Cluster composition P0 Gmnc KO

This is an analysis of the contribution of WT and Gmnc KO cells to distinct clusters. I start from a Seurat object generated previouly.

Load libraries

library(Seurat)
library(cowplot)
library(dplyr)
library(ggplot2)
library(ggExtra)
library(ggrepel)
library(reticulate)
library(Matrix)
library(viridis)
library(RColorBrewer)
library(MetBrewer)
library(wesanderson)
library(R.utils)

# Set ggplot theme as classic
theme_set(theme_classic())

Load the dataset

Gmnc.cleanup <- readRDS("Gmnc.cleanup.RDS")

DimPlot(Gmnc.cleanup, reduction = "Spring", pt.size = 0.2, label = T, label.size = 3, label.box = T, repel = T, cols = met.brewer("Klimt", 23)) + NoAxes() 

# Cluster composition

df <- as.data.frame(table(Gmnc.cleanup$seurat_clusters,Gmnc.cleanup$orig.ident))
colnames(df) <- c("Cluster", "Genotype", "ncells")

pct.WT <- prop.table(table(Gmnc.cleanup$orig.ident))[2]

p1 <- ggplot(df, aes(fill=Genotype, y=ncells, x=Cluster)) + 
    geom_bar(position="stack", stat="identity") + scale_fill_manual(values = met.brewer("Egypt", 4)) + NoLegend()

p2 <- ggplot(df, aes(fill=Genotype, y=ncells, x=Cluster)) +
  geom_bar(position="fill", stat="identity") + 
  scale_fill_manual(values = met.brewer("Egypt", 4)) +
  geom_hline(yintercept = pct.WT, colour = "black", linetype = 2) +
  geom_text(aes(label = paste0("n=",ncells)), position = position_fill(vjust = 0.5), colour = "black", size = 3, angle=90) +       ylab("% cells")
p1+p2

# Create a supertype class

Gmnc.cleanup@meta.data$Supertype[Gmnc.cleanup$seurat_clusters %in% c(2,8)] <- "Inhib.N"
Gmnc.cleanup@meta.data$Supertype[Gmnc.cleanup$seurat_clusters %in% c(0, 4, 9, 10, 13)] <- "Excit.N"
Gmnc.cleanup@meta.data$Supertype[Gmnc.cleanup$seurat_clusters %in% c(6)] <- "OPC"
Gmnc.cleanup@meta.data$Supertype[Gmnc.cleanup$seurat_clusters %in% c(3, 5, 11)] <- "Cycling prog."
Gmnc.cleanup@meta.data$Supertype[Gmnc.cleanup$seurat_clusters %in% c(14, 15, 16, 18, 20, 22)] <- "Non-neuro"
Gmnc.cleanup@meta.data$Supertype[Gmnc.cleanup$seurat_clusters %in% c(1, 7, 12, 17, 19)] <- "Astro/Epend"
#Gmnc.cleanup@meta.data$Supertype[Gmnc.cleanup$seurat_clusters %in% c(21)] <- "Doublets/lowQ"

DimPlot(Gmnc.cleanup, reduction = "Spring", group.by = "Supertype", pt.size = 0.2, label = F, label.size = 3, cols = met.brewer("Archambault", 6)) + NoAxes() 

Supertype composition

df1 <- as.data.frame(table(Gmnc.cleanup$Supertype,Gmnc.cleanup$orig.ident))
colnames(df1) <- c("Supertype", "Genotype", "ncells")

p1 <- ggplot(df1, aes(fill=Genotype, y=ncells, x=Supertype)) + 
    geom_bar(position="stack", stat="identity") + scale_fill_manual(values = met.brewer("Egypt", 4)) + 
    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) + NoLegend()

p2 <- ggplot(df1, aes(fill=Genotype, y=ncells, x=Supertype)) +
  geom_bar(position="fill", stat="identity") + 
  scale_fill_manual(values = met.brewer("Egypt", 4)) +
  geom_hline(yintercept = pct.WT, colour = "black", linetype = 2) +
  geom_text(aes(label = paste0("n=",ncells)), position = position_fill(vjust = 0.5), colour = "black", size = 3, angle=90) +       ylab("% cells") + 
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))

p1+p2

Cell cycle phases

df2 <- as.data.frame(table(Gmnc.cleanup$Phase,Gmnc.cleanup$orig.ident))
colnames(df2) <- c("Phase", "Genotype", "ncells")

p1 <- ggplot(df2, aes(fill=Genotype, y=ncells, x=Phase)) + 
    geom_bar(position="stack", stat="identity") + scale_fill_manual(values = met.brewer("Egypt", 4)) + NoLegend()

