Choroid plexus differentiation trajectory

In this part of the analysis we apply Revelio algorithm to explore cell cycle dynamic of pallial and hem domain radial glial cells

Load libraries

library(Seurat)
library(Revelio)
library(princurve)
library(monocle)
library(gprofiler2)
library(orthologsBioMART)
library(Matrix)
library(dplyr)
library(RColorBrewer)
library(ggplot2)
library(ggExtra)
library(cowplot)
library(wesanderson)

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

Load and filter progenitors data

Hem.data <- readRDS("../QC.filtered.clustered.cells.RDS")

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

Fit Pseudotime axis on ChP cells

ChP.data <-  subset(Hem.data, idents = c("ChP", "ChP_progenitors"))

DimPlot(ChP.data,
        reduction = "spring",
        pt.size = 1,
        cols =  c("#83c3b8", "#009fda")) + NoAxes()

Exclude septal cells

FeaturePlot(object = ChP.data ,
            features = c("Fgf8", "Fgf17", "Adamts15", "Fgfbp1"),
            pt.size = 0.5,
            cols = c("grey90", brewer.pal(9,"YlGnBu")),
            reduction = "spring",
            order = T) & NoAxes() & NoLegend()

ChP.data <- AddModuleScore(ChP.data,
                           features = list(c("Fgf8", "Fgf17", "Adamts15", "Fgfbp1")),
                           ctrl = 10,
                           name = "Septum")

FeaturePlot(object = ChP.data ,
            features = c("Septum1"),
            pt.size = 0.5,
            cols = rev(brewer.pal(10,"Spectral")),
            reduction = "spring",
            order = T) & NoAxes()

hist(ChP.data$Septum1, breaks = 20)

ChP.data$Septal.prog <- ChP.data$Septum1 > 0

p1 <- DimPlot(ChP.data,
        reduction = "spring",
        group.by = "Septal.prog",
        pt.size = 1) + NoAxes()

p2 <- FeaturePlot(object = ChP.data ,
            features = c("Fgf17"),
            pt.size = 0.5,
            cols = c("grey90", brewer.pal(9,"YlGnBu")),
            reduction = "spring",
            order = T) & NoAxes() & NoLegend()

p1 + p2

ChP.data <- subset(ChP.data,
                   subset = Septal.prog == FALSE & ChP.data$Spring_1 > 1300)

DimPlot(ChP.data,
        reduction = "spring",
        pt.size = 1,
        cols =  c("#83c3b8", "#009fda")) + NoAxes()

Fit principal curve

Trajectories.ChP <- ChP.data@meta.data %>%
                    dplyr::select("Barcodes", "nUMI", "Spring_1", "Spring_2")

fit <- principal_curve(as.matrix(Trajectories.ChP[,c("Spring_1", "Spring_2")]),
                       smoother='lowess',
                       trace=TRUE,
                       f = 0.8, 
                       stretch=2)

## Starting curve---distance^2: 117510509591
## Iteration 1---distance^2: 46558929
## Iteration 2---distance^2: 42504599
## Iteration 3---distance^2: 41393217
## Iteration 4---distance^2: 40991617
## Iteration 5---distance^2: 40890588
## Iteration 6---distance^2: 40826638
## Iteration 7---distance^2: 40830824

#The principal curve smoothed
ChP.pc.line <- as.data.frame(fit$s[order(fit$lambda),]) 

#Pseudotime score
Trajectories.ChP$Pseudotime <- fit$lambda/max(fit$lambda)

#Inverse the score if positive correlation with progenitor marker
if (cor(Trajectories.ChP$Pseudotime, ChP.data@assays$SCT@data['Hmga2', Trajectories.ChP$Barcodes]) > 0) {
  Trajectories.ChP$Pseudotime <- -(Trajectories.ChP$Pseudotime - max(Trajectories.ChP$Pseudotime))
}

