Pallial neurons clustering

Extract pallial late neurons by k-means clustering

We extract the most mature pallial cells in two steps :

  • We seleted the glutamatergic neuronal branch
  • From this cells we extracted the one with the most mature signature score

Seletion the glutamatergic neuronal branch

We perform Kmeans clustering on the 3 cell state scores :

  • Apical progenitors AP
  • Sub-pallial neurons SP
  • Pallial neurons Pal

We then extract the glutamatergic neuron branch as the cluster with the highest mean Pallial neurons signature

Extract late neurons

We then extract the glutamatergic neuron with the highest mean Late neurons signature

Perform iterative clustering implemented in scrattch.hicat package

For more detail on the scrattch.hicat please refer to the package page.

Prepare the dataset for clustering with scrattch.hicat

Iterative clustering

Run the iterative clustering

The default iter_clust function use in this version of the scrattch.hicat package does not allow to set the k.param argument. We modified this function to allow this argument to be set to other values.

## [1] "test-iter_clust"
##   Finding nearest neighbors...DONE ~ 0.006 s
##   Compute jaccard coefficient between nearest-neighbor sets...DONE ~ 0.011 s
##   Build undirected graph from the weighted links...DONE ~ 0.024 s
##   Run louvain clustering on the graph ...DONE ~ 0.012 s
##   Return a community class
##   -Modularity value: 0.8698811 
##   -Number of clusters: 21[1] "test-iter_clust.1"
## [1] "test-iter_clust.2"
##   Finding nearest neighbors...DONE ~ 0.001 s
##   Compute jaccard coefficient between nearest-neighbor sets...DONE ~ 0.001 s
##   Build undirected graph from the weighted links...DONE ~ 0.002 s
##   Run louvain clustering on the graph ...DONE ~ 0.001 s
##   Return a community class
##   -Modularity value: 0.3210654 
##   -Number of clusters: 2[1] "test-iter_clust.3"
## [1] "test-iter_clust.4"
##   Finding nearest neighbors...DONE ~ 0.001 s
##   Compute jaccard coefficient between nearest-neighbor sets...DONE ~ 0.002 s
##   Build undirected graph from the weighted links...DONE ~ 0.004 s
##   Run louvain clustering on the graph ...DONE ~ 0.002 s
##   Return a community class
##   -Modularity value: 0.7275928 
##   -Number of clusters: 9[1] "test-iter_clust.4.1"
##   Finding nearest neighbors...DONE ~ 0 s
##   Compute jaccard coefficient between nearest-neighbor sets...DONE ~ 0.001 s
##   Build undirected graph from the weighted links...DONE ~ 0.002 s
##   Run louvain clustering on the graph ...DONE ~ 0.001 s
##   Return a community class
##   -Modularity value: 0.5114614 
##   -Number of clusters: 3[1] "test-iter_clust.4.2"
##   Finding nearest neighbors...DONE ~ 0.001 s
##   Compute jaccard coefficient between nearest-neighbor sets...DONE ~ 0.002 s
##   Build undirected graph from the weighted links...DONE ~ 0.004 s
##   Run louvain clustering on the graph ...DONE ~ 0.002 s
##   Return a community class
##   -Modularity value: 0.7128587 
##   -Number of clusters: 7[1] "test-iter_clust.5"
## [1] "test-iter_clust.6"
##   Finding nearest neighbors...DONE ~ 0.001 s
##   Compute jaccard coefficient between nearest-neighbor sets...DONE ~ 0 s
##   Build undirected graph from the weighted links...DONE ~ 0.002 s
##   Run louvain clustering on the graph ...DONE ~ 0.001 s
##   Return a community class
##   -Modularity value: 0.5893365 
##   -Number of clusters: 4[1] "test-iter_clust.7"
##   Finding nearest neighbors...DONE ~ 0 s
##   Compute jaccard coefficient between nearest-neighbor sets...DONE ~ 0.001 s
##   Build undirected graph from the weighted links...DONE ~ 0.002 s
##   Run louvain clustering on the graph ...DONE ~ 0.001 s
##   Return a community class
##   -Modularity value: 0.4688921 
##   -Number of clusters: 3[1] "test-iter_clust.8"
##   Finding nearest neighbors...DONE ~ 0 s
##   Compute jaccard coefficient between nearest-neighbor sets...DONE ~ 0.001 s
##   Build undirected graph from the weighted links...DONE ~ 0.002 s
##   Run louvain clustering on the graph ...DONE ~ 0.001 s
##   Return a community class
##   -Modularity value: 0.6325457 
##   -Number of clusters: 5[1] "test-iter_clust.9"
##   Finding nearest neighbors...DONE ~ 0.001 s
##   Compute jaccard coefficient between nearest-neighbor sets...DONE ~ 0.002 s
##   Build undirected graph from the weighted links...DONE ~ 0.004 s
##   Run louvain clustering on the graph ...DONE ~ 0.002 s
##   Return a community class
##   -Modularity value: 0.7003194 
##   -Number of clusters: 6[1] "test-iter_clust.10"
##   Finding nearest neighbors...DONE ~ 0.001 s
##   Compute jaccard coefficient between nearest-neighbor sets...DONE ~ 0.001 s
##   Build undirected graph from the weighted links...DONE ~ 0.002 s
##   Run louvain clustering on the graph ...DONE ~ 0.001 s
##   Return a community class
##   -Modularity value: 0.626505 
##   -Number of clusters: 4[1] "test-iter_clust.11"
##   Finding nearest neighbors...DONE ~ 0.001 s
##   Compute jaccard coefficient between nearest-neighbor sets...DONE ~ 0.001 s
##   Build undirected graph from the weighted links...DONE ~ 0.003 s
##   Run louvain clustering on the graph ...DONE ~ 0.001 s
##   Return a community class
##   -Modularity value: 0.7033592 
##   -Number of clusters: 7[1] "test-iter_clust.12"
##   Finding nearest neighbors...DONE ~ 0 s
##   Compute jaccard coefficient between nearest-neighbor sets...DONE ~ 0 s
##   Build undirected graph from the weighted links...DONE ~ 0.002 s
##   Run louvain clustering on the graph ...DONE ~ 0.001 s
##   Return a community class
##   -Modularity value: 0.5403066 
##   -Number of clusters: 3

Transfert these ident on the full dataset

## [1] "Cluster_23: 109 Cells"
## [1] "Cluster_18: 86 Cells"
## [1] "Cluster_24: 45 Cells"
## [1] "Cluster_26: 26 Cells"
## [1] "Cluster_19: 48 Cells"
## [1] "Cluster_14: 16 Cells"
## [1] "Cluster_1: 33 Cells"
## [1] "Cluster_22: 37 Cells"
## [1] "Cluster_25: 66 Cells"
## [1] "Cluster_21: 24 Cells"
## [1] "Cluster_20: 31 Cells"
## [1] "Cluster_16: 24 Cells"
## [1] "Cluster_13: 16 Cells"

