Sub-pallial neurons Fig.S2

Load libraries and QCFiltered dataset

# load libraries
library(Seurat)
library(scrattch.hicat)
library(scrattch.vis)
library(RColorBrewer)
library(dendextend)
library(dplyr)
library(matrixStats)
library(Matrix)


# Set ggplot theme
theme_set(theme_classic())

# Load the full annotated dataset
Allcells.data <- readRDS("./Clustered.cells.RDS")

# Extract Subpallial neuron clusters
gaba.clusters <- paste0( c("LN.GABA."),c("1", "7", "8", "9", "10", "11"))
GABA.LN.data <- SubsetData(Allcells.data, ident.use = gaba.clusters, subset.raw = T,  do.clean = F)

# Exclude one outlier cell on Spring coordinates
outlier <- rownames(subset(GABA.LN.data@dr$spring@cell.embeddings, GABA.LN.data@dr$spring@cell.embeddings[,2] == max(GABA.LN.data@dr$spring@cell.embeddings[,2])))
Celltokeep <- GABA.LN.data@meta.data$Barcodes[GABA.LN.data@meta.data$Barcodes != outlier]
GABA.LN.data <- SubsetData(GABA.LN.data, cells.use =  Celltokeep , subset.raw = T,  do.clean = F)

rm(list = ls()[!ls() %in% c("GABA.LN.data")])

Fig S2 A

# Load the full dataset
Allcells.data <- readRDS("./QC.filtered.cells.RDS")

# Transfer the identities
Rename.Clust <-  function(Clustdata, RawQCdata) {
  unClustered.cells <- RawQCdata@meta.data$Barcodes
  RawQCdata <- SetIdent(RawQCdata, cells.use = unClustered.cells, ident.use = "All.Unclustered.Cells")
  
  for(i in unique(Clustdata@meta.data$Cluster.ident)){
  New.ident <- i
  Barcodes <- rownames(subset(Clustdata@meta.data, Clustdata@meta.data$Cluster.ident == i))
  print(paste0("Cluster_",i,": ",length(Barcodes), " Cells"))
  Barcodes <- Barcodes[Barcodes %in% rownames(RawQCdata@meta.data)]
  RawQCdata <- SetIdent(RawQCdata, cells.use = Barcodes ,ident.use = paste0("LN.Glut.",i))
  }
  return(RawQCdata)
}

Allcells.data <- Rename.Clust(Clustdata = GABA.LN.data, RawQCdata = Allcells.data)

## [1] "Cluster_LN.GABA.1: 26 Cells"
## [1] "Cluster_LN.GABA.7: 45 Cells"
## [1] "Cluster_LN.GABA.8: 23 Cells"
## [1] "Cluster_LN.GABA.9: 16 Cells"
## [1] "Cluster_LN.GABA.10: 112 Cells"
## [1] "Cluster_LN.GABA.11: 54 Cells"

DimPlot(Allcells.data,
        reduction.use = "spring", 
        dim.1 = 1,
        dim.2 = 2,
        do.label=T,
        label.size = 2,
        no.legend = T,
        cols.use = c("#969696","#ec756d", "#c773a7", "#7293c8", "#b79f0b", "#3ca73f","#31b6bd"))

Fig. S2 B

Annotation data.frame

colors <- c("#c773a7", "#b79f0b", "#3ca73f", "#31b6bd", "#ec756d", "#7293c8")

# Prepare annotation for hicat pipeline
colorsident <- cbind(ident = unique(as.character(GABA.LN.data@ident)),
                     colors = colors,
                     id = unique(as.character(GABA.LN.data@ident)))

# Create annotation data.frame
anno.df <- as.data.frame(cbind(
  sample_name = row.names(GABA.LN.data@meta.data),
  primary_type_id = colorsident[match(as.character(GABA.LN.data@ident), colorsident[,1]),3],
  primary_type_label = as.character(GABA.LN.data@ident),
  primary_type_color = colorsident[match(as.character(GABA.LN.data@ident), colorsident[,1]),2]
))

# Make a data.frame of unique cluster id, type, color, and broad type
cl.df <- anno.df %>%
         select(primary_type_id, primary_type_label, primary_type_color) %>%
         unique()

colnames(cl.df)[1:3] <- c("cluster_id", "cluster_label", "cluster_color")

# Sort by cluster_id
cl.df <- arrange(cl.df, cluster_id)
row.names(cl.df) <- cl.df$cluster_id

cl.fact <- setNames(factor(anno.df$primary_type_id), anno.df$sample_name)

Expression matrix

# Filter genes
num.cells <- Matrix::rowSums(GABA.LN.data@data > 0) 
genes.use <- names(x = num.cells[which(x = num.cells >= 10)]) 
GenesToRemove <- c(grep(pattern = "(^Rpl|^Rps|^Mrp)", x = genes.use, value = TRUE), grep(pattern = "^mt-", x = genes.use, value = TRUE), "Xist")

genes.use <- genes.use[!genes.use %in% GenesToRemove] ; rm(GenesToRemove, num.cells)