ChP.data$Pseudotime <- Trajectories.ChP$Pseudotime

FeaturePlot(object = ChP.data,
            features = "Pseudotime",
            pt.size = 2,
            cols = rev(colorRampPalette(brewer.pal(n =10, name = "Spectral"))(100)),
            reduction = "spring",
            order = T) & NoAxes()

Subset progenitors and fit cell cycle axis

Prog.data <-  subset(ChP.data, idents = c("ChP_progenitors"))

DimPlot(Prog.data,
        reduction = "spring",
        pt.size = 1,
        cols =  c("#009fda")) + NoAxes()

Prog.data <- NormalizeData(Prog.data, normalization.method = "LogNormalize", scale.factor = 10000, assay = "RNA")

Prepare data for revelio input

Export counts matrix

rawCounts <- as.matrix(Prog.data[["RNA"]]@counts)

# Filter genes expressed by less than 10 cells
num.cells <- Matrix::rowSums(rawCounts > 0)
genes.use <- names(x = num.cells[which(x = num.cells >= 10)])
rawCounts <- rawCounts[genes.use, ]

rm(list = ls()[!ls() %in% c("rawCounts", "CCgenes", "ChP.data", "Prog.data")])
gc()

##             used  (Mb) gc trigger   (Mb)  max used   (Mb)
## Ncells   6031711 322.2    8756530  467.7   8756530  467.7
## Vcells 109360801 834.4  603966152 4607.9 620030699 4730.5

Run Revelio

CCgenes <- read.table("CCgenes.csv", sep = ";", header = T)

We can now follow the tutorial form the package github page

myData <- createRevelioObject(rawData = rawCounts,
                              cyclicGenes = CCgenes,
                              lowernGeneCutoff = 0,
                              uppernUMICutoff = Inf,
                              ccPhaseAssignBasedOnIndividualBatches = F)

## 2022-02-24 16:17:15: reading data: 1.63secs

rm("rawCounts")
gc()

##             used  (Mb) gc trigger   (Mb)  max used   (Mb)
## Ncells   6050902 323.2    8756530  467.7   8756530  467.7
## Vcells 109450747 835.1  483172922 3686.4 620030699 4730.5

The getCellCyclePhaseAssignInformation filter “outlier” cells for cell cycle phase assignation. We modified the function to keep all cells as we observed this does not affect the global cell cycle fitting procedure

source("../Functions/functions_InitializationCCPhaseAssignFiltering.R")

myData <- getCellCyclePhaseAssign_allcells(myData)

## 2022-02-24 16:17:20: assigning cell cycle phases: 8.68secs

myData <- getPCAData(dataList = myData)

## 2022-02-24 16:17:35: calculating PCA: 8.72secs

myData <- getOptimalRotation(dataList = myData)

## 2022-02-24 16:17:58: calculating optimal rotation: 1.14secs

gc()

##             used   (Mb) gc trigger   (Mb)  max used   (Mb)
## Ncells   6082646  324.9   11352458  606.3  11352458  606.3
## Vcells 146912240 1120.9  483172922 3686.4 620030699 4730.5

Graphical exploration of the infered cell cycle axis

CellCycledata <- cbind(as.data.frame(t(myData@transformedData$dc$data[1:2,])),
                       nUMI= myData@cellInfo$nUMI,
                       Revelio.phase = factor(myData@cellInfo$ccPhase, levels = c("G1.S", "S", "G2", "G2.M", "M.G1")),
                       Revelio.cc= myData@cellInfo$ccPercentageUniformlySpaced,
                       Seurat.cc= Prog.data@meta.data[myData@cellInfo$cellID,"CC.Difference"])

