Load libraries
# Load library
library(Seurat)
library(dplyr)
library(patchwork)
#Set ggplot theme as classic
theme_set(theme_classic())To define temporally versus spatially regulated genes, we used dorso-pallial apical progenitors scRNAseq data obtained by facs sorting 1H after flashtag injection from :
Telley L, Agirman G et al. Temporal patterning of apical progenitors and their daughter neurons in the developing neocortex. Science 2019 May 10;364(6440).
Extract and filter apical progenitors from Telley et al 2019
Fetch Telley et al 2019 dataset from GEO
mkdir Telley2019data
wget https://ftp.ncbi.nlm.nih.gov/geo/series/GSE118nnn/GSE118953/suppl/GSE118953_raw_count.tsv.gz -P ./Telley2019data
gunzip ./Telley2019data/GSE118953_raw_count.tsv.gz## --2020-11-30 10:56:18-- https://ftp.ncbi.nlm.nih.gov/geo/series/GSE118nnn/GSE118953/suppl/GSE118953_raw_count.tsv.gz
## Résolution de ftp.ncbi.nlm.nih.gov (ftp.ncbi.nlm.nih.gov)… 130.14.250.11, 2607:f220:41e:250::12, 2607:f220:41e:250::7, ...
## Connexion à ftp.ncbi.nlm.nih.gov (ftp.ncbi.nlm.nih.gov)|130.14.250.11|:443… connecté.
## requête HTTP transmise, en attente de la réponse… 200 OK
## Taille : 19019091 (18M) [application/x-gzip]
## Enregistre : «./Telley2019data/GSE118953_raw_count.tsv.gz»
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## 13500K .......... .......... .......... .......... .......... 72% 719K 3s
## 13550K .......... .......... .......... .......... .......... 73% 5,15M 3s
## 13600K .......... .......... .......... .......... .......... 73% 680K 3s
## 13650K .......... .......... .......... .......... .......... 73% 7,56M 3s
## 13700K .......... .......... .......... .......... .......... 74% 11,9M 3s
## 13750K .......... .......... .......... .......... .......... 74% 693K 3s
## 13800K .......... .......... .......... .......... .......... 74% 11,2M 3s
## 13850K .......... .......... .......... .......... .......... 74% 7,58M 3s
## 13900K .......... .......... .......... .......... .......... 75% 693K 3s
## 13950K .......... .......... .......... .......... .......... 75% 3,92M 3s
## 14000K .......... .......... .......... .......... .......... 75% 9,40M 3s
## 14050K .......... .......... .......... .......... .......... 75% 742K 3s
## 14100K .......... .......... .......... .......... .......... 76% 4,28M 3s
## 14150K .......... .......... .......... .......... .......... 76% 12,1M 3s
## 14200K .......... .......... .......... .......... .......... 76% 1,34M 3s
## 14250K .......... .......... .......... .......... .......... 76% 1,15M 3s
## 14300K .......... .......... .......... .......... .......... 77% 12,5M 3s
## 14350K .......... .......... .......... .......... .......... 77% 11,4M 3s
## 14400K .......... .......... .......... .......... .......... 77% 652K 3s
## 14450K .......... .......... .......... .......... .......... 78% 10,2M 2s
## 14500K .......... .......... .......... .......... .......... 78% 1,39M 2s
## 14550K .......... .......... .......... .......... .......... 78% 1,14M 2s
## 14600K .......... .......... .......... .......... .......... 78% 8,61M 2s
## 14650K .......... .......... .......... .......... .......... 79% 11,6M 2s
## 14700K .......... .......... .......... .......... .......... 79% 684K 2s
## 14750K .......... .......... .......... .......... .......... 