p2 <- ggplot(df2, aes(fill=Genotype, y=ncells, x=Phase)) +
  geom_bar(position="fill", stat="identity") + 
  scale_fill_manual(values = met.brewer("Egypt", 4)) +
  geom_hline(yintercept = pct.WT, colour = "black", linetype = 2) +
  geom_text(aes(label = paste0("n=",ncells)), position = position_fill(vjust = 0.5), colour = "black", size = 3, angle=90) +       ylab("% cells")

p1+p2

Non-neuronal cells

#Subset non neuronal cells
non.neuro <- subset(Gmnc.cleanup,subset = Supertype == "Non-neuro")
non.neuro <- FindVariableFeatures(non.neuro, selection.method = "vst", nfeatures = 2000)
non.neuro <- ScaleData(non.neuro, vars.to.regress = "percent.mt")
non.neuro <- RunPCA(non.neuro, features = VariableFeatures(object = non.neuro))
non.neuro <- RunUMAP(non.neuro, dims = 1:20)

#Save previous cluster number and display on new UMAP coordinates
non.neuro$prev.cluster <- non.neuro$seurat_clusters
DimPlot(non.neuro, reduction = "umap", label = T, label.size = 3, label.box = T, repel = T, pt.size = 0.4, group.by = "prev.cluster",  cols = met.brewer("Klimt", 23)[c(15, 16, 17, 18, 21, 23)]) + NoAxes()

#Perform new clustering
non.neuro <- FindNeighbors(non.neuro, dims = 1:20)
non.neuro <- FindClusters(non.neuro, resolution = 0.2, verbose = F)
DimPlot(non.neuro, reduction = "umap", label = T, label.size = 3, label.box = T, repel = T, pt.size = 0.4, group.by = "seurat_clusters",  cols = met.brewer("NewKingdom", 9)) + NoAxes()

#Plot marker genes for annotation
VlnPlot(non.neuro, features=c("C1qa", "Csf1r", "Lyve1", "Siglech","Mki67", "Foxc1", "Kcnj8", "Pecam1", "Lum", "Slc6a13"),cols = met.brewer("NewKingdom", 9), pt.size = 0, stack = T, flip = T, fill.by = "ident") & NoLegend()

FeaturePlot(non.neuro, features=c("C1qa", "Csf1r", "Lyve1", "Siglech","Mki67", "Foxc1", "Kcnj8", "Pecam1", "Lum", "Slc6a13"), ncol=3, reduction = "umap", order = F, pt.size = 0.2) & scale_color_gradientn(colors=c("grey90", brewer.pal(9,"YlGnBu"))) & NoLegend() & NoAxes()

#Cluster composition
DimPlot(non.neuro, reduction = "umap", label = F, label.size = 2, label.box = T, repel = T, pt.size = 0.4, group.by = "orig.ident", cols = met.brewer("Egypt", 4)) + NoAxes()

df3 <- as.data.frame(table(non.neuro$seurat_clusters, non.neuro$orig.ident))
colnames(df3) <- c("Cluster", "Genotype", "ncells")

p1 <- ggplot(df3, aes(fill=Genotype, y=ncells, x=Cluster)) + 
    geom_bar(position="stack", stat="identity") + scale_fill_manual(values = met.brewer("Egypt", 4)) + NoLegend()

p2 <- ggplot(df3, aes(fill=Genotype, y=ncells, x=Cluster)) +
  geom_bar(position="fill", stat="identity") + 
  scale_fill_manual(values = met.brewer("Egypt", 4)) +
  geom_hline(yintercept = pct.WT, colour = "black", linetype = 2) +
  geom_text(aes(label = paste0("n=",ncells)), position = position_fill(vjust = 0.5), colour = "black", size = 3, angle=90) +       ylab("% cells")

p1+p2

Inhibitory neurons

#Subset inhibitory neurons
Inhib.N <- subset(Gmnc.cleanup,subset = Supertype == "Inhib.N")
Inhib.N <- FindVariableFeatures(Inhib.N, selection.method = "vst", nfeatures = 2000)
Inhib.N <- ScaleData(Inhib.N, vars.to.regress = "percent.mt")
Inhib.N <- RunPCA(Inhib.N, features = VariableFeatures(object = Inhib.N))
Inhib.N <- RunUMAP(Inhib.N, dims = 1:20)

#Save previous cluster number and display on new UMAP coordinates
Inhib.N$prev.cluster <- Inhib.N$seurat_clusters
DimPlot(Inhib.N, reduction = "umap", label = T, label.size = 3, label.box = T, repel = T, pt.size = 0.4, group.by = "prev.cluster",  cols = met.brewer("Klimt", 23)[c(3, 9)]) + NoAxes()