Refine the cell type taxonomie on the most mature clusters

Prepare data for scrattch.hicat

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

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Manuscript Fig. 2D dendrogramme

Manuscript Fig. 2D dendrogramme

Identify core cells using centroide classifier (Manuscript Fig. 2C)

Because some cells display intermediate transcriptomic signatures between two closely related clusters we used only clusters’ signatures on their core cells

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Manuscript element Fig. 2C

Manuscript element Fig. 2C

Sub-pallial neurons clustering

Load QC filtered dataset

Extract sub-pallial late neurons by k-means clustering on the cell state space

Selection of sub-pallial neuronal branche

As for the glutamaterigic neurons, we extract the most mature sub-pallial cells in two steps :

  • We seleted thesubplallial neuronal branche
  • From this we extracted cells with the most mature signature

We perform K-means clustering on the 3 cell state scores :

  • Apical progenitors AP
  • Sub-pallial neurons SP
  • Pallial neurons Pal

We then extract the GABAergic neuron branche as beeing the K-means cluster with the highest mean Sub-Pallial neurons signature

Extract late sub-pallial neurons

Perform iterative clustering implemented in scrattch.hicat package

We perform the clustering procedure as for the glutamatergic neurons

Prepare the dataset for clustering with scrattch.hicat

Iterative clustering

Run the iterative clustering

## [1] "test-iter_clust"
##   Finding nearest neighbors...DONE ~ 0.002 s
##   Compute jaccard coefficient between nearest-neighbor sets...DONE ~ 0.008 s
##   Build undirected graph from the weighted links...DONE ~ 0.013 s
##   Run louvain clustering on the graph ...DONE ~ 0.006 s
##   Return a community class
##   -Modularity value: 0.7894448 
##   -Number of clusters: 11[1] "test-iter_clust.1"
##   Finding nearest neighbors...DONE ~ 0 s
##   Compute jaccard coefficient between nearest-neighbor sets...DONE ~ 0.001 s
##   Build undirected graph from the weighted links...DONE ~ 0.002 s
##   Run louvain clustering on the graph ...DONE ~ 0.001 s
##   Return a community class
##   -Modularity value: 0.4405265 
##   -Number of clusters: 3[1] "test-iter_clust.2"
##   Finding nearest neighbors...DONE ~ 0.001 s
##   Compute jaccard coefficient between nearest-neighbor sets...DONE ~ 0.002 s
##   Build undirected graph from the weighted links...DONE ~ 0.003 s
##   Run louvain clustering on the graph ...DONE ~ 0.001 s
##   Return a community class
##   -Modularity value: 0.6305993 
##   -Number of clusters: 4[1] "test-iter_clust.3"
##   Finding nearest neighbors...DONE ~ 0 s
##   Compute jaccard coefficient between nearest-neighbor sets...DONE ~ 0.001 s
##   Build undirected graph from the weighted links...DONE ~ 0.002 s
##   Run louvain clustering on the graph ...DONE ~ 0 s
##   Return a community class
##   -Modularity value: 0.4473667 
##   -Number of clusters: 2[1] "test-iter_clust.4"
##   Finding nearest neighbors...DONE ~ 0.001 s
##   Compute jaccard coefficient between nearest-neighbor sets...DONE ~ 0.001 s
##   Build undirected graph from the weighted links...DONE ~ 0.002 s
##   Run louvain clustering on the graph ...DONE ~ 0.001 s
##   Return a community class
##   -Modularity value: 0.2015328 
##   -Number of clusters: 2[1] "test-iter_clust.5"
##   Finding nearest neighbors...DONE ~ 0 s
##   Compute jaccard coefficient between nearest-neighbor sets...DONE ~ 0.003 s
##   Build undirected graph from the weighted links...DONE ~ 0.007 s
##   Run louvain clustering on the graph ...DONE ~ 0.003 s
##   Return a community class
##   -Modularity value: 0.6771333 
##   -Number of clusters: 7[1] "test-iter_clust.6"
##   Finding nearest neighbors...DONE ~ 0 s
##   Compute jaccard coefficient between nearest-neighbor sets...DONE ~ 0.002 s
##   Build undirected graph from the weighted links...DONE ~ 0.004 s
##   Run louvain clustering on the graph ...DONE ~ 0.002 s
##   Return a community class
##   -Modularity value: 0.6456297 
##   -Number of clusters: 4

Broad neuronal types dotplot (Fig. 2B)

Hierarchical clustering using scrattch.hicat tools

Prepare data for used by scrattch.hicat

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

## Bootstrap (r = 0.5)... Done.
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Session Info

## [1] "30 novembre, 2020, 10,34"
## 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         limma_3.42.0          Rphenograph_0.99.1   
##  [4] igraph_1.2.5          wesanderson_0.3.6     ggExtra_0.9          
##  [7] tidyr_1.0.0           ggdendro_0.1-20       scrattch.vis_0.0.210 
## [10] purrr_0.3.3           ggbeeswarm_0.6.0      RColorBrewer_1.1-2   
## [13] Seurat_2.3.4          cowplot_1.0.0         ggplot2_3.2.1        
## [16] Matrix_1.2-17         matrixStats_0.55.0    dplyr_0.8.3          
## [19] dendextend_1.12.0     scrattch.hicat_0.0.16
## 
## loaded via a namespace (and not attached):
##   [1] snow_0.4-3          backports_1.1.5     Hmisc_4.3-0        
##   [4] plyr_1.8.4          lazyeval_0.2.2      splines_3.6.3      
##   [7] digest_0.6.25       foreach_1.4.7       htmltools_0.5.0    
##  [10] viridis_0.5.1       lars_1.2            gdata_2.18.0       
##  [13] magrittr_1.5        checkmate_1.9.4     cluster_2.1.0      
##  [16] mixtools_1.1.0      ROCR_1.0-7          R.utils_2.9.0      
##  [19] colorspace_1.4-1    xfun_0.18           crayon_1.3.4       
##  [22] jsonlite_1.7.0      zeallot_0.1.0       survival_2.44-1.1  
##  [25] zoo_1.8-6           iterators_1.0.12    ape_5.3            
##  [28] glue_1.4.1          gtable_0.3.0        kernlab_0.9-29     
##  [31] prabclus_2.3-1      DEoptimR_1.0-8      scales_1.1.0       
##  [34] bibtex_0.4.2        miniUI_0.1.1.1      Rcpp_1.0.5         
##  [37] metap_1.1           dtw_1.21-3          viridisLite_0.3.0  
##  [40] xtable_1.8-4        htmlTable_1.13.2    reticulate_1.13    
##  [43] foreign_0.8-72      bit_4.0.4           proxy_0.4-23       
##  [46] mclust_5.4.5        SDMTools_1.1-221.1  Formula_1.2-3      
##  [49] stats4_3.6.3        tsne_0.1-3          htmlwidgets_1.5.1  
##  [52] httr_1.4.1          gplots_3.0.1.1      fpc_2.2-3          
##  [55] ellipsis_0.3.0      acepack_1.4.1       modeltools_0.2-22  
##  [58] ica_1.0-2           farver_2.0.1        pkgconfig_2.0.3    
##  [61] R.methodsS3_1.7.1   flexmix_2.3-15      nnet_7.3-14        
##  [64] tidyselect_0.2.5    labeling_0.3        rlang_0.4.7        
##  [67] reshape2_1.4.3      later_1.0.0         munsell_0.5.0      
##  [70] tools_3.6.3         ggridges_0.5.1      evaluate_0.14      
##  [73] stringr_1.4.0       fastmap_1.0.1       yaml_2.2.1         
##  [76] npsurv_0.4-0        knitr_1.26          bit64_4.0.2        
##  [79] fitdistrplus_1.0-14 robustbase_0.93-5   caTools_1.17.1.2   
##  [82] RANN_2.6.1          pbapply_1.4-2       nlme_3.1-141       
##  [85] mime_0.7            R.oo_1.23.0         hdf5r_1.3.2.9000   
##  [88] compiler_3.6.3      rstudioapi_0.11     beeswarm_0.2.3     
##  [91] png_0.1-7           lsei_1.2-0          tibble_2.1.3       
##  [94] stringi_1.4.6       highr_0.8           lattice_0.20-41    
##  [97] vctrs_0.2.0         pillar_1.4.2        lifecycle_0.1.0    
## [100] Rdpack_0.11-0       lmtest_0.9-37       data.table_1.12.6  
## [103] bitops_1.0-6        irlba_2.3.3         gbRd_0.4-11        
## [106] httpuv_1.5.2        R6_2.4.1            latticeExtra_0.6-28
## [109] promises_1.1.0      KernSmooth_2.23-15  gridExtra_2.3      
## [112] vipor_0.4.5         codetools_0.2-16    MASS_7.3-53        
## [115] gtools_3.8.1        assertthat_0.2.1    withr_2.1.2        
## [118] diptest_0.75-7      parallel_3.6.3      doSNOW_1.0.18      
## [121] grid_3.6.3          rpart_4.1-15        class_7.3-17       
## [124] rmarkdown_2.5       segmented_1.0-0     Rtsne_0.15         
## [127] shiny_1.4.0         base64enc_0.1-3

