Comparison of pallial and subpallial neurogenesis

Load libraries and the dataset

# Load library
library(Seurat)
library(princurve)
library(monocle)
library(parallel)
library(seriation)

library(dplyr)
library(reshape2)

library(ggplot2)
library(cowplot)
library(ggExtra)
library(patchwork)
library(RColorBrewer)
library(wesanderson)

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

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

colors <-  c("#969696", "#68b041", "#e3c148", "#b7d174", "#83c3b8", "#009fda", "#3E69ac", "#e46b6b",
              "#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,
        no.axes = T,
        cols.use = colors)

Pallial and Subpallial transition state cells

# Calculate pallial BP scores
Pal.BP.genes <- c("Eomes", "Neurog2", "Neurog1", "Prmt8", "Nrp1")
genes.list <- list(Pal.BP.genes)
enrich.name <- "Pal.BP_signature"
Allcells.data <- AddModuleScore(Allcells.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)

# Calculate subpallial BP scores
SP.BP.genes <- c("Dlx1", "Dlx2", "Dlx5","Ascl1", "Gsx2")
genes.list <- list(SP.BP.genes)
enrich.name <- "SP.BP_signature"
Allcells.data <- AddModuleScore(Allcells.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)

set.seed(100)
# Run Kmeans clustering
cl <- kmeans(cbind(Allcells.data@meta.data$SP_signature1,
                   Allcells.data@meta.data$Pal_signature1,
                   Allcells.data@meta.data$SP.BP_signature1,
                   Allcells.data@meta.data$Pal.BP_signature1), 4)

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

col.pal <- wes_palette("GrandBudapest1", 4, type = "discrete")

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

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

Align pallial and subpallial cells along pseudotime

Extract Pallial and Subpallial lineages

# Extract subpallial cells
meankclust.sp.score <- aggregate(SP_signature1 ~ kmeanClust, Allcells.data@meta.data, mean)
SPclust <- meankclust.sp.score %>% filter(SP_signature1 == max(SP_signature1)) %>% pull(kmeanClust)

SPcells <- Allcells.data@meta.data %>%
                  filter( kmeanClust == SPclust ) %>%
                  pull(Barcodes)

SubPalldata <- as.data.frame(Allcells.data@dr$spring@cell.embeddings[SPcells, 1:2])
SubPalldata$Lineage <- "SubPallial"

# Extract Pallial cells
meankclust.p.score <- aggregate(Pal_signature1 ~ kmeanClust, Allcells.data@meta.data, mean)
Pclust <- meankclust.p.score %>% filter(Pal_signature1 == max(Pal_signature1)) %>% pull(kmeanClust)

meankclust.pbp.score <- aggregate(Pal.BP_signature1 ~ kmeanClust, Allcells.data@meta.data, mean)
Pbpclust <- meankclust.pbp.score%>% filter(Pal.BP_signature1 == max(Pal.BP_signature1)) %>% pull(kmeanClust)

Palcells <- Allcells.data@meta.data %>%
                  filter(kmeanClust %in% c(Pclust,Pbpclust)) %>%
                  pull(Barcodes)

Pal.data <- as.data.frame(Allcells.data@dr$spring@cell.embeddings[Palcells,1:2])
Pal.data$Lineage <- "Pallial"

# Extract AP cells
APcells <- Allcells.data@meta.data %>%
                  filter(Cluster.ident %in% grep("*AP", unique(Allcells.data@meta.data$Cluster.ident), value = T)) %>%
                  filter(!Barcodes %in% c(rownames(Pal.data), rownames(SubPalldata))) %>%
                  select(Barcodes,Cluster.ident)

AP.data <- as.data.frame(Allcells.data@dr$spring@cell.embeddings[APcells$Barcodes,1:2])

AP.data$Lineage <- sapply(as.character(APcells$Cluster.ident),
                          function(x) if(x %in% c("AP.Dorsal.Pallium", "AP.lateral.Pallium.1", "AP.lateral.Pallium.2", "AP.Ventral.Pallium")){x= "Pallial"}
                          else{x= "SubPallial"})

Pseudotime.data <- rbind(AP.data,
                         Pal.data,
                         SubPalldata)