79% 8,54M 2s
## 14800K .......... .......... .......... .......... .......... 79% 1,15M 2s
## 14850K .......... .......... .......... .......... .......... 80% 1,34M 2s
## 14900K .......... .......... .......... .......... .......... 80% 1,15M 2s
## 14950K .......... .......... .......... .......... .......... 80% 1,37M 2s
## 15000K .......... .......... .......... .......... .......... 81% 8,59M 2s
## 15050K .......... .......... .......... .......... .......... 81% 11,1M 2s
## 15100K .......... .......... .......... .......... .......... 81% 677K 2s
## 15150K .......... .......... .......... .......... .......... 81% 11,7M 2s
## 15200K .......... .......... .......... .......... .......... 82% 7,28M 2s
## 15250K .......... .......... .......... .......... .......... 82% 623K 2s
## 15300K .......... .......... .......... .......... .......... 82% 11,9M 2s
## 15350K .......... .......... .......... .......... .......... 82% 11,8M 2s
## 15400K .......... .......... .......... .......... .......... 83% 665K 2s
## 15450K .......... .......... .......... .......... .......... 83% 11,8M 2s
## 15500K .......... .......... .......... .......... .......... 83% 12,2M 2s
## 15550K .......... .......... .......... .......... .......... 83% 677K 2s
## 15600K .......... .......... .......... .......... .......... 84% 6,58M 2s
## 15650K .......... .......... .......... .......... .......... 84% 11,7M 2s
## 15700K .......... .......... .......... .......... .......... 84% 688K 2s
## 15750K .......... .......... .......... .......... .......... 85% 7,36M 2s
## 15800K .......... .......... .......... .......... .......... 85% 11,5M 2s
## 15850K .......... .......... .......... .......... .......... 85% 767K 2s
## 15900K .......... .......... .......... .......... .......... 85% 3,74M 2s
## 15950K .......... .......... .......... .......... .......... 86% 1,24M 2s
## 16000K .......... .......... .......... .......... .......... 86% 1,54M 2s
## 16050K .......... .......... .......... .......... .......... 86% 3,78M 1s
## 16100K .......... .......... .......... .......... .......... 86% 1,23M 1s
## 16150K .......... .......... .......... .......... .......... 87% 1,48M 1s
## 16200K .......... .......... .......... .......... .......... 87% 4,52M 1s
## 16250K .......... .......... .......... .......... .......... 87% 703K 1s
## 16300K .......... .......... .......... .......... .......... 88% 11,6M 1s
## 16350K .......... .......... .......... .......... .......... 88% 6,50M 1s
## 16400K .......... .......... .......... .......... .......... 88% 684K 1s
## 16450K .......... .......... .......... .......... .......... 88% 5,81M 1s
## 16500K .......... .......... .......... .......... .......... 89% 10,9M 1s
## 16550K .......... .......... .......... .......... .......... 89% 713K 1s
## 16600K .......... .......... .......... .......... .......... 89% 4,76M 1s
## 16650K .......... .......... .......... .......... .......... 89% 11,6M 1s
## 16700K .......... .......... .......... .......... .......... 90% 1,34M 1s
## 16750K .......... .......... .......... .......... .......... 90% 1,13M 1s
## 16800K .......... .......... .......... .......... .......... 90% 8,98M 1s
## 16850K .......... .......... .......... .......... .......... 