• Load libraries
  • load the dataset
  • Extract the apical progenitors
  • Filter gene counts matrix
  • Topic modeling
    • Fit topic model
    • Explore the different topics
    • Cluster Progenitors
    • Rename clusters
    • Transfer identity to the full dataset
  • Differentiating neurons lineages
    • Split Pallial from Cajal-Retzius cells
    • Transfer identities to the full dataset
  • Plot representative marker genes
  • Save data
  • Session Info

Load libraries

library(Seurat)
library(fastTopics)
library(RcppParallel)
library(Matrix)
library(dplyr)
library(RColorBrewer)
library(ggplot2)
library(ggExtra)
library(cowplot)
library(wesanderson)

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

load the dataset

Hem.data <- readRDS("../QC.filtered.cells.RDS")
DimPlot(Hem.data,
        reduction = "spring",
        cols = c(wes_palette("FantasticFox1"),"grey60"),
        pt.size = 0.5) & NoAxes()

Extract the apical progenitors

# Extract apical progenitors 
Progenitors.data <-  subset(Hem.data, idents = c(0,1,3))

DimPlot(Progenitors.data,
        reduction = "spring",
        pt.size = 0.5,
        cols = c(wes_palette("FantasticFox1")[c(1,2,4)]),
        split.by = 'ident') + NoLegend() & NoAxes()

rm(Hem.data) ; gc()
##             used   (Mb) gc trigger   (Mb)   max used   (Mb)
## Ncells   3307939  176.7    6059512  323.7    4834983  258.3
## Vcells 338673567 2583.9 1045076561 7973.4 1016168883 7752.8

Filter gene counts matrix

For this analysis we will keep only genes detected in at least 20 over 12325 cells

progenitors.counts <- GetAssayData(object = Progenitors.data[["RNA"]], slot = "counts")
dim(progenitors.counts)
## [1] 18268 12325
num.cells <- Matrix::rowSums(progenitors.counts > 0)
genes.use <- names(x = num.cells[which(x = num.cells >= 20)])
progenitors.counts <- progenitors.counts[genes.use, ]

dim(progenitors.counts)
## [1] 15314 12325
gc()
##             used   (Mb) gc trigger   (Mb)   max used   (Mb)
## Ncells   3295244  176.0    6059512  323.7    4834983  258.3
## Vcells 414502290 3162.5 1045076561 7973.4 1016168883 7752.8

Topic modeling

Fit topic model

set.seed(1)

fit <- fit_topic_model(t(progenitors.counts),
                       k = 15,
                       numiter.main = 200,
                       numiter.refine = 200,
                       method.main = "em",
                       method.refine = "scd",
                       control.main = list(numiter = 4, nc= 6),
                       control.refine = list(numiter = 4, nc= 6, extrapolate = TRUE),
                       verbose = "progressbar")
## Initializing factors using Topic SCORE algorithm.
## Initializing loadings by running 10 SCD updates.
## Fitting rank-15 Poisson NMF to 12325 x 15314 sparse matrix.
## Running 200 EM updates, without extrapolation (fastTopics 0.5-59).
## Refining model fit.
## Fitting rank-15 Poisson NMF to 12325 x 15314 sparse matrix.
## Running 200 SCD updates, with extrapolation (fastTopics 0.5-59).

Explore the different topics

# Add cells' topics loading to the metadata
Progenitors.data@meta.data <- cbind(Progenitors.data@meta.data, fit$L)
FeaturePlot(object = Progenitors.data,
                    features = paste0("k", 1:15),
                    cols = rev(brewer.pal(10,"Spectral")),
                    reduction = "spring") & NoLegend() & NoAxes()

FeaturePlot(object = Progenitors.data,
                    features = paste0("k", c(15,12,9,8,14,6)),
                    cols = rev(brewer.pal(10,"Spectral")),
                    reduction = "spring",
                    order = T) & NoLegend() & NoAxes()