Allcells.data <- SubsetData(Allcells.data, cells.use = rownames(Pseudotime.data), subset.raw = T,  do.clean = F)

Pseudotime.data <- Pseudotime.data[rownames(Allcells.data@meta.data),]
Pseudotime.data$Barcode <- rownames(Pseudotime.data)


Allcells.data@meta.data$Lineage <- Pseudotime.data$Lineage

DimPlot(Allcells.data,
        reduction.use = "spring",
        group.by = "Lineage",
        dim.1 = 1,
        dim.2 = 2,
        do.label=T,
        label.size = 2,
        no.legend = T,
        no.axes = T)

Fit the pallial principal curves

#Fit a principal curve to the global trajectory
Pal.data <- Pseudotime.data %>%
              filter(Lineage == "Pallial")

fit <- principal_curve(as.matrix(Pal.data[,1:2]),
                       smoother='lowess',
                       trace=TRUE,
                       f = .7,
                       stretch=0)

## Starting curve---distance^2: 77550139277
## Iteration 1---distance^2: 14983435
## Iteration 2---distance^2: 14563895
## Iteration 3---distance^2: 14248808
## Iteration 4---distance^2: 14111673
## Iteration 5---distance^2: 14101215

pc.line <- as.data.frame(fit$s[order(fit$lambda),])

Pal.data$PseudotimeScore <- fit$lambda/max(fit$lambda)



# Direction of the maturation score using Hmga2 expression (revert if positive correlation)
if (cor(Pal.data$PseudotimeScore, Allcells.data@data['Hmga2', Pal.data$Barcode]) > 0) {
  Pal.data$PseudotimeScore <- -(Pal.data$PseudotimeScore - max(Pal.data$PseudotimeScore))
}

Fit the suballial principal curve

#Fit a principal curve to the global trajectory
SubPal.data <- Pseudotime.data %>%
              filter(Lineage == "SubPallial")

fit <- principal_curve(as.matrix(SubPal.data[,1:2]),
                       smoother='lowess',
                       trace=TRUE,
                       f = .7,
                       stretch=0)

## Starting curve---distance^2: 24540041253
## Iteration 1---distance^2: 9085033
## Iteration 2---distance^2: 8834074
## Iteration 3---distance^2: 8940886
## Iteration 4---distance^2: 9103039
## Iteration 5---distance^2: 9215219
## Iteration 6---distance^2: 9230882
## Iteration 7---distance^2: 9204650
## Iteration 8---distance^2: 9183524
## Iteration 9---distance^2: 9164620
## Iteration 10---distance^2: 9159798

pc.line <- as.data.frame(fit$s[order(fit$lambda),])

SubPal.data$PseudotimeScore <- fit$lambda/max(fit$lambda)



# Direction of the maturation score using Hmga2 expression (revert if positive correlation)
if (cor(SubPal.data$PseudotimeScore, Allcells.data@data['Hmga2', SubPal.data$Barcode]) > 0) {
  SubPal.data$PseudotimeScore <- -(SubPal.data$PseudotimeScore - max(SubPal.data$PseudotimeScore))
}

Pseudotime.data <- rbind(Pal.data,
                         SubPal.data)

rownames(Pseudotime.data) <- Pseudotime.data$Barcode
Pseudotime.data <- Pseudotime.data[rownames(Allcells.data@meta.data),]

Allcells.data@meta.data$PseudotimeScore <- Pseudotime.data$PseudotimeScore

FeaturePlot(object = Allcells.data,
            features.plot = c("PseudotimeScore"),
            cols.use = rev(colorRampPalette(brewer.pal(n =11, name = "Spectral"))(100)),
            no.axes = T,
            reduction.use = "spring",
            no.legend = T)

Filter all cells dataset

Filter the gene counts matrix

# Keep only genes detected in more than 300 cells
num.cells <- Matrix::rowSums(Allcells.data@raw.data > 0)
genes.use <- names(x = num.cells[which(x = num.cells >= 300)])
Allcells.data@raw.data <- Allcells.data@raw.data[genes.use, ]

# Normalization and variable genes selection
Allcells.data <- NormalizeData(object = Allcells.data,
                         normalization.method = "LogNormalize", 
                         scale.factor = round(median(Allcells.data@meta.data$nUMI)),
                         display.progress = F)