90% 739K 1s
## 16900K .......... .......... .......... .......... .......... 91% 4,28M 1s
## 16950K .......... .......... .......... .......... .......... 91% 11,6M 1s
## 17000K .......... .......... .......... .......... .......... 91% 1,36M 1s
## 17050K .......... .......... .......... .......... .......... 92% 1,13M 1s
## 17100K .......... .......... .......... .......... .......... 92% 10,1M 1s
## 17150K .......... .......... .......... .......... .......... 92% 1,39M 1s
## 17200K .......... .......... .......... .......... .......... 92% 1,10M 1s
## 17250K .......... .......... .......... .......... .......... 93% 10,3M 1s
## 17300K .......... .......... .......... .......... .......... 93% 1,38M 1s
## 17350K .......... .......... .......... .......... .......... 93% 1,14M 1s
## 17400K .......... .......... .......... .......... .......... 93% 9,93M 1s
## 17450K .......... .......... .......... .......... .......... 94% 1,29M 1s
## 17500K .......... .......... .......... .......... .......... 94% 1,21M 1s
## 17550K .......... .......... .......... .......... .......... 94% 9,53M 1s
## 17600K .......... .......... .......... .......... .......... 95% 1,24M 1s
## 17650K .......... .......... .......... .......... .......... 95% 1,21M 1s
## 17700K .......... .......... .......... .......... .......... 95% 11,3M 0s
## 17750K .......... .......... .......... .......... .......... 95% 1,29M 0s
## 17800K .......... .......... .......... .......... .......... 96% 1,19M 0s
## 17850K .......... .......... .......... .......... .......... 96% 10,3M 0s
## 17900K .......... .......... .......... .......... .......... 96% 1,31M 0s
## 17950K .......... .......... .......... .......... .......... 96% 1,40M 0s
## 18000K .......... .......... .......... .......... .......... 97% 4,08M 0s
## 18050K .......... .......... .......... .......... .......... 97% 1,31M 0s
## 18100K .......... .......... .......... .......... .......... 97% 1,20M 0s
## 18150K .......... .......... .......... .......... .......... 97% 9,65M 0s
## 18200K .......... .......... .......... .......... .......... 98% 1,32M 0s
## 18250K .......... .......... .......... .......... .......... 98% 1,15M 0s
## 18300K .......... .......... .......... .......... .......... 98% 12,1M 0s
## 18350K .......... .......... .......... .......... .......... 99% 1,32M 0s
## 18400K .......... .......... .......... .......... .......... 99% 1,13M 0s
## 18450K .......... .......... .......... .......... .......... 99% 11,6M 0s
## 18500K .......... .......... .......... .......... .......... 99% 1,35M 0s
## 18550K .......... .......... ... 100% 739K=11s
##
## 2020-11-30 10:56:30 (1,63 MB/s) - «./Telley2019data/GSE118953_raw_count.tsv.gz» enregistré [19019091/19019091]Extract 1H flashtag cells and perform QC filtering
# Load the raw count matrix and extract 1H flashtagged cells
data <- read.table("./Telley2019data/GSE118953_raw_count.tsv", header = T, row.names = 1)
APcells <- grep("1H", colnames(data), value = T)
data <- data[,APcells]
# Initialize the Seurat object
Raw.data <- CreateSeuratObject(raw.data = data,
min.cells = 3,
min.genes = 800,
project = "Telley2019")
Raw.data@meta.data$Barcodes <- rownames(Raw.data@meta.data)
rm(data,APcells)Calculate percentage of mt and ribo counts
