.off()





  
  

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pdf("HCA_erythroid_genes.pdf", width = 5, height = 8)





  
  

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plot.DotPlot(scmat[,grepl("HSC|MPP|MEP|CMP|GMP|Mono|Eryth", scmat[["SingleR.label2"]][,1])], group.by = "SingleR.label2", features = unique(erythroid), cols = c("white", "red"), dot.scale = 5)





  
  

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dev.off()





  
  

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pdf("HCA_HSC_erythroid_genes_filt.pdf", width = 4, height = 3)





  
  

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plot.DotPlot(scmat[,grepl("HSC|MPP|MEP|CMP|GMP|Mono|Eryth", scmat[["SingleR.label2"]][,1])], group.by = "SingleR.label2", features = c("DPPA4","CRHBP","CSF3R","BEX1","EREG", "CA1", "HBD", "HEMGN", "MYL4", "SNCA"), cols = c("white", "red"), dot.scale = 6, scale.max = 50, scale.min = 0)





  
  

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dev.off()





  
  

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# same in FIMM MDS samples





  
  

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load("FIMM_AML_scRNA.Rdata")





  
  

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Idents(scmat)=scmat[["SingleR.label"]]





  
  

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markers=FindAllMarkers(scmat, features = MDS$MDS_signature_all_filt[MDS$MDS_signature_all_filt%in%rownames(scmat)], only.pos = T, logfc.threshold = 0.025)





  
  

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excl=table(scmat[["SingleR.label"]])<0.001*dim(scmat)[2]





  
  

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for(i in 1:5){





  
  

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  markers[,i]=prettyNum(signif(markers[,i],2))





  
  

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}





  
  

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write.table(markers[markers$avg_logFC>0&!markers$cluster%in%names(excl)[excl],c("gene", "cluster",  "avg_logFC",    "pct.1",    "pct.2",    "p_val",    "p_val_adj")], "TableS3_MDS_signature_markers_celltype_AML.txt", quote = F, row.names = F, sep="\t")





  
  

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scmat[["SingleR.label2"]]=factor(scmat[["SingleR.label"]][,1], levels=c("HSC","MPP","MEP", "CLP","CMP","GMP","Monocytes","DC","Macrophages","Macrophages M1","Macrophages M2","CD4+ Tn","CD4+ T-cells", "CD4+ Tcm", "CD4+ Tem","CD8+ Tn","CD8+ T-cells","CD8+ Tcm","CD8+ Tem","NK cells","Tregs","naive B-cells","Memory B-cells","Class-switched memory B-cells","Plasma cells","Endothelial cells","Neutrophils","Eosinophils","Fibroblasts","Smooth muscle","Erythrocytes","Megakaryocytes"))





  
  

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# pdf("FIMM_AML_HSC_genes.pdf", width = 5, height = 4)





  
  

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# plot.DotPlot(scmat[,grepl("HSC|MPP|MEP|CMP|GMP|Mono|Eryth", scmat[["SingleR.label2"]][,1])], group.by = "SingleR.label2", features = unique(HSC), cols = c("white", "red"), dot.scale = 5)





  
  

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# dev.off()





  
  

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# 





  
  

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# pdf("FIMM_AML_erythroid_genes.pdf", width = 5, height = 8)





  
  

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# plot.DotPlot(scmat[,grepl("HSC|MPP|MEP|CMP|GMP|Mono|Eryth", scmat[["SingleR.label2"]][,1])], group.by = "SingleR.label2", features = unique(erythroid), cols = c("white", "red"), dot.scale = 5)





  
  

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# dev.off()





  
  

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pdf("FigureS3L.pdf", width = 4, height = 3)





  
  

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plot.DotPlot(scmat[,grepl("HSC|MPP|MEP|CMP|GMP|Mono|Eryth", scmat[["SingleR.label2"]][,1])], group.by = "SingleR.label2", features = c("DPPA4","CRHBP","CSF3R","BEX1","EREG", "CA1", "HBD", "HEMGN", "MYL4", "SNCA"), cols = c("white", "red"), dot.scale = 6, scale.max = 50, scale.min = 0)





  
  

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dev.off()