Allcells.data <- FindVariableGenes(object = Allcells.data,
                             mean.function = ExpMean,
                             dispersion.function = LogVMR,
                             x.low.cutoff = 0.03,
                             x.high.cutoff = 4,
                             y.cutoff = 0.9, do.plot = F,
                             display.progress = F)

rm(list = ls()[!ls() %in% "Allcells.data"])

Find common transcriptional trajectory

Initialize a monocle object

# Transfer metadata
meta.data <- data.frame(barcode = rownames(Allcells.data@meta.data),
                        Cluster = Allcells.data@meta.data$Cluster.ident,
                        Lineage = Allcells.data@meta.data$Lineage,
                        Pseudotime.Score = Allcells.data@meta.data$PseudotimeScore,
                        row.names = rownames(Allcells.data@meta.data))

Annot.data  <- new('AnnotatedDataFrame', data = meta.data)

# Transfer counts data
count.data = data.frame(gene_short_name = rownames(Allcells.data@raw.data),
                        row.names = rownames(Allcells.data@raw.data))

feature.data <- new('AnnotatedDataFrame', data = count.data)

# Create the CellDataSet object
gbm_cds <- newCellDataSet(as.matrix(Allcells.data@raw.data),
                          phenoData = Annot.data,
                          featureData = feature.data,
                          lowerDetectionLimit = 1,
                          expressionFamily = negbinomial())

gbm_cds <- estimateSizeFactors(gbm_cds)
gbm_cds <- estimateDispersions(gbm_cds)
gbm_cds <- detectGenes(gbm_cds, min_expr = 0.1)

rm(list = ls()[!ls() %in% c("Allcells.data", "gbm_cds")])

Test each gene over pseudotime

# Exclude cell cycle associated genes
CCgenes <- as.character(read.table("./Progenitors/CellCycleGenes.csv", sep = "\t", header = F)[,1])
Input.genes <- Allcells.data@var.genes[!Allcells.data@var.genes %in% CCgenes]

# Split pallial and subpallial cells for gene expression fitting
#Pallial cells
Pallialcells <- Allcells.data@meta.data %>%
                  filter(Lineage == "Pallial") %>%
                  pull(Barcodes)

# Sub-pallial cells
SubPallialcells <- Allcells.data@meta.data %>%
                  filter(Lineage == "SubPallial") %>%
                  pull(Barcodes)

Pallial cells

Pseudotime.diff.Pall <- differentialGeneTest(gbm_cds[Input.genes, Pallialcells],
                                                 fullModelFormulaStr = "~sm.ns(Pseudotime.Score, df = 3)",
                                                 reducedModelFormulaStr = "~1",
                                                 cores = detectCores() - 2)

#Filter based on FDR
Pseudotime.diff.Pall.filtered <- Pseudotime.diff.Pall %>% filter(qval < 1e-3)

Subpallial cells

Pseudotime.diff.SubPallial <- differentialGeneTest(gbm_cds[Input.genes, SubPallialcells],
                                                 fullModelFormulaStr = "~sm.ns(Pseudotime.Score, df = 3)",
                                                 reducedModelFormulaStr = "~1",
                                                 cores = detectCores() - 2)

#Filter based on FDR
Pseudotime.diff.SubPallial.filtered <- Pseudotime.diff.SubPallial  %>% filter(qval < 1e-3)

Intersect the two gene lists

commonGenes <- intersect(Pseudotime.diff.Pall.filtered$gene_short_name, Pseudotime.diff.SubPallial.filtered$gene_short_name)

Smooth common genes expression along the two trajectories

# Create a new pseudo-DV vector of 200 points
nPoints <- 200

new_data = list()
for (Lineage in unique(pData(gbm_cds)$Lineage)){
  new_data[[length(new_data) + 1]] = data.frame(Pseudotime.Score = seq(min(pData(gbm_cds)$Pseudotime.Score), max(pData(gbm_cds)$Pseudotime.Score), length.out = nPoints), Lineage=Lineage)
}

new_data = do.call(rbind, new_data)

# Smooth gene expression
curve_matrix <- genSmoothCurves(gbm_cds[as.character(commonGenes),],
                                trend_formula = "~sm.ns(Pseudotime.Score, df = 3)*Lineage",
                                relative_expr = TRUE,
                                new_data = new_data,
                                cores= detectCores() - 2)