# Percent of mitochondrial counts
mito.genes <- grep(pattern = "^mt-", x = rownames(x = Raw.data@data), value = TRUE)
percent.mito <- Matrix::colSums(Raw.data@raw.data[mito.genes, ])/Matrix::colSums(Raw.data@raw.data)
Raw.data <- AddMetaData(object = Raw.data, metadata = percent.mito, col.name = "percent.mito")
# Percent of mitochondrial ribosomal
ribo.genes <- grep(pattern = "(^Rpl|^Rps|^Mrp)", x = rownames(x = Raw.data@data), value = TRUE)
percent.ribo <- Matrix::colSums(Raw.data@raw.data[ribo.genes, ])/Matrix::colSums(Raw.data@raw.data)
Raw.data <- AddMetaData(object = Raw.data, metadata = percent.ribo, col.name = "percent.ribo")# Violin plot
VlnPlot(object = Raw.data, features.plot = c("nGene","nUMI", "percent.mito", "percent.ribo"),
nCol = 2, cols.use = c("#bdd8ef", "#6db8e2", "#357ebc", "#1c4896"))
According to authors’ informations given in the supplementary materials, this dataset contains only QC passed cells.
“Cells expressing < 1000 genes or > 17% of mitochondrial 8 genes were excluded. After this step, 2,756 cells remained for analysis (FT +1 h: E12: 189 cells, E13: 207, E14: 134, E15: 301 […]”
##
## E12.1H E13.1H E14.1H E15.1H
## 189 207 134 301Filter genes, normalize counts and scale expression matrix
# Filter genes expressed by less than 3 cells
num.cells <- Matrix::rowSums(Raw.data@data > 0)
genes.use <- names(x = num.cells[which(x = num.cells >= 10)])
Raw.data@raw.data <- Raw.data@raw.data[genes.use, ]
Raw.data@data <- Raw.data@data[genes.use, ]# logNormalized the gene expression matrix
Raw.data <- NormalizeData(object = Raw.data,
normalization.method = "LogNormalize",
scale.factor = round(median(Raw.data@meta.data$nUMI)),
display.progress = F)# Assign Cell-Cycle Scores
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")
Raw.data <- CellCycleScoring(object = Raw.data,
s.genes = s.genes,
g2m.genes = g2m.genes,
set.ident = F)Explore transcriptional diversity among the progenitors
# Find most variable genes
Raw.data <- FindVariableGenes(object = Raw.data,
mean.function = ExpMean,
dispersion.function = LogVMR,
x.low.cutoff = 0.0125,
x.high.cutoff = 3,
y.cutoff = 1, do.plot = F)
length(Raw.data@var.genes)## [1] 1371# Run PCA
Raw.data <- RunPCA(object = Raw.data,
pcs.compute = 15,
do.print =F)
PCHeatmap(object = Raw.data, pc.use = 1:2, cells.use = 250, do.balanced = TRUE, label.columns = FALSE)
# Perform broad graph-based clustering
Raw.data <- FindClusters(Raw.data,
reduction.type = "pca",
dims.use = 1:10,
k.param = 20,
algorithm = 1,
resolution = 0.6,
print.output = F,
random.seed =1234)
Raw.data <- RunUMAP(Raw.data, dims.use = 1:10, n_neighbors = 20, max.dim = 2)p1 <- DimPlot(Raw.data,
reduction.use = "umap",
dim.1 = 1,
dim.2 = 2,
do.label=T,
label.size = 4,
no.legend = T,
pt.size = 2,
cols.use = c("#ec756d", "#c773a7", "#7293c8", "#b79f0b", "#046c9a"),
do.return = T)
p2 <- DimPlot(Raw.data,
reduction.use = "umap",
group.by ="orig.ident",
dim.1 = 1,
dim.2 = 2,
do.label=T,
label.size = 4,
no.legend = T,
pt.size = 2,
cols.use = c("#bdd8ef", "#6db8e2", "#357ebc", "#1c4896"),
do.return = T)
p1 + p2
# Find all markers between clusters
Allmarkers <- FindAllMarkers(object = Raw.data,
min.pct = 0.3,
logfc.threshold = 0.6,
print.bar = F,
only.pos = T)
Topmarkers <- Allmarkers %>% group_by(cluster) %>% top_n(10, avg_logFC)