Cluster Progenitors

set.seed(1)
pca <- prcomp(fit$L[,c(15,12,9,8,14,6)])$x
clusters <- cluster::pam(pca, k = 6)$clustering
Progenitors.data@meta.data$TopicsKmeans <- as.numeric(clusters)

FeaturePlot(object = Progenitors.data,
            features = "TopicsKmeans",
            cols = c(wes_palette("FantasticFox1"),"grey90", "grey40"),
            reduction = "spring") & NoLegend() & NoAxes()

Idents(Progenitors.data) <- Progenitors.data$TopicsKmeans

DimPlot(Progenitors.data,
        reduction = "spring",
        pt.size = 0.5,
        cols =  c(wes_palette("FantasticFox1"),"grey90", "grey40"),
        split.by = 'ident') + NoLegend() & NoAxes()

Rename clusters

ident = c("Dorso-Medial_pallium", "ChP", "Medial_pallium", "Hem", "ChP_progenitors", "Thalamic_eminence")

Progenitors.data$progenitor_type <- sapply(Progenitors.data$TopicsKmeans,
                                           FUN = function(x) {x= ident[x]})

Idents(Progenitors.data) <- Progenitors.data$progenitor_type
DimPlot(Progenitors.data,
        reduction = "spring",
        pt.size = 0.5,
        cols =  c(wes_palette("FantasticFox1"),"grey90"),
        split.by = 'ident') + NoLegend() & NoAxes()

Transfer identity to the full dataset

Hem.data <- readRDS("../QC.filtered.cells.RDS")
Hem.data$Cell_ident <- sapply(Hem.data$Barcodes,
                              FUN = function(x) {
                                if (x %in% Progenitors.data$Barcodes) {
                                  x = Progenitors.data@meta.data[x, "progenitor_type"]
                                } else {
                                  x = paste0("seurat_clusters_", Hem.data@meta.data[x, "seurat_clusters"])
                                  }
                              })
DimPlot(object = Hem.data,
        group.by = "Cell_ident",
        reduction = "spring",
        cols = c("#83c3b8", #"ChP"
                 "#009fda", #"ChP_progenitors"
                 "#68b041", #"Dorso-Medial_pallium"
                 "#e46b6b", #"Hem"
                 "#e3c148", #"Medial_pallium"
                 "#b7d174", #2
                 "grey40", #4
                 "black", #5
                 "#3e69ac" #"Thalamic_eminence"
                 ))

Differentiating neurons lineages

Neurons.data <-  subset(Hem.data, idents = 2)

DimPlot(Neurons.data ,
        reduction = "spring",
        pt.size = 1,
        cols =  c("#b7d174")) + NoAxes()

Split Pallial from Cajal-Retzius cells

p1 <- FeaturePlot(object = Neurons.data ,
            features = c("BP_signature1","LN_signature1"),
            pt.size = 0.5,
            cols = rev(brewer.pal(10,"Spectral")),
            reduction = "spring",
            order = T) & NoAxes()

p2 <- FeaturePlot(object = Neurons.data ,
            features = c("Foxg1", "Trp73"),
            pt.size = 0.5,
            cols = c("grey90", brewer.pal(9,"YlGnBu")),
            reduction = "spring",
            order = T) & NoAxes()

p1 / p2

Separation between the 2 lineage seems straightforward. We use louvain clustering to split the two.

Neurons.data <- RunPCA(Neurons.data, verbose = FALSE)

Neurons.data <- FindNeighbors(Neurons.data,
                              dims = 1:10,
                              k.param = 8)

Neurons.data <- FindClusters(Neurons.data, resolution = 0.05)
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 2835
## Number of edges: 56608
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9640
## Number of communities: 2
## Elapsed time: 0 seconds
DimPlot(Neurons.data,
        reduction = "spring",
        cols = c("#cc391b","#026c9a"),
        pt.size = 0.5) & NoAxes()

Neurons.data$Lineage <- sapply(as.numeric(Neurons.data$SCT_snn_res.0.05),
                               FUN = function(x) {x= c("Cajal-Retzius_neurons","Pallial_neurons")[x]})
DimPlot(object = Neurons.data,
        group.by = "Lineage",
        reduction = "spring",
        cols = c("#cc391b","#026c9a"),
        pt.size = 0.5) & NoAxes()