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

---
title: "Clustering the pallial neuronal population"
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
    includes:
      in_header: header.html
    theme: cosmo
    toc: yes
    toc_depth: 5
    toc_float:
      collapsed: yes
---

```{css, echo=FALSE}
h1 {
  font-size: 34px;
  margin-top: 2rem;
  margin-bottom: 1rem;
  color: #e64d00;
  text-decoration: none;
}
h1.title {
  font-size: 40px;
  margin-top: 2rem;
  margin-bottom: 1rem;
  text-align: center;
  text-decoration: none;
  color: #000000;
}
h2 {
  font-size: 30px;
  margin-top: 2rem;
  margin-bottom: 1rem;
  color: #000000;
}
h3 {
  font-size: 24px;
  margin-top: 2rem;
  margin-bottom: 1rem;
  color: #000000;
}
h4 {
  font-size: 20px;
  margin-top: 2rem;
  margin-bottom: 1rem;
  color: #000000;
}
h5 {
  font-size: 18px;
  margin-top: 2rem;
  margin-bottom: 1rem;
  color: #000000;
}

.scroll-100 {
  max-height: 200px;
  overflow-y: auto;
  background-color: inherit;
}

p {
  font-size: 16px;
}
```

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

# Load libraries and QCFiltered dataset

```{r }
#load library
library(scrattch.hicat)
library(dendextend)
library(dplyr)
library(matrixStats)
library(Matrix)
library(Seurat)
library(RColorBrewer)
library(scrattch.vis)
library(ggdendro)
library(tidyr)
library(ggExtra)
library(wesanderson)

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

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

# Pallial neurons clustering

## Extract pallial late neurons by k-means clustering

We extract the most mature pallial cells in two steps :

- We seleted the glutamatergic neuronal branch
- From this cells we extracted the one with the most mature signature score

### Seletion the glutamatergic neuronal branch
```{r fig.dim=c(9, 8)}
# Cell state scores use to select
FeaturePlot(object = QCFiltered.data,
            features.plot = c("AP_signature1", "SP_signature1", "Pal_signature1"),
            cols.use = rev(brewer.pal(10,"Spectral")),
            reduction.use = "spring",
            no.legend = T,
            overlay = F,
            dark.theme = F
            )
```

We perform Kmeans clustering on the 3 cell state scores :

- Apical progenitors `AP`
- Sub-pallial neurons `SP`
- Pallial neurons `Pal`

```{r}
# K-means clustering based on AP, SP and Pal signature scores
set.seed(100)
cl <- kmeans(cbind(QCFiltered.data@meta.data$AP_signature1,
                   QCFiltered.data@meta.data$SP_signature1,
                   QCFiltered.data@meta.data$Pal_signature1), 3)

QCFiltered.data@meta.data$kmeanClust <- paste0("Clust.",cl$cluster)
```


```{r fig.dim=c(5.3, 4)}
col.pal <- wes_palette("GrandBudapest1", 3, type = "discrete")

p1 <- ggplot(QCFiltered.data@meta.data, aes(x=Pal_signature1, y=SP_signature1, colour = kmeanClust)) +
  scale_color_manual(values=col.pal) +
  geom_point() + 
  theme(legend.position="none")
ggMarginal(p1, type = "histogram", fill="lightgrey")

DimPlot(QCFiltered.data,
        group.by = "kmeanClust",
        reduction.use = "spring",
        cols.use = col.pal,
        dim.1 = 1,
        dim.2 = 2,
        do.label=T,
        label.size = 4,
        no.legend = T)
```

We then extract the glutamatergic neuron branch as the cluster with the highest mean `Pallial neurons` signature

```{r}
# Find the k-means cluster with the highest mean Pal_signature1 score
MeanKclust.Palscore <- aggregate(Pal_signature1 ~ kmeanClust, QCFiltered.data@meta.data, mean)
Palclust <- MeanKclust.Palscore %>% filter(Pal_signature1 == max(Pal_signature1)) %>% pull(kmeanClust)

# Extract barcodes and filter the seurat object
Glut.cells <- QCFiltered.data@meta.data %>% filter(kmeanClust == Palclust) %>% pull(Barcodes)
Glut.QCFiltered.data <-  SubsetData(QCFiltered.data, cells.use = Glut.cells , subset.raw = T,  do.clean = F)
```


### Extract late neurons

```{r}
# K-means clustering based on EN and LN signature scores
cl <- kmeans(cbind(Glut.QCFiltered.data@meta.data$EN_signature1, Glut.QCFiltered.data@meta.data$LN_signature1), 2)