DoHeatmap(object = Raw.data,
genes.use = Topmarkers$gene,
use.scaled = TRUE,
slim.col.label = TRUE,
remove.key = T,
col.low = "#4575b4",
col.mid = "#1a1a1a",
col.high = "#fdac61",
cex.row =8)
- Cluster 2 cells express Gad1/2 and Lhx6 and might therefore correspond to cells contaminated with GABAergic neurons transcripts.
- Cluster 3 gathers only E15 cells expressing Wnt5a, thus likely to be from a medial pallial origin.
- Cluster 4 contains 6 cells with unclear transcriptional signature
We excluded these 3 clusters for downstream analysis
Retain only dorso-pallial derived AP form the dataset
Filtered.data <- SubsetData(Raw.data,
ident.use = c(0,1),
subset.raw = T,
do.clean = F)
table(Filtered.data@meta.data$orig.ident)##
## E12.1H E13.1H E14.1H E15.1H
## 178 202 124 221DimPlot(Filtered.data,
reduction.use = "umap",
dim.1 = 1,
dim.2 = 2,
do.label=T,
label.size = 4,
no.legend = T,
pt.size = 2,
cols.use = c("#ec756d", "#c773a7", "#7293c8", "#b79f0b", "#046c9a"),
do.return = T)
# Filter genes expressed by less than 3 cells
num.cells <- Matrix::rowSums(Filtered.data@data > 0)
genes.use <- names(x = num.cells[which(x = num.cells >= 10)])
Filtered.data@raw.data <- Filtered.data@raw.data[genes.use, ]
Filtered.data@data <- Filtered.data@data[genes.use, ]# logNormalized the gene expression matrix
Filtered.data <- NormalizeData(object = Filtered.data,
normalization.method = "LogNormalize",
scale.factor = round(median(Filtered.data@meta.data$nUMI)),
display.progress = F)Session Info
## [1] "30 novembre, 2020, 10,59"## 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] patchwork_0.0.1 dplyr_0.8.3 Seurat_2.3.4 Matrix_1.2-17
## [5] cowplot_1.0.0 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 RColorBrewer_1.1-2
## [64] yaml_2.2.1 reticulate_1.13 pbapply_1.4-2
## [67] gridExtra_2.3 rpart_4.1-15 segmented_1.0-0
## [70] latticeExtra_0.6-28 stringi_1.4.6 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 purrr_0.3.3 labeling_0.3
## [88] htmlwidgets_1.5.1 bit_4.0.4 tidyselect_0.2.5
## [91] plyr_1.8.4 magrittr_1.5 R6_2.4.1
## [94] snow_0.4-3 gplots_3.0.1.1 Hmisc_4.3-0
## [97] pillar_1.4.2 foreign_0.8-72 withr_2.1.2
## [100] fitdistrplus_1.0-14 mixtools_1.1.0 survival_2.44-1.1
## [103] nnet_7.3-14 tsne_0.1-3 tibble_2.1.3
## [106] crayon_1.3.4 hdf5r_1.3.2.9000 KernSmooth_2.23-15
## [109] rmarkdown_2.5 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_0.5.0Institute of Psychiatry and Neuroscience of Paris, INSERM U1266, 75014, Paris, France, matthieu.moreau@inserm.fr↩
---
title: "Preprocessing Telley et al, 2019 dataset"
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

```{r}
# Load library
library(Seurat)
library(dplyr)
library(patchwork)

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

To define temporally versus spatially regulated genes, we used **dorso-pallial apical progenitors** scRNAseq data obtained by facs sorting 1H after flashtag injection from :

> Telley L, Agirman G *et al.* **Temporal patterning of apical progenitors and their daughter neurons in the developing neocortex.** [Science 2019 May 10;364(6440).](https://science.sciencemag.org/content/364/6440/eaav2522.long)

# Extract and filter apical progenitors from Telley et al 2019

## Fetch Telley et al 2019 dataset from GEO