#Perform new clustering
Inhib.N <- FindNeighbors(Inhib.N, dims = 1:20)
Inhib.N <- FindClusters(Inhib.N, resolution = 0.2, verbose = F)
DimPlot(Inhib.N, reduction = "umap", label = T, label.size = 3, label.box = T, repel = T, pt.size = 0.4, group.by = "seurat_clusters",  cols = met.brewer("Hokusai3", 9)) + NoAxes()

#Plot marker genes for annotation
VlnPlot(Inhib.N, features=c("Lhx6", "Foxp1", "Zfp503", "Ebf1", "Isl1", "Htr3a", "Prox1", "Sp8", "Sp9"),cols = met.brewer("Hokusai3", 9), pt.size = 0, stack = T, flip = T, fill.by = "ident") & NoLegend()

FeaturePlot(Inhib.N, features=c("Lhx6", "Foxp1", "Zfp503", "Htr3a", "Prox1", "Ebf1", "Isl1", "Sp8", "Sp9"), ncol=3, reduction = "umap", order = F, pt.size = 0.2) & scale_color_gradientn(colors=c("grey90", brewer.pal(9,"YlGnBu"))) & NoLegend() & NoAxes()

#Cluster composition
DimPlot(Inhib.N, reduction = "umap", label = F, label.size = 2, pt.size = 0.4, group.by = "orig.ident", cols = met.brewer("Egypt", 4)) + NoAxes()

df4 <- as.data.frame(table(Inhib.N$seurat_clusters, Inhib.N$orig.ident))
colnames(df4) <- c("Cluster", "Genotype", "ncells")

p1 <- ggplot(df4, aes(fill=Genotype, y=ncells, x=Cluster)) + 
    geom_bar(position="stack", stat="identity") + scale_fill_manual(values = met.brewer("Egypt", 4)) + NoLegend()

p2 <- ggplot(df4, aes(fill=Genotype, y=ncells, x=Cluster)) +
  geom_bar(position="fill", stat="identity") + 
  scale_fill_manual(values = met.brewer("Egypt", 4)) +
  geom_hline(yintercept = pct.WT, colour = "black", linetype = 2) +
  geom_text(aes(label = paste0("n=",ncells)), position = position_fill(vjust = 0.5), colour = "black", size = 3, angle=90) +       ylab("% cells")

p1+p2

Excitatory neurons

#Subset excitatory neurons
Excit.N <- subset(Gmnc.cleanup,subset = Supertype == "Excit.N")
Excit.N <- FindVariableFeatures(Excit.N, selection.method = "vst", nfeatures = 2000)
Excit.N <- ScaleData(Excit.N, vars.to.regress = "percent.mt")
Excit.N <- RunPCA(Excit.N, features = VariableFeatures(object = Excit.N))
Excit.N <- RunUMAP(Excit.N, dims = 1:20)

#Save previous cluster number and display on new UMAP coordinates
Excit.N$prev.cluster <- Excit.N$seurat_clusters
DimPlot(Excit.N, reduction = "umap", label = T, label.size = 3, label.box = T, repel = T, pt.size = 0.4, group.by = "prev.cluster",  cols = met.brewer("Klimt", 23)[c(1, 5, 10, 11, 14)]) + NoAxes()

#Perform new clustering
Excit.N <- FindNeighbors(Excit.N, dims = 1:20)
Excit.N <- FindClusters(Excit.N, resolution = 0.2, verbose = F)
DimPlot(Excit.N, reduction = "umap", label = T, label.size = 3, label.box = T, repel = T, pt.size = 0.4, group.by = "seurat_clusters",  cols = met.brewer("OKeeffe2", 8)) + NoAxes()

#Plot marker genes for annotation
VlnPlot(Excit.N, features=c("Trp73", "Reln", "Hs3st4", "Tcerg1l", "Fezf2", "Pou3f2", "Satb2", "Tbr1", "Eomes", "Prox1" ,"Gad2", "Alas2"),cols = met.brewer("OKeeffe2", 8), pt.size = 0, stack = T, flip = T, fill.by = "ident") & NoLegend()

FeaturePlot(Excit.N, features=c("Trp73", "Reln", "Hs3st4", "Tcerg1l", "Fezf2", "Pou3f2", "Satb2", "Tbr1", "Eomes", "Prox1", "Gad2", "Alas2"), ncol=3, reduction = "umap", order = F, pt.size = 0.2) & scale_color_gradientn(colors=c("grey90", brewer.pal(9,"YlGnBu"))) & NoLegend() & NoAxes()