Plot smoothed gene trends along pseudotimes

# Extract genes with person's cor > 0.6 between the 2 trajectories

Pallial.smoothed <- scale(t(curve_matrix[,c(201:400)])) #Pallial points
SubPallial.smoothed <- scale(t(curve_matrix[,c(1:200)])) #SubPallial points

mat <- cor(Pallial.smoothed, SubPallial.smoothed, method = "pearson")

Gene.Cor <- diag(mat)
hist(Gene.Cor,breaks = 100)
abline(v=0.6,col=c("blue"))

PanNeuro.genes <- names(Gene.Cor[Gene.Cor > 0.6])

# We further filter genes detected in less than 300 or 100 cells along Pallial or Subpallial lineages, respectively
num.cells <- Matrix::rowSums(Allcells.data@raw.data[,Pallialcells] > 0)
PallGenes <- names(x = num.cells[which(x = num.cells >= 300)])

num.cells <- Matrix::rowSums(Allcells.data@raw.data[,SubPallialcells] > 0)
SubPallGenes <- names(x = num.cells[which(x = num.cells >= 100)])

PanNeuro.genes <- PanNeuro.genes[PanNeuro.genes %in% intersect(PallGenes, SubPallGenes)]

# Order rows using seriation
dst <- as.dist((1-cor(scale(t(curve_matrix[PanNeuro.genes,c(1:200)])), method = "pearson")))
row.ser <- seriate(dst, method ="OLO_complete")
gene.order <- PanNeuro.genes[get_order(row.ser)]

anno.colors <- list(lineage = c(Pallial="#026c9a",SubPallial="#cc391b"))


pheatmap::pheatmap(curve_matrix[rev(gene.order),
                                c(400:201, #Pallial points
                                  1:200)], #SubPallial points
                   scale = "row",
                   cluster_rows = F,
                   cluster_cols = F,
                   annotation_col = data.frame(lineage = rep(c("SubPallial","Pallial"), each=200)),
                   annotation_colors = anno.colors,
                   show_colnames = F,
                   show_rownames = T,
                   fontsize_row = 2,
                   color =  viridis::viridis(9),
                   breaks = seq(-2.5,2.5, length.out = 9),
                   main = "")

write.table(rev(gene.order), "./Trajectories/PanNeuroGenes.csv", sep = ";", quote = F)

Plot relevant gene trends

source("./functions/TrajectoriesPlotFunctions.R")

Plot.Genes.trend(Allcells.data,
                 genes = c("Sox2", "Hmga2",
                           "Hes6","Mfng",
                           "St18", "Myt1",
                           "Neurog2","Slc17a6",
                           "Dlx1", "Gad2"),
                 color.by = "lineage",
                 Use.scale.data = F)

plot <- FeaturePlot(object = Allcells.data,
                    features.plot =  c("Sox2", "Hmga2",
                                       "Hes6", "Mfng",
                                       "St18","Myt1"),
                    cols.use = c("grey90", brewer.pal(9,"YlGnBu")),
                    reduction.use = "spring",
                    no.legend = T,
                    pt.size = 1,
                    overlay = F,
                    dark.theme = F,
                    do.return =T,
                    no.axes = T)

for (i in 1:length(plot)){
  plot[[i]]$data <- plot[[i]]$data[order(plot[[i]]$data$gene),]
}

cowplot::plot_grid(plotlist = plot[1:6], ncol =2)

Find DEG along the panGlutamatergic and panGABAergic trajectories

Pseudotime.lineages.diff <- differentialGeneTest(gbm_cds[Input.genes,], 
                                                 fullModelFormulaStr = "~sm.ns(Pseudotime.Score, df = 3)*Lineage", 
                                                 reducedModelFormulaStr = "~sm.ns(Pseudotime.Score, df = 3)", 
                                                 cores = detectCores() - 2)

# Filter genes based on FDR
Pseudotime.lineages.diff.filtered <- Pseudotime.lineages.diff %>% filter(qval < 1e-3)

Direction of the DEG by calculating the area between curves (ABC)