```{bash class.output="scroll-100"}
mkdir Telley2019data

wget https://ftp.ncbi.nlm.nih.gov/geo/series/GSE118nnn/GSE118953/suppl/GSE118953_raw_count.tsv.gz -P ./Telley2019data
gunzip ./Telley2019data/GSE118953_raw_count.tsv.gz
```

## Extract 1H flashtag cells and perform QC filtering

```{r}
# Load the raw count matrix and extract 1H flashtagged cells
data <- read.table("./Telley2019data/GSE118953_raw_count.tsv", header = T, row.names = 1)
APcells <- grep("1H", colnames(data), value = T)

data <- data[,APcells]

# Initialize the Seurat object
Raw.data <- CreateSeuratObject(raw.data = data,
                               min.cells = 3,
                               min.genes = 800,
                               project = "Telley2019")

Raw.data@meta.data$Barcodes <- rownames(Raw.data@meta.data)

rm(data,APcells)
```

## Calculate percentage of mt and ribo counts

```{r}
# Percent of mitochondrial counts
mito.genes <- grep(pattern = "^mt-", x = rownames(x = Raw.data@data), value = TRUE)
percent.mito <- Matrix::colSums(Raw.data@raw.data[mito.genes, ])/Matrix::colSums(Raw.data@raw.data)
Raw.data <- AddMetaData(object = Raw.data, metadata = percent.mito, col.name = "percent.mito")

# Percent of mitochondrial ribosomal
ribo.genes <- grep(pattern = "(^Rpl|^Rps|^Mrp)", x = rownames(x = Raw.data@data), value = TRUE)
percent.ribo <- Matrix::colSums(Raw.data@raw.data[ribo.genes, ])/Matrix::colSums(Raw.data@raw.data)
Raw.data <- AddMetaData(object = Raw.data, metadata = percent.ribo, col.name = "percent.ribo")
```

```{r}
# Violin plot 
VlnPlot(object = Raw.data, features.plot = c("nGene","nUMI", "percent.mito", "percent.ribo"),
        nCol = 2, cols.use = c("#bdd8ef", "#6db8e2", "#357ebc", "#1c4896"))
```

According to authors' informations given in the [supplementary materials](https://science.sciencemag.org/content/sci/suppl/2019/05/08/364.6440.eaav2522.DC1/aav2522_Telley_SM.pdf), this dataset contains only QC passed cells.

> "Cells expressing < 1000 genes or > 17% of mitochondrial 8 genes were excluded. After this step, 2,756 cells remained for analysis (FT +1 h: **E12: 189** cells, **E13: 207**, **E14: 134**,  **E15: 301** [...]"

```{r}
table(Raw.data@ident)
```

## Filter genes, normalize counts and scale expression matrix

```{r}
# Filter genes expressed by less than 3 cells
num.cells <- Matrix::rowSums(Raw.data@data > 0)
genes.use <- names(x = num.cells[which(x = num.cells >= 10)])
Raw.data@raw.data <- Raw.data@raw.data[genes.use, ]
Raw.data@data <- Raw.data@data[genes.use, ]
```

```{r}
# logNormalized the gene expression matrix
Raw.data <- NormalizeData(object = Raw.data,
                         normalization.method = "LogNormalize", 
                         scale.factor = round(median(Raw.data@meta.data$nUMI)),
                         display.progress = F)
```

```{r}
# Assign Cell-Cycle Scores
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")

Raw.data <- CellCycleScoring(object = Raw.data,
                            s.genes = s.genes,
                            g2m.genes = g2m.genes,
                            set.ident = F)
```

```{r}
# We decide to regress out the effect cell cycle  
Raw.data@meta.data$CC.Difference <- Raw.data@meta.data$S.Score - Raw.data@meta.data$G2M.Score
Raw.data <- ScaleData(object = Raw.data,
                     vars.to.regress = c("CC.Difference","percent.mito","nGene", "nUMI"),
                     display.progress = F)
```

# Explore transcriptional diversity among the progenitors

```{r}
# Find most variable genes
Raw.data  <- FindVariableGenes(object = Raw.data,
                               mean.function = ExpMean,
                               dispersion.function = LogVMR,
                               x.low.cutoff = 0.0125,
                               x.high.cutoff = 3,
                              y.cutoff = 1, do.plot = F)

length(Raw.data@var.genes)
```

```{r}
# Run PCA
Raw.data <- RunPCA(object = Raw.data,
                  pcs.compute = 15,
                  do.print =F)

PCHeatmap(object = Raw.data, pc.use = 1:2, cells.use = 250, do.balanced = TRUE, label.columns = FALSE)
```
 