#Cluster composition
DimPlot(Excit.N, reduction = "umap", label = F, label.size = 2, pt.size = 0.4, group.by = "orig.ident", cols = met.brewer("Egypt", 4)) + NoAxes()

df5 <- as.data.frame(table(Excit.N$seurat_clusters, Excit.N$orig.ident))
colnames(df5) <- c("Cluster", "Genotype", "ncells")

p1 <- ggplot(df5, aes(fill=Genotype, y=ncells, x=Cluster)) + 
    geom_bar(position="stack", stat="identity") + scale_fill_manual(values = met.brewer("Egypt", 4)) + NoLegend()

p2 <- ggplot(df5, aes(fill=Genotype, y=ncells, x=Cluster)) +
  geom_bar(position="fill", stat="identity") + 
  scale_fill_manual(values = met.brewer("Egypt", 4)) +
  geom_hline(yintercept = pct.WT, colour = "black", linetype = 2) +
  geom_text(aes(label = paste0("n=",ncells)), position = position_fill(vjust = 0.5), colour = "black", size = 3, angle=90) +       ylab("% cells")

p1+p2

Astroependymal cells

#Subset astroependymal cells
Astro.Epend <- subset(Gmnc.cleanup,subset = Supertype == "Astro/Epend")
Astro.Epend <- FindVariableFeatures(Astro.Epend, selection.method = "vst", nfeatures = 2000)
Astro.Epend <- ScaleData(Astro.Epend, vars.to.regress = "percent.mt")
Astro.Epend <- RunPCA(Astro.Epend, features = VariableFeatures(object = Astro.Epend))
Astro.Epend <- RunUMAP(Astro.Epend, dims = 1:20)

#Save previous cluster number and display on new UMAP coordinates
Astro.Epend$prev.cluster <- Astro.Epend$seurat_clusters
DimPlot(Astro.Epend, reduction = "umap", label = T, label.size = 3, label.box = T, repel = T, pt.size = 0.4, group.by = "prev.cluster",  cols = met.brewer("Klimt", 23)[c(2, 8, 13, 18, 20)]) + NoAxes()

#Perform new clustering
Astro.Epend <- FindNeighbors(Astro.Epend, dims = 1:20)
Astro.Epend <- FindClusters(Astro.Epend, resolution = 0.2, verbose = F)
DimPlot(Astro.Epend, reduction = "umap", label = T, label.size = 3, label.box = T, repel = T, pt.size = 0.4, group.by = "seurat_clusters",  cols = met.brewer("Hokusai1", 9)) + NoAxes()

#Plot marker genes for annotation
VlnPlot(Astro.Epend, features=c("Gfap", "Aqp4", "Id3", "Veph1", "Lhx9","Gad2", "Slc17a6", "Foxj1", "Trp73", "Lmx1a"),cols = met.brewer("Hokusai1", 9), pt.size = 0, stack = T, flip = T, fill.by = "ident") & NoLegend()

FeaturePlot(Astro.Epend, features=c("Gfap", "Aqp4", "Id3", "Veph1", "Lhx9","Gad2", "Slc17a6", "Foxj1", "Trp73", "Lmx1a"), ncol=3, reduction = "umap", order = F, pt.size = 0.2) & scale_color_gradientn(colors=c("grey90", brewer.pal(9,"YlGnBu"))) & NoLegend() & NoAxes()

#Cluster composition
DimPlot(Astro.Epend, reduction = "umap", label = F, label.size = 2, pt.size = 0.4, group.by = "orig.ident", cols = met.brewer("Egypt", 4)) + NoAxes()

df6 <- as.data.frame(table(Astro.Epend$seurat_clusters, Astro.Epend$orig.ident))
colnames(df6) <- c("Cluster", "Genotype", "ncells")

p1 <- ggplot(df6, aes(fill=Genotype, y=ncells, x=Cluster)) + 
    geom_bar(position="stack", stat="identity") + scale_fill_manual(values = met.brewer("Egypt", 4)) + NoLegend()

p2 <- ggplot(df6, aes(fill=Genotype, y=ncells, x=Cluster)) +
  geom_bar(position="fill", stat="identity") + 
  scale_fill_manual(values = met.brewer("Egypt", 4)) +
  geom_hline(yintercept = pct.WT, colour = "black", linetype = 2) +
  geom_text(aes(label = paste0("n=",ncells)), position = position_fill(vjust = 0.5), colour = "black", size = 3, angle=90) +       ylab("% cells")

p1+p2

Session Info

#date
format(Sys.time(), "%d %B, %Y, %H:%M")

## [1] "11 January, 2024, 16:22"

#Packages used
sessionInfo()