Glut.QCFiltered.data@meta.data$kmeanClust <- paste0("Clust.",cl$cluster)
```

```{r}
# Find the k-means cluster with the highest mean Late_signature1 score
MeanKclust.LNscore <- aggregate(LN_signature1 ~ kmeanClust, Glut.QCFiltered.data@meta.data, mean)
LN.clust <- MeanKclust.LNscore %>% filter(LN_signature1 == max(LN_signature1)) %>% pull(kmeanClust)

# Extract barcodes
LN.cells <- Glut.QCFiltered.data@meta.data %>% filter(kmeanClust == LN.clust) %>% pull(Barcodes)
```


```{r fig.dim=c(5.3, 4)}
col.pal <- wes_palette("GrandBudapest1", 2, type = "discrete")

p1 <- ggplot(Glut.QCFiltered.data@meta.data, aes(x=LN_signature1, y=EN_signature1, colour = kmeanClust)) +
  annotate(geom = "text", label = paste0(length(LN.cells), " Late Neurons"), x = 1, y =1.5) +
  scale_color_manual(values=col.pal) +
  geom_point() + 
  theme(legend.position="none") 
ggMarginal(p1, type = "histogram", fill="lightgrey") ; rm(p1)

DimPlot(Glut.QCFiltered.data,
         group.by = "kmeanClust",
         reduction.use = "spring",
         cols.use = col.pal,
         dim.1 = 1,
         dim.2 = 2,
         do.label=T,
         label.size = 4,
         no.legend = T)
```

We then extract the glutamatergic neuron with the highest mean `Late neurons` signature

```{r}
# Filter the seurat object
Glut.QCFiltered.data <-  SubsetData(Glut.QCFiltered.data, cells.use = LN.cells , subset.raw = T,  do.clean = F)
```

```{r fig.dim=c(5.3, 4)}
DimPlot(Glut.QCFiltered.data,
         group.by = "kmeanClust",
         reduction.use = "spring",
         cols.use = "#969696",
         dim.1 = 1,
         dim.2 = 2,
         do.label=F,
         label.size = 4,
         no.legend = T)
```


```{r}
rm(list = ls()[!ls() %in% "Glut.QCFiltered.data"])
```

## Perform iterative clustering implemented in scrattch.hicat package

For more detail on the scrattch.hicat please refer to the [package page](https://github.com/AllenInstitute/scrattch.hicat). 

### Prepare the dataset for clustering with scrattch.hicat

#### Independent gene filtering

We decide to exclude **Cell cycle**, **ribosomal** and **mitochondrial** associated genes, as well as **Xist** for the clustering step.

```{r}
# Exclude cell cycle associated genes
s.genes <- c("Mcm5", "Pcna", "Tym5", "Fen1", "Mcm2", "Mcm4", "Rrm1", "Ung", "Gins2", "Mcm6", "Cdca7", "Dtl", "Prim1", "Uhrf1", "Mlf1ip", "Hells", "Rfc2", "Rap2", "Nasp", "Rad51ap1", "Gmnn", "Wdr76", "Slbp", "Ccne2", "Ubr7", "Pold3", "Msh2", "Atad2", "Rad51", "Rrm2", "Cdc45", "Cdc6", "Exo1", "Tipin", "Dscc1", "Blm", " Casp8ap2", "Usp1", "Clspn", "Pola1", "Chaf1b", "Brip1", "E2f8")
g2m.genes <- c("Hmgb2", "Ddk1","Nusap1", "Ube2c", "Birc5", "Tpx2", "Top2a", "Ndc80", "Cks2", "Nuf2", "Cks1b", "Mki67", "Tmpo", " Cenpk", "Tacc3", "Fam64a", "Smc4", "Ccnb2", "Ckap2l", "Ckap2", "Aurkb", "Bub1", "Kif11", "Anp32e", "Tubb4b", "Gtse1", "kif20b", "Hjurp", "Cdca3", "Hn1", "Cdc20", "Ttk", "Cdc25c", "kif2c", "Rangap1", "Ncapd2", "Dlgap5", "Cdca2", "Cdca8", "Ect2", "Kif23", "Hmmr", "Aurka", "Psrc1", "Anln", "Lbr", "Ckap5", "Cenpe", "Ctcf", "Nek2", "G2e3", "Gas2l3", "Cbx5", "Cenpa")

# Exclude genes detected in less than 3 cells
num.cells <- Matrix::rowSums(Glut.QCFiltered.data@data > 0)
genes.use <- names(x = num.cells[which(x = num.cells >= 3)])

GenesToRemove <- c(grep(pattern = "(^Rpl|^Rps|^Mrp)", x = genes.use, value = TRUE),
                   grep(pattern = "^mt-", x = genes.use, value = TRUE),
                   "Xist", s.genes, g2m.genes)

genes.use <- genes.use[!genes.use %in% GenesToRemove]
```

#### Normalization
```{r}
dgeMatrix_count <- as.matrix(Glut.QCFiltered.data@raw.data)[rownames(Glut.QCFiltered.data@raw.data) %in% genes.use,]
dgeMatrix_cpm <- cpm(dgeMatrix_count)
norm.dat <- log2(dgeMatrix_cpm + 1)
```

```{r}
norm.dat <- Matrix(norm.dat, sparse = TRUE)
Data.matrix <- list(raw.dat=dgeMatrix_count, norm.dat=norm.dat)
attach(Data.matrix)
```

```{r}
rm(list = ls()[!ls() %in% c("Glut.QCFiltered.data", "Data.matrix")])
```

#### Select features used to remove PCs which would correlate with unwanted sources of variation

```{r}
gene.counts <- log2(colSums(as.matrix(Data.matrix$norm.dat) > 0))
nUMI <- log2(colSums(Data.matrix$raw.dat))
perctMito <- Glut.QCFiltered.data@meta.data$percent.mito
perctRibo <- Glut.QCFiltered.data@meta.data$percent.ribo

rm.eigen <- as.matrix(cbind(gene.counts,
                            nUMI,
                            perctMito,
                            perctRibo))

row.names(rm.eigen) <- names(gene.counts)

colnames(rm.eigen) <- c("log2nGenes",
                        "log2nUMI",
                        "perctMito",
                        "perctRibo")

rm(gene.counts, nUMI, perctMito, perctRibo)
```

### Iterative clustering

```{r}
# Parameters for iterative clustering
de.param <- de_param(padj.th     = 0.01, 
                     lfc.th      = 0.9,
                     low.th      = 1, 
                     q1.th       = 0.25, 
                     q2.th       = NULL,
                     q.diff.th   = 0.7,
                     de.score.th = 30,
                     min.cells = 10)
```

#### Run the iterative clustering

The default iter_clust function use in this version of the scrattch.hicat package does not allow to set the k.param argument. We modified this function to allow this argument to be set to other values.