```{r}
# Perform broad graph-based clustering
Raw.data <- FindClusters(Raw.data,
                        reduction.type = "pca",
                        dims.use = 1:10,
                        k.param = 20,
                        algorithm = 1, 
                        resolution = 0.6,
                        print.output = F,
                        random.seed =1234)

Raw.data <- RunUMAP(Raw.data, dims.use = 1:10, n_neighbors = 20, max.dim = 2)
```

```{r fig.dim=c(8, 3)}
p1 <- DimPlot(Raw.data,
              reduction.use = "umap", 
              dim.1 = 1,
              dim.2 = 2,
              do.label=T,
              label.size = 4,
              no.legend = T,
              pt.size = 2,
              cols.use = c("#ec756d", "#c773a7", "#7293c8", "#b79f0b", "#046c9a"),
              do.return = T)

p2 <- DimPlot(Raw.data,
              reduction.use = "umap",
              group.by ="orig.ident", 
              dim.1 = 1,
              dim.2 = 2,
              do.label=T,
              label.size = 4,
              no.legend = T,
              pt.size = 2,
              cols.use = c("#bdd8ef", "#6db8e2", "#357ebc", "#1c4896"),
              do.return = T)

p1 + p2
```

```{r fig.dim=c(9, 6)}
# Find all markers between clusters 
Allmarkers <- FindAllMarkers(object = Raw.data,
                             min.pct = 0.3,
                             logfc.threshold = 0.6,
                             print.bar = F,
                             only.pos = T)

Topmarkers <- Allmarkers %>% group_by(cluster) %>% top_n(10, avg_logFC)
DoHeatmap(object = Raw.data,
          genes.use = Topmarkers$gene,
          use.scaled = TRUE,
          slim.col.label = TRUE,
          remove.key = T,
          col.low = "#4575b4",
          col.mid = "#1a1a1a", 
          col.high = "#fdac61", 
          cex.row =8)
```

- Cluster 2 cells express *Gad1/2* and *Lhx6* and might therefore correspond to cells contaminated with GABAergic neurons transcripts. 
- Cluster 3 gathers only E15 cells expressing *Wnt5a*, thus likely to be from a medial pallial origin. 
- Cluster 4 contains 6 cells with unclear transcriptional signature

<center>
![Allen institute Developing Mouse Brain, Wnt5a ISH](./Wnt5a_E15.png){#id .class width=50% height=50%}
<center>


We excluded these 3 clusters for downstream analysis

# Retain only dorso-pallial derived AP form the dataset

```{r}
Filtered.data <- SubsetData(Raw.data,
                            ident.use = c(0,1),
                            subset.raw = T,
                            do.clean = F)

table(Filtered.data@meta.data$orig.ident)
```

```{r}
DimPlot(Filtered.data,
        reduction.use = "umap", 
        dim.1 = 1,
        dim.2 = 2,
        do.label=T,
        label.size = 4,
        no.legend = T,
        pt.size = 2,
        cols.use = c("#ec756d", "#c773a7", "#7293c8", "#b79f0b", "#046c9a"),
        do.return = T)
```


```{r}
# Filter genes expressed by less than 3 cells
num.cells <- Matrix::rowSums(Filtered.data@data > 0)
genes.use <- names(x = num.cells[which(x = num.cells >= 10)])
Filtered.data@raw.data <- Filtered.data@raw.data[genes.use, ]
Filtered.data@data <- Filtered.data@data[genes.use, ]
```

```{r}
# logNormalized the gene expression matrix
Filtered.data <- NormalizeData(object = Filtered.data,
                               normalization.method = "LogNormalize", 
                               scale.factor = round(median(Filtered.data@meta.data$nUMI)),
                               display.progress = F)
```

```{r}
Filtered.data <- ScaleData(object = Filtered.data,
                           vars.to.regress = c("CC.Difference","percent.mito","nGene", "nUMI"),
                           display.progress = F)
```

```{r}
saveRDS(Filtered.data, "./Telley2019data/Telley2019.RDS")
```


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

#Packages used
sessionInfo()
```