Transfer identities to the full dataset

Hem.data$Cell_ident <- sapply(Hem.data$Barcodes,
                              FUN = function(x) {
                                if (x %in% Neurons.data$Barcodes) {
                                  x = Neurons.data@meta.data[x, "Lineage"]
                                } else {
                                  x = Hem.data@meta.data[x, "Cell_ident"]
                                  }
                              })

Plot representative marker genes

We excluded Meninges and Immune cell clusters

Idents(Hem.data) <- Hem.data$Cell_ident

Hem.data <-  subset(Hem.data, idents = unique(Hem.data$Cell_ident)[!unique(Hem.data$Cell_ident) %in% c("seurat_clusters_4", "seurat_clusters_5")])
Hem.data <- BuildClusterTree(Hem.data,
                             assay = "SCT",
                             slot = "data",
                             reorder = T,
                             verbose = TRUE)

data.tree <- Tool(object = Hem.data, slot = "BuildClusterTree")
tree.rotated <- ape::rotate(data.tree, node =c(12))

Idents(Hem.data) <- factor(x = Idents(Hem.data),
                           levels = c("ChP","Cajal-Retzius_neurons","Pallial_neurons",
                                      "Dorso-Medial_pallium","Medial_pallium","Hem",
                                      "Thalamic_eminence","ChP_progenitors"),
                           ordered = TRUE)
DimPlot(object = Hem.data,
        group.by = "Cell_ident",
        reduction = "spring",
        cols = c("#ebcb2e", # CR 
                 "#9ec22f", #"ChP" 
                 "#e7823a", #"ChP_progenitors"
                 "#cc3a1b", #"Dorso-Medial_pallium" 
                 "#d14c8d", #"Hem" 
                 "#4cabdc", #"Medial_pallium"
                 "#046c9a", # Pallial
                 "#4990c9" #"Thalamic_eminence"
                 )
        ) + NoAxes()

p1 <- ggdendro::ggdendrogram(ggdendro::dendro_data(as.hclust(tree.rotated)), labels = F, rotate = T) + scale_y_reverse()
Marker.genes <- c("Htr2c", "Cfap126",
                  "Trp73", "Lhx1", "Foxg1","Cbln4", "Tbr1", "Neurod2",
                  "Lmo2", "Sox9", "Lhx2", "Meis2", "Shisa2",
                  "Wif1", "Wnt5a", "Id3",
                  "Rassf4", "Dkk3","Rspo3",
                  "Dlk1", "Meg3",
                  "Mlf1","Sulf1", "Ttr")

data.to.plot <- data.frame(t(as.matrix(Hem.data@assays$SCT[Marker.genes,])))
  
data.to.plot$Cell <- rownames(data.to.plot)
data.to.plot$id <- Idents(Hem.data)
  
#Reshape the dataframe
data.to.plot <- data.to.plot %>% tidyr::gather(key = Marker.genes, value = expression, -c(Cell, id)) 
  
#For each genes in each cluster calculate mean expression and percent cell with norm expression > 0
data.to.plot <- data.to.plot %>%
          group_by(id, Marker.genes) %>% 
              summarize(avg.exp = mean(expm1(x = expression)), pct.exp = length(x = expression[expression > 0.7]) / length(x = expression)) 
  
data.to.plot <- data.to.plot %>% ungroup() %>%
    group_by(Marker.genes) %>% 
    mutate(avg.exp.scale = scale(x = avg.exp)) %>%
    mutate(avg.exp.scale = MinMax(data = avg.exp.scale, max = 2, min = -2)) # add column with scaled expression values from -2 to 2
  
data.to.plot$genes.plot <- factor(x = data.to.plot$Marker.genes, levels = rev(x = Marker.genes)) #Put gene names as factor 
  