```{r class.output="scroll-100"}
# Perform the iterative clustering 
iter.result <- iter_clust(norm.dat, 
                          counts = raw.dat,
                          dim.method = "pca",
                          max.dim = 15,
                          de.param = de.param,
                          type = "undirectional",
                          rm.eigen = rm.eigen,
                          k.param = 8,
                          rm.th = 0.7,
                          vg.padj.th = 0.5,
                          method = "louvain",
                          prefix = "test-iter_clust",
                          verbose = F)

```

#### Merge clusters not seperable by DEGs

```{r}
# Merge clusters which are not seperable by DEGs
rd.dat <- t(norm.dat[iter.result$markers,])
merge.result <- merge_cl(norm.dat, 
                         cl = iter.result$cl, 
                         rd.dat = rd.dat,
                         de.param = de.param)

cat(length(unique(merge.result$cl))," Clusters\n")
cat(length(unique(merge.result$markers)), " DE genes")
```

#### Import cell labels into the Seurat obj


```{r fig.dim=c(5.3, 4), fig.cap= "Manuscript element Fig. 2C"}
Glut.QCFiltered.data@ident <- as.factor(merge.result$cl)
Glut.QCFiltered.data@meta.data$LN.ident <-as.character(Glut.QCFiltered.data@ident)

colors <-  c("#ebcb2e", "#9ec22f", "#a9961b", "#cc3a1b", "#cc8778" , "#d14c8d", "#4cabdc", "#5ab793", "#e7823a","#e6bb9b", "#046c9a", "#4784a2" , "#4990c9")

DimPlot(Glut.QCFiltered.data,
        reduction.use = "spring",
        dim.1 = 1,
        dim.2 = 2,
        do.label=T,
        label.size = 4,
        no.legend = T,
        cols.use = colors)
```

```{r}
rm(list = ls()[!ls() %in% c("Glut.QCFiltered.data", "colors")])
```

### Transfert these ident on the full dataset

```{r}
# Load 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$LN.ident)){
  New.ident <- i
  Barcodes <- rownames(subset(Clustdata@meta.data, Clustdata@meta.data$LN.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 = Glut.QCFiltered.data, RawQCdata = Allcells.data)
```

```{r fig.dim=c(5.3, 4)}
colors <-  c("#969696",colors)

DimPlot(Allcells.data,
        reduction.use = "spring", 
        dim.1 = 1,
        dim.2 = 2,
        do.label=T,
        label.size = 2,
        no.legend = T,
        cols.use = colors)
```

```{r}
Allcells.data <- StashIdent(Allcells.data, save.name = "Cluster.ident")
saveRDS(Allcells.data, "./Clustered.cells.RDS")
rm(list = ls()[!ls() %in% c("Glut.QCFiltered.data", "colors")])
```


## Refine the cell type taxonomie on the most mature clusters

### Select most mature neuron clusters (LN score > 1)

```{r fig.dim=c(6, 4.5)}
Glut.QCFiltered.data@meta.data %>% ggplot(aes(x= LN.ident, y= LN_signature1, colour= LN.ident)) +
                                          scale_color_manual(values=colors[-1]) +
                                          geom_boxplot() +
                                          geom_hline(yintercept = 1)
```

```{r fig.dim=c(5, 3.5), fig.cap= "Manuscript element Fig. 2C"}
# Extract clusters with median LN_score > 1
Median_LN_score <- aggregate(LN_signature1 ~ LN.ident, Glut.QCFiltered.data@meta.data, median)
mature_LN <- Median_LN_score %>% filter(LN_signature1 > 1) %>% pull(LN.ident)

Glut.QCFiltered.data <- SubsetData(Glut.QCFiltered.data, ident.use = mature_LN , subset.raw = T,  do.clean = F)

colors <- c("#ebcb2e", "#9ec22f", "#a9961b", "#cc3a1b", "#d14c8d", "#4cabdc", "#5ab793", "#e7823a", "#046c9a", "#4990c9") #colors[-c(1,5,10,12)]
DimPlot(Glut.QCFiltered.data,
        reduction.use = "spring", 
        dim.1 = 1,
        dim.2 = 2,
        do.label=T,
        label.size = 5,
        no.legend = T,
        cols.use = colors)
```

### Prepare data for scrattch.hicat 

#### Annotation data.frame

```{r}

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

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

```{r}
# Filter genes
num.cells <- Matrix::rowSums(Glut.QCFiltered.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)

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

# Find all var genes
Glut.QCFiltered.data <- FindVariableGenes(object = Glut.QCFiltered.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(Glut.QCFiltered.data@raw.data)[rownames(Glut.QCFiltered.data@raw.data) %in% Glut.QCFiltered.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

```{r}
# Take the mean expression over clusters
cl.med <- get_cl_means(norm.dat, cl.fact)
```

```{r}
# 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)

```

```{r fig.dim=c(5, 3.5), fig.cap= "Manuscript Fig. 2D dendrogramme"}
# Attach cluster labels to the leaves of the tree
dend.labeled <- dend.result$dend
labels(dend.labeled) <- cl.df[labels(dend.labeled), "cluster_label"]

plot(dend.labeled) 
```

```{r}
# Reorder the clusters for use in other plots
cl.fact <- setNames(factor(as.character(cl.fact),levels = labels(dend.labeled)), names(cl.fact))

cl.df <- cl.df[levels(cl.fact),]
```

### Identify core cells using centroide classifier (Manuscript Fig. 2C)

Because some cells display intermediate transcriptomic signatures between two closely related clusters we used only clusters' signatures on their core cells

```{r class.output="scroll-100"}
# Select marker genes
de.param <- de_param(padj.th     = 0.01,
                     lfc.th      = 0.6,
                     low.th      = 0.5, 
                     q1.th       = 0.25,
                     q2.th       = NULL,
                     q.diff.th   = 0.6, 
                     de.score.th = 20, 
                     min.cells = 5)

display.result <- display_cl(Glut.QCFiltered.data@ident, 
                             norm.dat, 
                             plot=F, 
                             de.param = de.param,
                             n.markers = 50)

select.markers <- select_markers(norm.dat, 
                                 cl.fact, 
                                 de.genes = display.result$de.genes,
                                 n.markers = 50)

marker.genes <- select.markers$markers
```


```{r class.output="scroll-100"}
# Run the centroide classifier
map.results <- map_sampling(train.dat = norm.dat,
                            train.cl = Glut.QCFiltered.data@ident,
                            test.dat = norm.dat,
                            markers = marker.genes,
                            markers.perc = 0.8,
                            iter = 100)
```

```{r fig.dim=c(5, 3.5)}
Glut.QCFiltered.data@meta.data$Core_cells <- ifelse(map.results$map.df$prob > 0.95, "Core.Cells", "Intermediate")

DimPlot(Glut.QCFiltered.data,
        group.by = "Core_cells",
        reduction.use = "spring",
        cols.use = wes_palette("Royal1", 2, type = "discrete"),
        dim.1 = 1,
        dim.2 = 2,
        do.label=F,
        label.size = 2,
        no.legend = F)
```

```{r}
# Remove intermediate cells
Core.cells <- Glut.QCFiltered.data@meta.data %>% dplyr::filter(Core_cells == "Core.Cells") %>% pull(Barcodes)