GABA.LN.data@raw.data <- GABA.LN.data@raw.data[genes.use, ]
GABA.LN.data <- NormalizeData(object = GABA.LN.data,
                                      normalization.method = "LogNormalize", 
                                      scale.factor = round(median(GABA.LN.data@meta.data$nUMI)),
                                      display.progress = F)

# Find all var genes
GABA.LN.data <- FindVariableGenes(object = GABA.LN.data,
                                          mean.function = ExpMean,
                                          dispersion.function = LogVMR,
                                          x.low.cutoff = 0.02,
                                          x.high.cutoff = 3,
                                          y.cutoff = 1, 
                                          do.plot = F, display.progress = F)

dgeMatrix_count <- as.matrix(GABA.LN.data@raw.data)[rownames(GABA.LN.data@raw.data) %in% GABA.LN.data@var.genes,]
dgeMatrix_cpm <- cpm(dgeMatrix_count) 
norm.dat <- log2(dgeMatrix_cpm + 1) ; rm(dgeMatrix_cpm)

Data.matrix <- list(raw.dat=dgeMatrix_count, norm.dat=norm.dat) ; attach(Data.matrix)

rm(dgeMatrix_count,norm.dat)

Hierarchical clustering

Scrattch.hicat perform hierarchical clustering on a cluster correlation matrix based on median expression values for the top 50 most DEGs between every pair of clusters. It estimates branch confidence level using a bootstrap approach implemented by the pvclust package

# Take median expression over cluster
cl.med <- get_cl_medians(norm.dat, cl.fact)

# Build the dendrogram
dend.result <- build_dend(cl.med[,levels(cl.fact)],
                          l.color= setNames(as.character(cl.df$cluster_color), row.names(cl.df)),
                          nboot = 100)

## Bootstrap (r = 0.5)... Done.
## Bootstrap (r = 0.6)... Done.
## Bootstrap (r = 0.7)... Done.
## Bootstrap (r = 0.8)... Done.
## Bootstrap (r = 0.9)... Done.
## Bootstrap (r = 1.0)... Done.
## Bootstrap (r = 1.1)... Done.
## Bootstrap (r = 1.2)... Done.
## Bootstrap (r = 1.3)... Done.
## Bootstrap (r = 1.4)... Done.

# Attach cluster labels to the leaves of the tree
dend.labeled <- dend.result$dend
labels(dend.labeled) <- cl.df[labels(dend.labeled), "cluster_label"]

# Rotate dendrogramme leafs
NewOrder <- paste0("LN.GABA.", c(1,11,8,7,10,9))
cl.fact <- factor(cl.fact, levels = NewOrder)
l.rank <- setNames(1:nrow(cl.df), NewOrder) #set the cluster order 

# Color of the leaf nodes.
l.color <- setNames(as.character(cl.df$cluster_color), row.names(cl.df)) 

dend.result <- build_dend(cl.med[,levels(cl.fact)],
                          l.rank,
                          l.color=l.color,
                          nboot = 100) 

## Bootstrap (r = 0.5)... Done.
## Bootstrap (r = 0.6)... Done.
## Bootstrap (r = 0.7)... Done.
## Bootstrap (r = 0.8)... Done.
## Bootstrap (r = 0.9)... Done.
## Bootstrap (r = 1.0)... Done.
## Bootstrap (r = 1.1)... Done.
## Bootstrap (r = 1.2)... Done.
## Bootstrap (r = 1.3)... Done.
## Bootstrap (r = 1.4)... Done.

dend <- dend.result$dend

dend.labeled <- dend.result$dend
labels(dend.labeled) <- cl.df[labels(dend.labeled), "cluster_label"]

plot(dend.labeled) 

Build the markers barplot

data <- cbind(sample_name = colnames(GABA.LN.data@data),
              as.data.frame(t(as.matrix(GABA.LN.data@data))))

Selected.markers <- c("Foxg1","Bcl11b","Gad1", "Gad2","Dlx1","Dlx2",
                           "Dlx5","Dlx6", "Lhx6","Sst","Calb1","Gm17750",
                           "Npy", "Nxph1", "Rprm", "Elmo1","Arx","Meis2",
                           "Rbfox1","Zfp503","Pou3f1", "Isl1", "Ebf1",
                           "Dlc1","Dlk1","Nefm","Nefl","Sybu","Lypd1",
                           "Parvb", "Fgf15", "Zic1", "Th","Pnoc", "Crabp2",
                           "Klhdc8b","Pax6", "Sp8","Six3","Tshz2", "Phlda1")

sample_bar_plot(data, 
                anno.df, 
                genes = Selected.markers,
                group_order = levels(cl.fact),
                grouping = "primary_type",
                log_scale = FALSE,
                font_size = 7,
                label_height = 10,
                label_type = "angle",
                bg_color ="#f7f7f7")

Manuscript Fig. S2B

Fig. S2 C

FeaturePlot(object = GABA.LN.data,
            features.plot = c("Pax6", "Sp8", "Six3", "Isl1", "Ebf1", "Dlk1", "Tshz2", "Th", "Lhx6"),
            cols.use = c("grey90", brewer.pal(9,"YlGnBu")),
            reduction.use = "spring",
            no.legend = T,
            nCol = 3,
            overlay = F,
            dark.theme = F)