Smooth common genes along the two trajectories

# Create a new pseudo-DV vector of 200 points
nPoints <- 200

new_data = list()
for (Lineage in unique(pData(gbm_cds)$Lineage)){
  new_data[[length(new_data) + 1]] = data.frame(Pseudotime.Score = seq(min(pData(gbm_cds)$Pseudotime.Score), max(pData(gbm_cds)$Pseudotime.Score), length.out = nPoints), Lineage=Lineage)
}

new_data = do.call(rbind, new_data)

# Smooth gene expression
Diff.curve_matrix <- genSmoothCurves(gbm_cds[as.character(Pseudotime.lineages.diff.filtered$gene_short_name),],
                                      trend_formula = "~sm.ns(Pseudotime.Score, df = 3)*Lineage",
                                      relative_expr = TRUE,
                                      new_data = new_data,
                                      cores= detectCores() - 2)

Compute the ABC for each gene

SubPallial_curve_matrix <- Diff.curve_matrix[, 1:nPoints] #SubPallial points
Pallial_curve_matrix <- Diff.curve_matrix[, (nPoints + 1):(2 * nPoints)] #Pallial points
  
ABCs_res <- SubPallial_curve_matrix - Pallial_curve_matrix # Direction of the comparison : postive ABCs <=> Upregulated in SubPallial lineage
ILR_res <- log2(SubPallial_curve_matrix/ (Pallial_curve_matrix + 0.1)) # Average logFC between the 2 curves
  
ABCs_res <- apply(ABCs_res, 1, function(x, nPoints) {
                  avg_delta_x <- (x[1:(nPoints - 1)] + x[2:(nPoints)])/2
                  step <- (100/(nPoints - 1))
                  res <- round(sum(avg_delta_x * step), 3)
                  return(res)},
                  nPoints = nPoints) # Compute the area below the curve
  
ABCs_res <- cbind(ABCs_res, ILR_res[,ncol(ILR_res)])
colnames(ABCs_res)<- c("ABCs", "Endpoint_ILR")

# Import ABC values into the DE test results table
Pseudotime.lineages.diff.filtered <- cbind(Pseudotime.lineages.diff.filtered[,1:4],
                                           ABCs_res,
                                           Pseudotime.lineages.diff.filtered[,5:6])

Plot pallial specific trajectory

# Extract Pallial neurons trajectory genes
Pallial.res <- as.data.frame(Pseudotime.lineages.diff.filtered[Pseudotime.lineages.diff.filtered$ABCs < 0,])
Pallial.genes <- row.names(Pallial.res)

# We further filter genes detected in less than 300 and more than 250 cells among Pallial and Subpallial lineages, respectively
num.cells <- Matrix::rowSums(Allcells.data@raw.data[Pallial.genes, Pallialcells] > 0)
Pallial.genes <- names(x = num.cells[which(x = num.cells >= 300)])

num.cells <- Matrix::rowSums(Allcells.data@raw.data[Pallial.genes, SubPallialcells] > 0)
filtered <- names(x = num.cells[which(x = num.cells >= 250)])

Pallial.genes <- Pallial.genes[!Pallial.genes %in% filtered]

Pallial_curve_matrix <- Pallial_curve_matrix[Pallial.genes, ]

# Order rows using seriation
dst <- as.dist((1-cor(scale(t(Pallial_curve_matrix)), method = "pearson")))
row.ser <- seriate(dst, method ="GW")
gene.order <- rownames(Pallial_curve_matrix[get_order(row.ser),])

anno.colors <- list(lineage = c(Pallial="#026c9a",SubPallial="#cc391b"))


pheatmap::pheatmap(Diff.curve_matrix[gene.order,
                                c(400:201, #Pallial points
                                  1:200)], #SubPallial points
                   scale = "row",
                   cluster_rows = F,
                   cluster_cols = F,
                   annotation_col = data.frame(lineage = rep(c("SubPallial","Pallial"), each=200)),
                   annotation_colors = anno.colors,
                   show_colnames = F,
                   show_rownames = T,
                   fontsize_row = 8,
                   color =  viridis::viridis(9),
                   breaks = seq(-2.5,2.5, length.out = 9),
                   main = "")