Glut.QCFiltered.data <- SubsetData(Glut.QCFiltered.data, cells.use = Core.cells , subset.raw = T,  do.clean = F)

anno.df <- anno.df %>% filter(sample_name %in% Core.cells)
```

```{r fig.dim=c(5, 3.5), fig.cap= "Manuscript element Fig. 2C"}
DimPlot(Glut.QCFiltered.data,
        reduction.use = "spring", 
        dim.1 = 1,
        dim.2 = 2,
        do.label=T,
        label.size = 3,
        no.legend = T,
        cols.use = colors)
```


### Built the cluster markers barplot (Manuscript Fig. 2D)

```{r fig.dim=c(7, 10), fig.cap= "Manuscript Fig. 2D"}
data <- cbind(sample_name = colnames(Glut.QCFiltered.data@data),
              as.data.frame(t(as.matrix(Glut.QCFiltered.data@data))))

Selected.markers <- c("Slc17a6","Tbr1", "Gap43", "Foxg1","Mn1","Reln",
                      "Lhx5","Calb2","Samd3", "Dmrta2", "Nr2f2", "Ebf2",
                      "Gm27199", "Chl1","Unc5c", "Grm1", "Insm2","Trp73",
                      "Ebf3", "Cdkn1a", "Lhx1", "Lhx1os","Zfp503", "Cacna2d2",
                      "Serpine2", "Rspo3", "Grm2", "Cxcr4","Lamp5","Neurod2",
                      "Neurod6", "Tcf4","Lrfn5", "Fezf1", "Fezf2", "Pcp4",
                      "Nfia", "Nfib",  "Nfix", "Bhlhe22","Ppp1r14c", "Scg2",
                      "Meis2","Pbx3", "Mab21l1", "Barhl2",  "Runx1t1","Mef2c",
                      "Nxph1", "Meis1",  "Tshz2","Lhx9", "Nr4a2", "Cck", "Foxp2",
                      "Pou3f2", "Sox6","Ebf1","Nrip3", "Zic1", "Etv1", "Tfap2e", "Pax6")

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")
```

```{r}
rm(list = ls())
```

# Sub-pallial neurons clustering

Load QC filtered dataset

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

## Extract sub-pallial late neurons by k-means clustering on the cell state space

### Selection of sub-pallial neuronal branche

As for the glutamaterigic neurons, we extract the most mature sub-pallial cells in two steps :

- We seleted thesubplallial neuronal branche
- From this we extracted cells with the most mature signature

```{r fig.dim=c(9, 8)}
FeaturePlot(object = QCFiltered.data,
            features.plot = c("AP_signature1", "Pal_signature1", "SP_signature1"),
            cols.use = rev(brewer.pal(10,"Spectral")),
            reduction.use = "spring",
            no.legend = T,
            overlay = F,
            dark.theme = F
)

```

We perform K-means clustering on the 3 cell state scores :

- Apical progenitors `AP`
- Sub-pallial neurons `SP`
- Pallial neurons `Pal`

```{r}
# K-means clustering based on AP, SP and Pal signature scores
set.seed(100)
cl <- kmeans(x = cbind(QCFiltered.data@meta.data$AP_signature1,
                   QCFiltered.data@meta.data$SP_signature1,
                   QCFiltered.data@meta.data$Pal_signature1),
             centers = 3)

QCFiltered.data@meta.data$kmeanClust <- paste0("Clust.",cl$cluster)
```

```{r fig.dim=c(5.3, 4)}
col.pal <- wes_palette("GrandBudapest1", 3, type = "discrete")

p1 <- ggplot(QCFiltered.data@meta.data, aes(x=Pal_signature1, y=SP_signature1, colour = kmeanClust)) +
  scale_color_manual(values=col.pal) +
  geom_point() + 
  theme(legend.position="none")
ggMarginal(p1, type = "histogram", fill="lightgrey")
```

```{r fig.dim=c(5.3, 4)}
DimPlot(QCFiltered.data,
        group.by = "kmeanClust",
        reduction.use = "spring",
        cols.use = col.pal,
        dim.1 = 1,
        dim.2 = 2,
        do.label=T,
        label.size = 4,
        no.legend = T)
```

We then extract the GABAergic neuron branche as beeing the K-means cluster with the highest mean `Sub-Pallial neurons` signature

```{r}
# Find the k-means clusters with the highest mean SP_signature1 score
MeanKclust.SPscore <- aggregate(SP_signature1 ~ kmeanClust, QCFiltered.data@meta.data, mean)
SPclust <- MeanKclust.SPscore %>% filter(SP_signature1 == max(SP_signature1)) %>% pull(kmeanClust)

# Extract barcodes and filter the seurat object
SP.cells <- QCFiltered.data@meta.data %>% filter(kmeanClust == SPclust) %>% pull(Barcodes)
SP.QCFiltered.data <-  SubsetData(QCFiltered.data, cells.use = SP.cells , subset.raw = T,  do.clean = F)
```

### Extract late sub-pallial neurons

```{r}
# Calculate SP early neurons signature score based on selected marker genes
LNgenes <- c("Dlx6os1", "Nudt4", "Abracl", "Arl4d", "Tmem123", "Ccdc109b", "Hmgn2", "E130006D01Rik", "Cdca7")
genes.list <- list(LNgenes)
enrich.name <- "EN_signature"
SP.QCFiltered.data <- AddModuleScore(SP.QCFiltered.data,
                                     genes.list = genes.list,
                                     genes.pool = NULL,
                                     n.bin = 5,
                                     seed.use = 1,
                                     ctrl.size = length(genes.list),
                                     use.k = FALSE,
                                     enrich.name = enrich.name,
                                     random.seed = 1)
```


```{r}
# K-means clustering based on EN and LN signature scores
cl <- kmeans(cbind(SP.QCFiltered.data@meta.data$EN_signature1, SP.QCFiltered.data@meta.data$LN_signature1), 3)

SP.QCFiltered.data@meta.data$kmeanClust <- paste0("Clust.",cl$cluster)

# Find the k-means clusters with the highest mean Late_signature1 score
MeanKclust.LNscore <- aggregate(LN_signature1 ~ kmeanClust, SP.QCFiltered.data@meta.data, mean)
LN.clust <- MeanKclust.LNscore %>% filter(LN_signature1 == max(LN_signature1)) %>% pull(kmeanClust)

# Extract barcodes
LN.cells <- SP.QCFiltered.data@meta.data %>% filter(kmeanClust == LN.clust) %>% pull(Barcodes)
```

```{r fig.dim=c(5.3, 4)}
col.pal <- wes_palette("GrandBudapest1", 3, type = "discrete")

p1 <- ggplot(SP.QCFiltered.data@meta.data, aes(x=LN_signature1, y=EN_signature1, colour = kmeanClust)) +
  annotate(geom = "text", label = paste0(length(LN.cells), " Late Neurons"), x = 1, y =1.5) +
  scale_color_manual(values=col.pal) +
  geom_point() + 
  theme(legend.position="none") 
ggMarginal(p1, type = "histogram", fill="lightgrey") ; rm(p1)

