Merge pull request #15 from PNNL-CompBio/matrisome-updates

Matrisome updates

fig4ab_plotGeneExpression.R

@@ -31,7 +31,7 @@ bannotes<-bannotes[pat2,]
 mat<-mat[,pat2]
 
 
-pheatmap(cor(mat,method='spearman'),
+pheatmap(cor(mat,method='pearson'),
          annotation_col = bannotes,#%>%
          annotation_row= bannotes,#%>%
          clustering_distance_rows = 'correlation',
@@ -79,18 +79,32 @@ p2<-restab%>%as.data.frame()%>%tibble::rownames_to_column('cNF Sample')%>%
   left_join(cnfs)%>%
   dplyr::mutate(`Biobank Patient`=stringr::str_replace_all(patient,'tumor[0-9]*',''))%>%
   dplyr::mutate(`Biobank Patient`=stringr::str_replace_all(`Biobank Patient`,'patient','Patient '))%>%
-  dplyr::mutate(`Biobank Patient`=factor(`Biobank Patient`,levels=rev(c('Patient 1','Patient 2','Patient 3','Patient 4','Patient 5','Patient 6',
-                                                                 'Patient 8','Patient 9','Patient 10','Patient 11','Patient 13'))))
+  dplyr::mutate(`Biobank Patient`=factor(`Biobank Patient`,levels=c('Patient 1','Patient 2','Patient 3','Patient 4','Patient 5','Patient 6',
+                                                                 'Patient 8','Patient 9','Patient 10','Patient 11','Patient 13')))
+p2$Extras=rep("None",nrow(p2))
+p2$Extras[which(p2$Media=='Tumor')]<-'Tumor'
+p2$Extras[intersect(which(p2$Cytokines=='TRUE'),which(p2$Forskolin=='TRUE'))]<-'F+C'
+p2$Extras[intersect(which(p2$Cytokines=='TRUE'),which(p2$Forskolin=='FALSE'))]<-'C only'
+p2$Extras[intersect(which(p2$Cytokines=='FALSE'),which(p2$Forskolin=='TRUE'))]<-'F only'
 
 p3<-p2%>%
-  rowwise()%>%
-  #dplyr::mutate(extras=ifelse(Media=="Tumor"&&extras=="None","Tumor",extras))%>%
   ggplot(aes(x=`Biobank Patient`,y=Similarity,fill=Media))+geom_boxplot(outlier.shape=NA)+
   scale_fill_manual(values=media_pal)+
-  facet_grid(Forskolin+Cytokines~.)
-  #coord_flip()
+  facet_grid(Extras~.)+
+  theme_classic()
+
+
+
+p4<-p2%>%
+  ggplot(aes(x=`Biobank Patient`,y=Similarity,fill=Media))+geom_boxplot(outlier.shape=NA)+
+  scale_fill_manual(values=media_pal)+
+  facet_grid(Extras~.)+
+  scale_y_continuous(limits=c(0.5,1))+
+  theme_classic()
 
 ggsave('fig4b_organoidCorrelation.pdf',p3,height=12,width=10)
+ggsave('fig4b_organoidCorrelation_largerY.pdf',p4,height=12,width=10)
+
 write.csv(p2,file='fig4b_data.csv',row.names=F)
 
 ##now let's do the deconvolution

fig4cd_tumorDeconv.R

@@ -9,6 +9,13 @@ source("orgPlottingFunctions.R")
 source("loadExpFromCounts.R")
 #source("loadOrganoidData.R")
 library(ggplot2)
+library(GSVA)
+
+
+##load up one more annotation file
+bannotes[which(bannotes$individualID=='NF0007'),'individualID']<-
+  sapply(rownames(bannotes[which(bannotes$individualID=='NF0007'),]),function(x)
+    unlist(stringr::str_split(x,pattern='[ |-][M|D|S]'))[1])
 
 #' runImmuneDeconv
 #' @param tab

orgPlottingFunctions.R

@@ -23,8 +23,10 @@ pal = c(wesanderson::wes_palette("Chevalier1"),
 media_pal = wesanderson::wes_palette('Darjeeling1')[1:4]
 names(media_pal)<-c('Mammo','DMEM','StemPro','Tumor')
 
-org_pal = c("#212155","#2B3A8D","#3375B7","#6EC6EA","#CDE8F4")
-names(org_pal)<-c("NF0009","NF0012","NF0002","NF0007","NF0008")
+org_pal = c("#212155","#2B3A8D","#3375B7","#6EC6EA","#CDE8F4","#6EC6EA","#9EC6EA")
+names(org_pal)<-c("NF0009","NF0012","NF0002","NF0007","NF0008","NF0007-4","NF0007-2")
+
+
 
 ctf_pal <- c("#27194D","#453286","#5D509D","#7165A9","#938ABE","#BFB7D9","#D1B4D3",
              "#BF8CBA","#AE5E9F","#873180","#572455")