Pallial.Common <- Pseudotime.lineages.diff.filtered[Pseudotime.lineages.diff.filtered$gene_short_name %in% Pallial.genes, ]

write.table(Pallial.Common[gene.order,], "./Trajectories/Pallial_Common_Genes.csv", sep = ";", quote = F)

Plot SubPallial specific trajectory

# Extract subPallial neurons trajectory genes
SubPallial.res <- as.data.frame(Pseudotime.lineages.diff.filtered[Pseudotime.lineages.diff.filtered$ABCs > 0,])
SubPallial.genes <- row.names(SubPallial.res)
SubPallial_curve_matrix <- SubPallial_curve_matrix[SubPallial.genes, ]

# Extract subPallial neurons trajectory genes
SubPallial.res <- as.data.frame(Pseudotime.lineages.diff.filtered[Pseudotime.lineages.diff.filtered$ABCs > 0,])
SubPallial.genes <- row.names(SubPallial.res)

# We further filter genes detected in less than 100 and more than 300 cells among Subpallial and Pallial lineages, respectively
num.cells <- Matrix::rowSums(Allcells.data@raw.data[SubPallial.genes, SubPallialcells] > 0)
SubPallial.genes <- names(x = num.cells[which(x = num.cells >= 100)])

num.cells <- Matrix::rowSums(Allcells.data@raw.data[SubPallial.genes, Pallialcells] > 0)
filtered <- names(x = num.cells[which(x = num.cells >= 300)])

SubPallial.genes <- SubPallial.genes[!SubPallial.genes%in% filtered]

SubPallial_curve_matrix <- SubPallial_curve_matrix[SubPallial.genes, ]

# Order rows using seriation
dst <- as.dist((1-cor(scale(t(SubPallial_curve_matrix)), method = "pearson")))
row.ser <- seriate(dst, method ="GW")
gene.order <- rownames(SubPallial_curve_matrix[get_order(row.ser),])

anno.colors <- list(lineage = c(Pallial="#026c9a",SubPallial="#cc391b"))


pheatmap::pheatmap(Diff.curve_matrix[gene.order,
                                c(400:201, #Pallial points
                                  1:200)], #SubPallial points
                   scale = "row",
                   cluster_rows = F,
                   cluster_cols = F,
                   annotation_col = data.frame(lineage = rep(c("SubPallial","Pallial"), each=200)),
                   annotation_colors = anno.colors,
                   show_colnames = F,
                   show_rownames = T,
                   fontsize_row = 8,
                   color =  viridis::viridis(9),
                   breaks = seq(-2.5,2.5, length.out = 9),
                   main = "")

SubPallial.Common <- Pseudotime.lineages.diff.filtered[Pseudotime.lineages.diff.filtered$gene_short_name %in% SubPallial.genes, ]

write.table(SubPallial.Common[gene.order,], "./Trajectories/SubPallial_Common_Genes.csv", sep = ";", quote = F)

Session Info

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

## [1] "03 mai, 2021, 11,51"

#Packages used
sessionInfo()