DimPlot(SP.QCFiltered.data,
         group.by = "kmeanClust",
         reduction.use = "spring",
         cols.use = col.pal,
         dim.1 = 1,
         dim.2 = 2,
         do.label=T,
         label.size = 4,
         no.legend = T)
```

```{r}
# Filter the seurat object
SP.QCFiltered.data <-  SubsetData(SP.QCFiltered.data, cells.use = LN.cells , subset.raw = T,  do.clean = F)
```

```{r fig.dim=c(5.3, 4)}
DimPlot(SP.QCFiltered.data,
         group.by = "kmeanClust",
         reduction.use = "spring",
         cols.use = "#969696",
         dim.1 = 1,
         dim.2 = 2,
         do.label=F,
         label.size = 4,
         no.legend = T)
```


## Perform iterative clustering implemented in scrattch.hicat package

We perform the clustering procedure as for the glutamatergic neurons

### Prepare the dataset for clustering with scrattch.hicat

#### Independent gene filtering

We decide to exclude **Cell cycle**, **ribosomal** and **mitochondrial** associated genes, as well as **Xist** for the clustering step.

```{r}
# Exclude cell cycle associated genes
s.genes <- c("Mcm5", "Pcna", "Tym5", "Fen1", "Mcm2", "Mcm4", "Rrm1", "Ung", "Gins2", "Mcm6", "Cdca7", "Dtl", "Prim1", "Uhrf1", "Mlf1ip", "Hells", "Rfc2", "Rap2", "Nasp", "Rad51ap1", "Gmnn", "Wdr76", "Slbp", "Ccne2", "Ubr7", "Pold3", "Msh2", "Atad2", "Rad51", "Rrm2", "Cdc45", "Cdc6", "Exo1", "Tipin", "Dscc1", "Blm", " Casp8ap2", "Usp1", "Clspn", "Pola1", "Chaf1b", "Brip1", "E2f8")
g2m.genes <- c("Hmgb2", "Ddk1","Nusap1", "Ube2c", "Birc5", "Tpx2", "Top2a", "Ndc80", "Cks2", "Nuf2", "Cks1b", "Mki67", "Tmpo", " Cenpk", "Tacc3", "Fam64a", "Smc4", "Ccnb2", "Ckap2l", "Ckap2", "Aurkb", "Bub1", "Kif11", "Anp32e", "Tubb4b", "Gtse1", "kif20b", "Hjurp", "Cdca3", "Hn1", "Cdc20", "Ttk", "Cdc25c", "kif2c", "Rangap1", "Ncapd2", "Dlgap5", "Cdca2", "Cdca8", "Ect2", "Kif23", "Hmmr", "Aurka", "Psrc1", "Anln", "Lbr", "Ckap5", "Cenpe", "Ctcf", "Nek2", "G2e3", "Gas2l3", "Cbx5", "Cenpa")

# Exclude genes detected in less than 3 cells
num.cells <- Matrix::rowSums(SP.QCFiltered.data@data > 0)
genes.use <- names(x = num.cells[which(x = num.cells >= 3)])

GenesToRemove <- c(grep(pattern = "(^Rpl|^Rps|^Mrp)", x = genes.use, value = TRUE), grep(pattern = "^mt-", x = genes.use, value = TRUE), s.genes, g2m.genes, "Xist")

genes.use <- genes.use[!genes.use %in% GenesToRemove]
```

#### Normalization
```{r}
dgeMatrix_count <- as.matrix(SP.QCFiltered.data@raw.data)[rownames(SP.QCFiltered.data@raw.data) %in% genes.use,]
dgeMatrix_cpm <- cpm(dgeMatrix_count)
norm.dat <- log2(dgeMatrix_cpm + 1)
```

```{r}
norm.dat <- Matrix(norm.dat, sparse = TRUE)
Data.matrix <- list(raw.dat=dgeMatrix_count, norm.dat=norm.dat)
attach(Data.matrix)
```

```{r}
rm(list = ls()[!ls() %in% c("SP.QCFiltered.data", "Data.matrix")])
```

#### Select features used to remove PCs which would correlate with unwanted sources of variation 

```{r}
gene.counts <- log2(colSums(as.matrix(Data.matrix$norm.dat) > 0))
nUMI <- log2(colSums(Data.matrix$raw.dat))
perctMito <- SP.QCFiltered.data@meta.data$percent.mito
perctRibo <- SP.QCFiltered.data@meta.data$percent.ribo

rm.eigen <- as.matrix(cbind(gene.counts,
                            nUMI,
                            perctMito,
                            perctRibo))

row.names(rm.eigen) <- names(gene.counts)

colnames(rm.eigen) <- c("log2nGenes",
                        "log2nUMI",
                        "perctMito",
                        "perctRibo")

rm(gene.counts, nUMI, perctMito, perctRibo)
```

### Iterative clustering

```{r}
# Parameters for iterative clustering
de.param <- de_param(padj.th     = 0.01, 
                     lfc.th      = 0.9,
                     low.th      = 1, 
                     q1.th       = 0.25, 
                     q2.th       = NULL,
                     q.diff.th   = 0.7,
                     de.score.th = 30,
                     min.cells = 10)
```

#### Run the iterative clustering

```{r class.output="scroll-100"}
# Perform the iterative clustering 
iter.result <- iter_clust(norm.dat, 
                          counts = raw.dat,
                          dim.method = "pca",
                          max.dim = 15,
                          de.param = de.param,
                          type = "undirectional",
                          rm.eigen = rm.eigen,
                          rm.th = 0.7,
                          vg.padj.th = 0.5,
                          method = "louvain",
                          prefix = "test-iter_clust",
                          verbose = F)

```

#### Merge clusters not seperable by DEGs

```{r}
# Merge clusters which are not seperable by DEGs
rd.dat <- t(norm.dat[iter.result$markers,])
merge.result <- merge_cl(norm.dat, 
                         cl = iter.result$cl, 
                         rd.dat = rd.dat,
                         de.param = de.param)

cat(length(unique(merge.result$cl))," Clusters\n")
cat(length(unique(merge.result$markers)), " DE genes")
```

#### Import cell labels into the Seurat obj


```{r fig.dim=c(5.3, 4)}
SP.QCFiltered.data@ident <- as.factor(merge.result$cl)
SP.QCFiltered.data@meta.data$LN.ident <-as.character(SP.QCFiltered.data@ident)
```


```{r fig.dim=c(5.3, 4)}
colors <-  c("#c773a7", "#b79f0b", "#3ca73f", "#31b6bd", "#ec756d", "#7293c8")

DimPlot(SP.QCFiltered.data,
        reduction.use = "spring",
        dim.1 = 1,
        dim.2 = 2,
        do.label=T,
        label.size = 4,
        no.legend = T,
        cols.use = colors)
```

```{r}
rm(list = ls()[!ls() %in% c("SP.QCFiltered.data", "colors")])
```

### Transfert these ident on the full dataset (Manuscript Fig. 2A)