Manuscript Fig. S2C

Session Info

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

## [1] "30 novembre, 2020, 11,36"

#Packages used
sessionInfo()

## R version 3.6.3 (2020-02-29)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.5 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/atlas/libblas.so.3.10.3
## LAPACK: /usr/lib/x86_64-linux-gnu/atlas/liblapack.so.3.10.3
## 
## locale:
##  [1] LC_CTYPE=fr_FR.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=fr_FR.UTF-8        LC_COLLATE=fr_FR.UTF-8    
##  [5] LC_MONETARY=fr_FR.UTF-8    LC_MESSAGES=fr_FR.UTF-8   
##  [7] LC_PAPER=fr_FR.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=fr_FR.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] pvclust_2.2-0         matrixStats_0.55.0    dendextend_1.12.0    
##  [4] RColorBrewer_1.1-2    scrattch.vis_0.0.210  purrr_0.3.3          
##  [7] ggbeeswarm_0.6.0      dplyr_0.8.3           scrattch.hicat_0.0.16
## [10] Seurat_2.3.4          Matrix_1.2-17         cowplot_1.0.0        
## [13] ggplot2_3.2.1        
## 
## loaded via a namespace (and not attached):
##   [1] Rtsne_0.15          colorspace_1.4-1    class_7.3-17       
##   [4] modeltools_0.2-22   ggridges_0.5.1      mclust_5.4.5       
##   [7] htmlTable_1.13.2    base64enc_0.1-3     rstudioapi_0.11    
##  [10] proxy_0.4-23        farver_2.0.1        npsurv_0.4-0       
##  [13] flexmix_2.3-15      bit64_4.0.2         codetools_0.2-16   
##  [16] splines_3.6.3       R.methodsS3_1.7.1   lsei_1.2-0         
##  [19] robustbase_0.93-5   knitr_1.26          zeallot_0.1.0      
##  [22] jsonlite_1.7.0      Formula_1.2-3       ica_1.0-2          
##  [25] cluster_2.1.0       kernlab_0.9-29      png_0.1-7          
##  [28] R.oo_1.23.0         compiler_3.6.3      httr_1.4.1         
##  [31] backports_1.1.5     assertthat_0.2.1    lazyeval_0.2.2     
##  [34] lars_1.2            acepack_1.4.1       htmltools_0.5.0    
##  [37] tools_3.6.3         igraph_1.2.5        gtable_0.3.0       
##  [40] glue_1.4.1          RANN_2.6.1          reshape2_1.4.3     
##  [43] Rcpp_1.0.5          vctrs_0.2.0         gdata_2.18.0       
##  [46] ape_5.3             nlme_3.1-141        iterators_1.0.12   
##  [49] fpc_2.2-3           gbRd_0.4-11         lmtest_0.9-37      
##  [52] xfun_0.18           stringr_1.4.0       lifecycle_0.1.0    
##  [55] irlba_2.3.3         gtools_3.8.1        DEoptimR_1.0-8     
##  [58] MASS_7.3-53         zoo_1.8-6           scales_1.1.0       
##  [61] doSNOW_1.0.18       parallel_3.6.3      yaml_2.2.1         
##  [64] reticulate_1.13     pbapply_1.4-2       gridExtra_2.3      
##  [67] rpart_4.1-15        segmented_1.0-0     latticeExtra_0.6-28
##  [70] stringi_1.4.6       highr_0.8           foreach_1.4.7      
##  [73] checkmate_1.9.4     caTools_1.17.1.2    bibtex_0.4.2       
##  [76] Rdpack_0.11-0       SDMTools_1.1-221.1  rlang_0.4.7        
##  [79] pkgconfig_2.0.3     dtw_1.21-3          prabclus_2.3-1     
##  [82] bitops_1.0-6        evaluate_0.14       lattice_0.20-41    
##  [85] ROCR_1.0-7          labeling_0.3        htmlwidgets_1.5.1  
##  [88] bit_4.0.4           tidyselect_0.2.5    plyr_1.8.4         
##  [91] magrittr_1.5        R6_2.4.1            snow_0.4-3         
##  [94] gplots_3.0.1.1      Hmisc_4.3-0         pillar_1.4.2       
##  [97] foreign_0.8-72      withr_2.1.2         fitdistrplus_1.0-14
## [100] mixtools_1.1.0      survival_2.44-1.1   nnet_7.3-14        
## [103] tsne_0.1-3          tibble_2.1.3        crayon_1.3.4       
## [106] hdf5r_1.3.2.9000    KernSmooth_2.23-15  rmarkdown_2.5      
## [109] viridis_0.5.1       grid_3.6.3          data.table_1.12.6  
## [112] metap_1.1           digest_0.6.25       diptest_0.75-7     
## [115] tidyr_1.0.0         R.utils_2.9.0       stats4_3.6.3       
## [118] munsell_0.5.0       viridisLite_0.3.0   beeswarm_0.2.3     
## [121] vipor_0.4.5

---

1. Institute of Psychiatry and Neuroscience of Paris, INSERM U1266, 75014, Paris, France, matthieu.moreau@inserm.fr↩