data.to.plot$pct.exp[data.to.plot$pct.exp < 0.05] <- NA #Set to Na if less than percent.min of cells express the gene
data.to.plot$pct.exp <- data.to.plot$pct.exp * 100
  
p2 <- ggplot(data = data.to.plot, mapping = aes(x = genes.plot, y = id)) +
        geom_point(mapping = aes(size = pct.exp, color = avg.exp.scale)) + # modify the colors by if want to color by domains or cluster ident
        scale_size_area(max_size= 4) + # Scale the radius of the dot from 0 to 6 
        scale_x_discrete(position = "top") +
        theme(axis.text.x = element_text(angle = 90, vjust = 1), axis.title.y = element_blank()) +
        xlab("") + ylab("") +
        scale_colour_gradientn(colours = brewer.pal(11,"RdPu"))
plot_grid(plotlist = list(p1,p2), ncol=2, align='h', rel_widths = c(0.2, 1.5))

FeaturePlot(object = Hem.data,
            features = c("Tbr1", "Trp73", "Htr2c",
                         "Shisa2", "Wif1", "Rassf4","Dkk3",
                         "Dlk1", "Sulf1", "Ttr"),
            pt.size = 0.2,
            cols = c("grey90", brewer.pal(9,"YlGnBu")),
            reduction = "spring",
            order = T) & NoAxes() & NoLegend()

Save data

saveRDS(Hem.data, "../QC.filtered.clustered.cells.RDS")

Session Info

#date
format(Sys.time(), "%d %B, %Y, %H,%M")
## [1] "23 février, 2022, 12,24"
#Packages used
sessionInfo()
## R version 4.1.2 (2021-11-01)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.3 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
## 
## 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] wesanderson_0.3.6  cowplot_1.1.1      ggExtra_0.9        ggplot2_3.3.5     
##  [5] RColorBrewer_1.1-2 dplyr_1.0.7        Matrix_1.4-0       RcppParallel_5.1.4
##  [9] fastTopics_0.5-59  SeuratObject_4.0.4 Seurat_4.0.5      
## 
## loaded via a namespace (and not attached):
##   [1] plyr_1.8.6            igraph_1.2.11         lazyeval_0.2.2       
##   [4] splines_4.1.2         listenv_0.8.0         scattermore_0.7      
##   [7] digest_0.6.29         invgamma_1.1          foreach_1.5.1        
##  [10] htmltools_0.5.2       SQUAREM_2021.1        fansi_0.5.0          
##  [13] magrittr_2.0.2        tensor_1.5            cluster_2.1.2        
##  [16] ROCR_1.0-11           recipes_0.1.17        globals_0.14.0       
##  [19] gower_0.2.2           matrixStats_0.61.0    MCMCpack_1.6-0       
##  [22] spatstat.sparse_2.0-0 prettyunits_1.1.1     colorspace_2.0-2     
##  [25] ggrepel_0.9.1         xfun_0.28             crayon_1.4.2         
##  [28] jsonlite_1.7.2        spatstat.data_2.1-0   ape_5.5              
##  [31] survival_3.2-13       zoo_1.8-9             iterators_1.0.13     
##  [34] glue_1.5.1            polyclip_1.10-0       gtable_0.3.0         
##  [37] ipred_0.9-12          MatrixModels_0.5-0    leiden_0.3.9         
##  [40] future.apply_1.8.1    abind_1.4-5           SparseM_1.81         
##  [43] scales_1.1.1          DBI_1.1.1             miniUI_0.1.1.1       
##  [46] Rcpp_1.0.8            progress_1.2.2        viridisLite_0.4.0    
##  [49] xtable_1.8-4          reticulate_1.22       spatstat.core_2.3-1  
##  [52] stats4_4.1.2          lava_1.6.10           prodlim_2019.11.13   
##  [55] truncnorm_1.0-8       htmlwidgets_1.5.4     httr_1.4.2           
##  [58] ellipsis_0.3.2        ica_1.0-2             farver_2.1.0         
##  [61] pkgconfig_2.0.3       nnet_7.3-17           sass_0.4.0           
##  [64] uwot_0.1.10           deldir_1.0-6          utf8_1.2.2           
##  [67] caret_6.0-90          labeling_0.4.2        tidyselect_1.1.1     
##  [70] rlang_0.4.12          reshape2_1.4.4        later_1.3.0          
##  [73] munsell_0.5.0         tools_4.1.2           generics_0.1.1       
##  [76] ggridges_0.5.3        ggdendro_0.1.23       evaluate_0.14        
##  [79] stringr_1.4.0         fastmap_1.1.0         yaml_2.2.1           
##  [82] goftest_1.2-3         mcmc_0.9-7            ModelMetrics_1.2.2.2 
##  [85] knitr_1.36            fitdistrplus_1.1-6    purrr_0.3.4          
##  [88] RANN_2.6.1            pbapply_1.5-0         future_1.23.0        
##  [91] nlme_3.1-153          mime_0.12             quantreg_5.86        
##  [94] compiler_4.1.2        plotly_4.10.0         png_0.1-7            
##  [97] spatstat.utils_2.2-0  tibble_3.1.6          bslib_0.3.1          
## [100] stringi_1.7.6         highr_0.9             lattice_0.20-45      
## [103] vctrs_0.3.8           pillar_1.6.4          lifecycle_1.0.1      
## [106] spatstat.geom_2.3-0   lmtest_0.9-39         jquerylib_0.1.4      
## [109] RcppAnnoy_0.0.19      data.table_1.14.2     irlba_2.3.3          
## [112] conquer_1.2.1         httpuv_1.6.3          patchwork_1.1.1      
## [115] R6_2.5.1              promises_1.2.0.1      KernSmooth_2.23-20   
## [118] gridExtra_2.3         parallelly_1.29.0     codetools_0.2-18     
## [121] MASS_7.3-55           assertthat_0.2.1      withr_2.4.3          
## [124] sctransform_0.3.2     hms_1.1.1             mgcv_1.8-38          
## [127] parallel_4.1.2        quadprog_1.5-8        grid_4.1.2           
## [130] rpart_4.1.16          timeDate_3043.102     tidyr_1.1.4          
## [133] coda_0.19-4           class_7.3-20          rmarkdown_2.11       
## [136] ashr_2.2-47           Rtsne_0.15            mixsqp_0.3-43        
## [139] pROC_1.18.0           shiny_1.7.1           lubridate_1.8.0