```{r}
# Load full dataset
Allcells.data <- readRDS("./Clustered.cells.RDS")

# Transfer the identities
Rename.Clust <-  function(Clustdata, RawQCdata) {
  for(i in unique(Clustdata@meta.data$LN.ident)){
    New.ident <- i
    Barcodes <- rownames(subset(Clustdata@meta.data, Clustdata@meta.data$LN.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.GABA.",i))
  }
  return(RawQCdata)
}

Allcells.data <- Rename.Clust(Clustdata = SP.QCFiltered.data, RawQCdata = Allcells.data)
```

```{r fig.dim=c(8, 6), fig.cap= "Manuscript Fig. 2A"}
colors2 <-  c("#969696",
              "#ec756d", "#c773a7", "#7293c8", "#b79f0b", "#3ca73f","#31b6bd",
              "#ebcb2e", "#9ec22f", "#a9961b", "#cc3a1b", "#cc8778" , "#d14c8d", "#4cabdc", "#5ab793", "#e7823a","#e6bb9b", "#046c9a", "#4784a2" , "#4990c9")

DimPlot(Allcells.data,
        reduction.use = "spring", 
        dim.1 = 1,
        dim.2 = 2,
        do.label=T,
        label.size = 2,
        no.legend = T,
        cols.use = colors2)
```

```{r}
Allcells.data <-StashIdent(Allcells.data, save.name = "Cluster.ident")
saveRDS(Allcells.data, "./Clustered.cells.RDS")
rm(list = ls()[!ls() %in% c("Allcells.data")])
```

# Broad neuronal types dotplot (Fig. 2B)

```{r}
# Extract late neurons clusters
glut.clusters <- paste0(c("LN.Glut."),c("13", "1", "14", "22", "16", "19", "24", "26", "20", "21"))
gaba.clusters <- paste0( c("LN.GABA."),c("1", "7", "8", "9", "10", "11"))
clusters <- c(gaba.clusters, glut.clusters)
All.LN.data <- SubsetData(Allcells.data, ident.use = clusters, subset.raw = T,  do.clean = F)

rm(glut.clusters, gaba.cluster, clusters)
```

```{r fig.dim=c(5.3, 4)}
colors <- c("#ec756d", "#c773a7", "#7293c8", "#b79f0b", "#3ca73f","#31b6bd",
            "#ebcb2e", "#9ec22f", "#a9961b", "#cc3a1b", "#d14c8d", "#4cabdc", "#5ab793", "#e7823a", "#046c9a", "#4990c9") 

DimPlot(All.LN.data,
        reduction.use = "spring", 
        dim.1 = 1,
        dim.2 = 2,
        do.label=T,
        label.size = 2,
        no.legend = T,
        cols.use = colors)
```

## Hierarchical clustering using scrattch.hicat tools

### Merge clusters to broad neuronal types

```{r}
# Sub-pallial neurons
All.LN.data <- SetIdent(All.LN.data, cells.use = WhichCells(All.LN.data, ident = paste0("LN.GABA.", c(8,11))), ident.use = "dLGE")
All.LN.data <- SetIdent(All.LN.data, cells.use = WhichCells(All.LN.data, ident = paste0("LN.GABA.", c(7,9,10))), ident.use = "Striatal_IN")
All.LN.data <- SetIdent(All.LN.data, cells.use = WhichCells(All.LN.data, ident = paste0("LN.GABA.", c(1))), ident.use = "Cortical_IN")

# Pallial neurons
All.LN.data <- SetIdent(All.LN.data, cells.use = WhichCells(All.LN.data, ident = paste0("LN.Glut.", c(1,13,14))), ident.use = "CR")
All.LN.data <- SetIdent(All.LN.data, cells.use = WhichCells(All.LN.data, ident = paste0("LN.Glut.", c(22))), ident.use = "Fezf1")
All.LN.data <- SetIdent(All.LN.data, cells.use = WhichCells(All.LN.data, ident = paste0("LN.Glut.", c(16,19))), ident.use = "Pcp4")
All.LN.data <- SetIdent(All.LN.data, cells.use = WhichCells(All.LN.data, ident = paste0("LN.Glut.", c(24))), ident.use = "Nr4a2")
All.LN.data <- SetIdent(All.LN.data, cells.use = WhichCells(All.LN.data, ident = paste0("LN.Glut.", c(26,20,21))), ident.use = "Foxp2")
```


### Prepare data for used by scrattch.hicat 

#### Annotation data.frame

```{r}
colors <- c("#ea6569", "#7694c8", "#e6a0c4", "#e2d203", "#096b9a", "#49adc7", "#e58606", "#b31021")

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

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

```{r}
# Filter genes
num.cells <- Matrix::rowSums(All.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)

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

# Find all var genes
All.LN.data <- FindVariableGenes(object = All.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(All.LN.data@raw.data)[rownames(All.LN.data@raw.data) %in% All.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

```{r}
# Take median expression over cluster
cl.med <- get_cl_means(norm.dat, cl.fact)
```

```{r}
# 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)

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

```{r fig.dim=c(5, 3.5)}
# Rotate dendrogramme leafs
NewOrder <- c("Cortical_IN","Striatal_IN","dLGE","CR","Fezf1","Pcp4","Nr4a2","Foxp2")
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), NewOrder) 

# Re-build the dendrogram
cl.med <- get_cl_medians(norm.dat, cl.fact)


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

dend <- dend.result$dend

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

plot(dend.labeled) 
```


## Plot Figure 2B

```{r}
# Load custom plotting functions
source("./functions/ClusterPlotFunctions.R")
```

```{r }
# Import dendrogram into Seurat object
All.LN.data@cluster.tree[[1]] <- ape::as.phylo(dend.labeled)
labels(All.LN.data@cluster.tree[[1]]) <- cl.df[as.numeric(labels(All.LN.data@cluster.tree[[1]])), "cluster_label"]
All.LN.data@ident <- factor(All.LN.data@ident, levels = c("Cortical_IN","Striatal_IN","dLGE","CR","Fezf1","Pcp4","Nr4a2","Foxp2"))
```

```{r fig.dim=c(6, 4), fig.cap= "Manuscript Fig. 2B"}
p1 <- ggdendrogram(dendro_data(as.hclust(dend.labeled)), labels = F, rotate = T) + scale_y_reverse()

p2 <- Cluster.dotplot(All.LN.data,
                      Marker.genes = rev(c("Foxg1","Tbr1", "Slc17a6",
                                       "Reln", "Lhx5", "Neurod6",
                                       "Pcp4", "Fezf1",  "Pbx3",
                                       "Foxp2", "Nr4a2", "Dlx5", "Gad2",
                                       "Six3", "Sp8", "Zfp503", "Isl1",  "Lhx6", "Sst")),
                      min.expression = 0.7, percent.mi=0.15, maxdot.size = 5)

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

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

#Packages used
sessionInfo()
```