## R version 3.6.3 (2020-02-29)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.5 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/atlas/libblas.so.3.10.3
## LAPACK: /usr/lib/x86_64-linux-gnu/atlas/liblapack.so.3.10.3
## 
## locale:
##  [1] LC_CTYPE=fr_FR.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=fr_FR.UTF-8        LC_COLLATE=fr_FR.UTF-8    
##  [5] LC_MONETARY=fr_FR.UTF-8    LC_MESSAGES=fr_FR.UTF-8   
##  [7] LC_PAPER=fr_FR.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=fr_FR.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
##  [1] splines   stats4    parallel  stats     graphics  grDevices utils    
##  [8] datasets  methods   base     
## 
## other attached packages:
##  [1] wesanderson_0.3.6   RColorBrewer_1.1-2  patchwork_0.0.1    
##  [4] ggExtra_0.9         reshape2_1.4.3      dplyr_0.8.3        
##  [7] seriation_1.2-9     monocle_2.14.0      DDRTree_0.1.5      
## [10] irlba_2.3.3         VGAM_1.1-2          Biobase_2.46.0     
## [13] BiocGenerics_0.32.0 princurve_2.1.4     Seurat_2.3.4       
## [16] Matrix_1.2-17       cowplot_1.0.0       ggplot2_3.2.1      
## 
## 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           igraph_1.2.5         lazyeval_0.2.2      
##   [7] densityClust_0.3     fastICA_1.2-2        digest_0.6.25       
##  [10] foreach_1.4.7        htmltools_0.5.0      viridis_0.5.1       
##  [13] lars_1.2             gdata_2.18.0         magrittr_1.5        
##  [16] checkmate_1.9.4      cluster_2.1.0        gclus_1.3.2         
##  [19] mixtools_1.1.0       ROCR_1.0-7           limma_3.42.0        
##  [22] matrixStats_0.55.0   R.utils_2.9.0        docopt_0.6.1        
##  [25] colorspace_1.4-1     ggrepel_0.8.1        xfun_0.18           
##  [28] sparsesvd_0.2        crayon_1.4.0         jsonlite_1.7.0      
##  [31] zeallot_0.1.0        survival_2.44-1.1    zoo_1.8-6           
##  [34] iterators_1.0.12     ape_5.3              glue_1.4.1          
##  [37] registry_0.5-1       gtable_0.3.0         kernlab_0.9-29      
##  [40] prabclus_2.3-1       DEoptimR_1.0-8       scales_1.1.0        
##  [43] pheatmap_1.0.12      bibtex_0.4.2         miniUI_0.1.1.1      
##  [46] Rcpp_1.0.5           metap_1.1            dtw_1.21-3          
##  [49] xtable_1.8-4         viridisLite_0.3.0    htmlTable_1.13.2    
##  [52] reticulate_1.18      foreign_0.8-72       bit_4.0.4           
##  [55] proxy_0.4-23         mclust_5.4.5         SDMTools_1.1-221.1  
##  [58] Formula_1.2-3        tsne_0.1-3           htmlwidgets_1.5.1   
##  [61] httr_1.4.1           FNN_1.1.3            gplots_3.0.1.1      
##  [64] fpc_2.2-3            acepack_1.4.1        modeltools_0.2-22   
##  [67] ica_1.0-2            farver_2.0.1         pkgconfig_2.0.3     
##  [70] R.methodsS3_1.7.1    flexmix_2.3-15       nnet_7.3-14         
##  [73] labeling_0.3         later_1.0.0          tidyselect_0.2.5    
##  [76] rlang_0.4.7          munsell_0.5.0        tools_3.6.3         
##  [79] ggridges_0.5.1       fastmap_1.0.1        evaluate_0.14       
##  [82] stringr_1.4.0        yaml_2.2.1           npsurv_0.4-0        
##  [85] knitr_1.26           bit64_4.0.2          fitdistrplus_1.0-14 
##  [88] robustbase_0.93-5    caTools_1.17.1.2     purrr_0.3.3         
##  [91] RANN_2.6.1           pbapply_1.4-2        nlme_3.1-141        
##  [94] mime_0.7             slam_0.1-46          R.oo_1.23.0         
##  [97] hdf5r_1.3.2.9000     compiler_3.6.3       rstudioapi_0.11     
## [100] png_0.1-7            lsei_1.2-0           tibble_2.1.3        
## [103] stringi_1.4.6        lattice_0.20-41      HSMMSingleCell_1.6.0
## [106] vctrs_0.2.0          pillar_1.4.2         lifecycle_0.1.0     
## [109] combinat_0.0-8       Rdpack_0.11-0        lmtest_0.9-37       
## [112] data.table_1.12.6    bitops_1.0-6         gbRd_0.4-11         
## [115] httpuv_1.5.2         R6_2.4.1             latticeExtra_0.6-28 
## [118] promises_1.1.0       TSP_1.1-10           KernSmooth_2.23-15  
## [121] gridExtra_2.3        codetools_0.2-16     MASS_7.3-53         
## [124] gtools_3.8.1         assertthat_0.2.1     withr_2.1.2         
## [127] qlcMatrix_0.9.7      diptest_0.75-7       doSNOW_1.0.18       
## [130] grid_3.6.3           rpart_4.1-15         tidyr_1.0.0         
## [133] class_7.3-17         rmarkdown_2.5        segmented_1.0-0     
## [136] Rtsne_0.15           shiny_1.4.0          base64enc_0.1-3

---

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