  1. Institute of Psychiatry and Neuroscience of Paris, INSERM U1266, 75014, Paris, France, ↩︎

---
title: "Progenitor domains and neuronal lineage characterisation"
author:
   - Matthieu Moreau^[Institute of Psychiatry and Neuroscience of Paris, INSERM U1266, 75014, Paris, France, matthieu.moreau@inserm.fr] [![](https://orcid.org/sites/default/files/images/orcid_16x16.png)](https://orcid.org/0000-0002-2592-2373)
date: "`r format(Sys.time(), '%d %B, %Y')`"
output: 
  html_document: 
    code_download: yes
    df_print: tibble
    highlight: haddock
    theme: cosmo
    css: "../style.css"
    toc: yes
    toc_depth: 5
    toc_float:
      collapsed: yes
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, fig.align = 'center', message=FALSE, warning=FALSE)
```

# Load libraries

```{r message=FALSE, warning=FALSE}
library(Seurat)
library(fastTopics)
library(RcppParallel)
library(Matrix)
library(dplyr)
library(RColorBrewer)
library(ggplot2)
library(ggExtra)
library(cowplot)
library(wesanderson)

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

# load the dataset

```{r}
Hem.data <- readRDS("../QC.filtered.cells.RDS")
```

```{r}
DimPlot(Hem.data,
        reduction = "spring",
        cols = c(wes_palette("FantasticFox1"),"grey60"),
        pt.size = 0.5) & NoAxes()
```