## R version 4.1.1 (2021-08-10)
## Platform: x86_64-conda-linux-gnu (64-bit)
## Running under: CentOS Linux 7 (Core)
## 
## Matrix products: default
## BLAS/LAPACK: /shared/ifbstor1/software/miniconda/envs/r-4.1.1/lib/libopenblasp-r0.3.18.so
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] R.utils_2.11.0     R.oo_1.24.0        R.methodsS3_1.8.1  wesanderson_0.3.6 
##  [5] MetBrewer_0.2.0    RColorBrewer_1.1-3 viridis_0.6.2      viridisLite_0.4.1 
##  [9] Matrix_1.6-4       reticulate_1.24    ggrepel_0.9.1      ggExtra_0.9       
## [13] ggplot2_3.3.6      dplyr_1.0.10       cowplot_1.1.1      Seurat_5.0.1      
## [17] SeuratObject_5.0.1 sp_2.1-2          
## 
## loaded via a namespace (and not attached):
##   [1] Rtsne_0.16             colorspace_2.0-3       deldir_1.0-6          
##   [4] ellipsis_0.3.2         ggridges_0.5.3         RcppHNSW_0.3.0        
##   [7] spatstat.data_3.0-3    rstudioapi_0.13        farver_2.1.1          
##  [10] leiden_0.3.10          listenv_0.8.0          RSpectra_0.16-1       
##  [13] fansi_1.0.3            codetools_0.2-18       splines_4.1.1         
##  [16] knitr_1.40             polyclip_1.10-0        spam_2.7-0            
##  [19] jsonlite_1.8.2         ica_1.0-2              cluster_2.1.2         
##  [22] png_0.1-7              uwot_0.1.11            spatstat.sparse_3.0-3 
##  [25] shiny_1.7.1            sctransform_0.4.1      compiler_4.1.1        
##  [28] httr_1.4.4             assertthat_0.2.1       fastmap_1.1.0         
##  [31] lazyeval_0.2.2         cli_3.4.1              later_1.3.0           
##  [34] htmltools_0.5.2        tools_4.1.1            igraph_1.3.1          
##  [37] dotCall64_1.0-1        gtable_0.3.1           glue_1.6.2            
##  [40] RANN_2.6.1             reshape2_1.4.4         Rcpp_1.0.9            
##  [43] scattermore_1.2        jquerylib_0.1.4        vctrs_0.4.2           
##  [46] nlme_3.1-153           spatstat.explore_3.2-5 progressr_0.10.0      
##  [49] lmtest_0.9-40          spatstat.random_3.2-2  xfun_0.34             
##  [52] stringr_1.4.1          globals_0.14.0         mime_0.12             
##  [55] miniUI_0.1.1.1         lifecycle_1.0.3        irlba_2.3.5.1         
##  [58] goftest_1.2-3          future_1.25.0          MASS_7.3-54           
##  [61] zoo_1.8-10             scales_1.2.1           spatstat.utils_3.0-4  
##  [64] promises_1.2.0.1       parallel_4.1.1         yaml_2.3.6            
##  [67] pbapply_1.5-0          gridExtra_2.3          sass_0.4.1            
##  [70] stringi_1.7.8          highr_0.9              fastDummies_1.7.3     
##  [73] rlang_1.0.6            pkgconfig_2.0.3        matrixStats_0.62.0    
##  [76] evaluate_0.17          lattice_0.20-45        tensor_1.5            
##  [79] ROCR_1.0-11            purrr_0.3.5            labeling_0.4.2        
##  [82] patchwork_1.1.1        htmlwidgets_1.5.4      tidyselect_1.2.0      
##  [85] parallelly_1.31.1      RcppAnnoy_0.0.19       plyr_1.8.7            
##  [88] magrittr_2.0.3         R6_2.5.1               generics_0.1.3        
##  [91] DBI_1.1.2              withr_2.5.0            pillar_1.8.1          
##  [94] fitdistrplus_1.1-8     abind_1.4-5            survival_3.2-13       
##  [97] tibble_3.1.8           future.apply_1.9.0     crayon_1.5.2          
## [100] KernSmooth_2.23-20     utf8_1.2.2             spatstat.geom_3.2-7   
## [103] plotly_4.10.0          rmarkdown_2.11         grid_4.1.1            
## [106] data.table_1.14.2      digest_0.6.30          xtable_1.8-4          
## [109] tidyr_1.2.1            httpuv_1.6.5           munsell_0.5.0         
## [112] bslib_0.3.1

---

1. IPNP & Imagine Institute, Paris, France, frederic.causeret@inserm.fr↩︎