# Extract the apical progenitors

```{r}
# Extract apical progenitors 
Progenitors.data <-  subset(Hem.data, idents = c(0,1,3))

DimPlot(Progenitors.data,
        reduction = "spring",
        pt.size = 0.5,
        cols = c(wes_palette("FantasticFox1")[c(1,2,4)]),
        split.by = 'ident') + NoLegend() & NoAxes()

rm(Hem.data) ; gc()
```

# Filter gene counts matrix

For this analysis we will keep only genes detected in at least 20 over 12325 cells

```{r}
progenitors.counts <- GetAssayData(object = Progenitors.data[["RNA"]], slot = "counts")
dim(progenitors.counts)

num.cells <- Matrix::rowSums(progenitors.counts > 0)
genes.use <- names(x = num.cells[which(x = num.cells >= 20)])
progenitors.counts <- progenitors.counts[genes.use, ]

dim(progenitors.counts)
```

```{r}
gc()
```

# Topic modeling

## Fit topic model

```{r fit_topic_model, cache=TRUE, class.output="scroll-100"}
set.seed(1)

fit <- fit_topic_model(t(progenitors.counts),
                       k = 15,
                       numiter.main = 200,
                       numiter.refine = 200,
                       method.main = "em",
                       method.refine = "scd",
                       control.main = list(numiter = 4, nc= 6),
                       control.refine = list(numiter = 4, nc= 6, extrapolate = TRUE),
                       verbose = "progressbar")
```

## Explore the different topics

```{r}
# Add cells' topics loading to the metadata
Progenitors.data@meta.data <- cbind(Progenitors.data@meta.data, fit$L)
```

```{r fig.dim=c(6, 9)}
FeaturePlot(object = Progenitors.data,
                    features = paste0("k", 1:15),
                    cols = rev(brewer.pal(10,"Spectral")),
                    reduction = "spring") & NoLegend() & NoAxes()

```

```{r fig.dim=c(6, 9)}
FeaturePlot(object = Progenitors.data,
                    features = paste0("k", c(15,12,9,8,14,6)),
                    cols = rev(brewer.pal(10,"Spectral")),
                    reduction = "spring",
                    order = T) & NoLegend() & NoAxes()

```

## Cluster Progenitors

```{r Kmeans clustering on topics PCs}
set.seed(1)
pca <- prcomp(fit$L[,c(15,12,9,8,14,6)])$x
clusters <- cluster::pam(pca, k = 6)$clustering
```

```{r}
Progenitors.data@meta.data$TopicsKmeans <- as.numeric(clusters)

FeaturePlot(object = Progenitors.data,
            features = "TopicsKmeans",
            cols = c(wes_palette("FantasticFox1"),"grey90", "grey40"),
            reduction = "spring") & NoLegend() & NoAxes()
```

```{r}
Idents(Progenitors.data) <- Progenitors.data$TopicsKmeans

DimPlot(Progenitors.data,
        reduction = "spring",
        pt.size = 0.5,
        cols =  c(wes_palette("FantasticFox1"),"grey90", "grey40"),
        split.by = 'ident') + NoLegend() & NoAxes()
```


## Rename clusters

```{r}
ident = c("Dorso-Medial_pallium", "ChP", "Medial_pallium", "Hem", "ChP_progenitors", "Thalamic_eminence")

Progenitors.data$progenitor_type <- sapply(Progenitors.data$TopicsKmeans,
                                           FUN = function(x) {x= ident[x]})

Idents(Progenitors.data) <- Progenitors.data$progenitor_type
```

```{r}
DimPlot(Progenitors.data,
        reduction = "spring",
        pt.size = 0.5,
        cols =  c(wes_palette("FantasticFox1"),"grey90"),
        split.by = 'ident') + NoLegend() & NoAxes()
```

## Transfer identity to the full dataset

```{r}
Hem.data <- readRDS("../QC.filtered.cells.RDS")
```

```{r }
Hem.data$Cell_ident <- sapply(Hem.data$Barcodes,
                              FUN = function(x) {
                                if (x %in% Progenitors.data$Barcodes) {
                                  x = Progenitors.data@meta.data[x, "progenitor_type"]
                                } else {
                                  x = paste0("seurat_clusters_", Hem.data@meta.data[x, "seurat_clusters"])
                                  }
                              })
```

```{r}
DimPlot(object = Hem.data,
        group.by = "Cell_ident",
        reduction = "spring",
        cols = c("#83c3b8", #"ChP"
                 "#009fda", #"ChP_progenitors"
                 "#68b041", #"Dorso-Medial_pallium"
                 "#e46b6b", #"Hem"
                 "#e3c148", #"Medial_pallium"
                 "#b7d174", #2
                 "grey40", #4
                 "black", #5
                 "#3e69ac" #"Thalamic_eminence"
                 ))
```

# Differentiating neurons lineages

```{r}
Neurons.data <-  subset(Hem.data, idents = 2)

DimPlot(Neurons.data ,
        reduction = "spring",
        pt.size = 1,
        cols =  c("#b7d174")) + NoAxes()
```

## Split Pallial from Cajal-Retzius cells

```{r}
p1 <- FeaturePlot(object = Neurons.data ,
            features = c("BP_signature1","LN_signature1"),
            pt.size = 0.5,
            cols = rev(brewer.pal(10,"Spectral")),
            reduction = "spring",
            order = T) & NoAxes()

p2 <- FeaturePlot(object = Neurons.data ,
            features = c("Foxg1", "Trp73"),
            pt.size = 0.5,
            cols = c("grey90", brewer.pal(9,"YlGnBu")),
            reduction = "spring",
            order = T) & NoAxes()

p1 / p2
```

Separation between the 2 lineage seems straightforward. We use louvain clustering to split the two.

```{r}
Neurons.data <- RunPCA(Neurons.data, verbose = FALSE)

Neurons.data <- FindNeighbors(Neurons.data,
                              dims = 1:10,
                              k.param = 8)

Neurons.data <- FindClusters(Neurons.data, resolution = 0.05)
```

```{r}
DimPlot(Neurons.data,
        reduction = "spring",
        cols = c("#cc391b","#026c9a"),
        pt.size = 0.5) & NoAxes()
```

```{r}
Neurons.data$Lineage <- sapply(as.numeric(Neurons.data$SCT_snn_res.0.05),
                               FUN = function(x) {x= c("Cajal-Retzius_neurons","Pallial_neurons")[x]})
```

```{r}
DimPlot(object = Neurons.data,
        group.by = "Lineage",
        reduction = "spring",
        cols = c("#cc391b","#026c9a"),
        pt.size = 0.5) & NoAxes()
```

## Transfer identities to the full dataset

```{r Transfer identities}
Hem.data$Cell_ident <- sapply(Hem.data$Barcodes,
                              FUN = function(x) {
                                if (x %in% Neurons.data$Barcodes) {
                                  x = Neurons.data@meta.data[x, "Lineage"]
                                } else {
                                  x = Hem.data@meta.data[x, "Cell_ident"]
                                  }
                              })
```

# Plot representative marker genes

We excluded Meninges and Immune cell clusters

```{r}
Idents(Hem.data) <- Hem.data$Cell_ident

Hem.data <-  subset(Hem.data, idents = unique(Hem.data$Cell_ident)[!unique(Hem.data$Cell_ident) %in% c("seurat_clusters_4", "seurat_clusters_5")])
```

```{r}
Hem.data <- BuildClusterTree(Hem.data,
                             assay = "SCT",
                             slot = "data",
                             reorder = T,
                             verbose = TRUE)

data.tree <- Tool(object = Hem.data, slot = "BuildClusterTree")
tree.rotated <- ape::rotate(data.tree, node =c(12))

Idents(Hem.data) <- factor(x = Idents(Hem.data),
                           levels = c("ChP","Cajal-Retzius_neurons","Pallial_neurons",
                                      "Dorso-Medial_pallium","Medial_pallium","Hem",
                                      "Thalamic_eminence","ChP_progenitors"),
                           ordered = TRUE)
```

```{r}
DimPlot(object = Hem.data,
        group.by = "Cell_ident",
        reduction = "spring",
        cols = c("#ebcb2e", # CR 
                 "#9ec22f", #"ChP" 
                 "#e7823a", #"ChP_progenitors"
                 "#cc3a1b", #"Dorso-Medial_pallium" 
                 "#d14c8d", #"Hem" 
                 "#4cabdc", #"Medial_pallium"
                 "#046c9a", # Pallial
                 "#4990c9" #"Thalamic_eminence"
                 )
        ) + NoAxes()
```

```{r}
p1 <- ggdendro::ggdendrogram(ggdendro::dendro_data(as.hclust(tree.rotated)), labels = F, rotate = T) + scale_y_reverse()
```


```{r}
Marker.genes <- c("Htr2c", "Cfap126",
                  "Trp73", "Lhx1", "Foxg1","Cbln4", "Tbr1", "Neurod2",
                  "Lmo2", "Sox9", "Lhx2", "Meis2", "Shisa2",
                  "Wif1", "Wnt5a", "Id3",
                  "Rassf4", "Dkk3","Rspo3",
                  "Dlk1", "Meg3",
                  "Mlf1","Sulf1", "Ttr")

data.to.plot <- data.frame(t(as.matrix(Hem.data@assays$SCT[Marker.genes,])))
  
data.to.plot$Cell <- rownames(data.to.plot)
data.to.plot$id <- Idents(Hem.data)
  
#Reshape the dataframe
data.to.plot <- data.to.plot %>% tidyr::gather(key = Marker.genes, value = expression, -c(Cell, id)) 
  
#For each genes in each cluster calculate mean expression and percent cell with norm expression > 0
data.to.plot <- data.to.plot %>%
		  group_by(id, Marker.genes) %>% 
    		  summarize(avg.exp = mean(expm1(x = expression)), pct.exp = length(x = expression[expression > 0.7]) / length(x = expression)) 
  
data.to.plot <- data.to.plot %>% ungroup() %>%
    group_by(Marker.genes) %>% 
    mutate(avg.exp.scale = scale(x = avg.exp)) %>%
    mutate(avg.exp.scale = MinMax(data = avg.exp.scale, max = 2, min = -2)) # add column with scaled expression values from -2 to 2
  
data.to.plot$genes.plot <- factor(x = data.to.plot$Marker.genes, levels = rev(x = Marker.genes)) #Put gene names as factor 
  
data.to.plot$pct.exp[data.to.plot$pct.exp < 0.05] <- NA #Set to Na if less than percent.min of cells express the gene
data.to.plot$pct.exp <- data.to.plot$pct.exp * 100
  
p2 <- ggplot(data = data.to.plot, mapping = aes(x = genes.plot, y = id)) +
	    geom_point(mapping = aes(size = pct.exp, color = avg.exp.scale)) + # modify the colors by if want to color by domains or cluster ident
	    scale_size_area(max_size= 4) + # Scale the radius of the dot from 0 to 6 
	    scale_x_discrete(position = "top") +
	    theme(axis.text.x = element_text(angle = 90, vjust = 1), axis.title.y = element_blank()) +
	    xlab("") + ylab("") +
	    scale_colour_gradientn(colours = brewer.pal(11,"RdPu"))
```

```{r}
plot_grid(plotlist = list(p1,p2), ncol=2, align='h', rel_widths = c(0.2, 1.5))
```

```{r}
FeaturePlot(object = Hem.data,
            features = c("Tbr1", "Trp73", "Htr2c",
                         "Shisa2", "Wif1", "Rassf4","Dkk3",
                         "Dlk1", "Sulf1", "Ttr"),
            pt.size = 0.2,
            cols = c("grey90", brewer.pal(9,"YlGnBu")),
            reduction = "spring",
            order = T) & NoAxes() & NoLegend()
```

# Save data

```{r}
saveRDS(Hem.data, "../QC.filtered.clustered.cells.RDS")
```

# Session Info

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

#Packages used
sessionInfo()
```