Cells distribution in the DC1-DC2 space

ggplot(CellCycledata, aes(DC1, DC2)) +
        geom_point(aes(color = Revelio.phase)) +
        scale_color_manual(values= c(wes_palette("FantasticFox1")[1:3],"grey40",wes_palette("FantasticFox1")[5]))

p1 <- ggplot(CellCycledata, aes(DC1, DC2)) +
        geom_point(aes(color = Revelio.phase)) +
        scale_color_manual(values= c(wes_palette("FantasticFox1")[1:3],"grey40",wes_palette("FantasticFox1")[5]))

p2 <- ggplot(CellCycledata, aes(DC1, DC2)) +
        geom_point(aes(color=Revelio.cc), size=2, shape=16) + 
        scale_color_gradientn(colours=rev(colorRampPalette(brewer.pal(n =11, name = "Spectral"))(100)),
                              name='Revelio_cc')


p1 + p2

ggplot(CellCycledata, aes(x= Revelio.cc, y= nUMI/10000)) +
        geom_point(aes(color= Revelio.phase), size=0.5) +
        scale_color_manual(values= c(wes_palette("FantasticFox1")[1:3],"grey40",wes_palette("FantasticFox1")[5])) +
        geom_smooth(method="loess", n= 50, fill="grey") +
        ylim(0,NA)

Import coordinates

Prog.data$Revelio.DC1 <- CellCycledata$DC1
Prog.data$Revelio.DC2 <- CellCycledata$DC2

Prog.data$Revelio.phase <- CellCycledata$Revelio.phase
Prog.data$Revelio.cc <- CellCycledata$Revelio.cc

p1 <- FeaturePlot(object = Prog.data,
            features = "Revelio.cc",
            pt.size = 1,
            cols = rev(brewer.pal(10,"Spectral")),
            reduction = "spring",
            order = T) & NoAxes()

p2 <- DimPlot(object = Prog.data,
        group.by = "Revelio.phase",
        pt.size = 1,
        reduction = "spring",
        cols =  c(wes_palette("FantasticFox1")[1:3],"grey40",wes_palette("FantasticFox1")[5])) & NoAxes()

p3 <- FeaturePlot(object = Prog.data,
            features = "Pseudotime",
            pt.size = 2,
            cols = rev(colorRampPalette(brewer.pal(n =10, name = "Spectral"))(100)),
            reduction = "spring",
            order = T) & NoAxes()

p1 + p2 + p3 

Trajectories.progenitors <- Prog.data@meta.data %>%
                              dplyr::select(Barcodes, nUMI, Spring_1, Spring_2, Pseudotime) %>% 
                              mutate(Cycling.axis= Prog.data$Revelio.cc,
                                     Phase = Prog.data$Revelio.phase,
                                     Gmnc= Prog.data@assays$RNA@data["Gmnc",],
                                     Ttr= Prog.data@assays$RNA@data["Ttr",],
                                     Htr2c= Prog.data@assays$RNA@data["Htr2c",],
                                     Top2a= Prog.data@assays$RNA@data["Top2a",])

p1 <- ggplot(Trajectories.progenitors, aes(x= Pseudotime, y= Cycling.axis)) +
        geom_point(aes(color= Phase), size=1.5) +
        scale_color_manual(values= c(wes_palette("FantasticFox1")[1:3],"grey40",wes_palette("FantasticFox1")[5]))

p2 <- Trajectories.progenitors %>% arrange(Gmnc) %>%
      ggplot(aes(x= Pseudotime, y= Cycling.axis)) +
        geom_point(aes(color=Gmnc), size=1.5) +
        scale_color_gradientn(colours =c("grey90", brewer.pal(9,"YlGnBu")))

p3 <- Trajectories.progenitors %>% arrange(Ttr) %>%
      ggplot(aes(x= Pseudotime, y= Cycling.axis)) +
        geom_point(aes(color=Ttr), size=1.5) +
        scale_color_gradientn(colours =c("grey90", brewer.pal(9,"YlGnBu")))

p4 <- Trajectories.progenitors %>% arrange(Htr2c) %>%
      ggplot(aes(x= Pseudotime, y= Cycling.axis)) +
        geom_point(aes(color=Htr2c), size=1.5) +
        scale_color_gradientn(colours =c("grey90", brewer.pal(9,"YlGnBu")))

p1 + p2 + p3 + p4  + patchwork::plot_layout(ncol = 2)

rm(list = ls()[!ls() %in% c("Trajectories.progenitors", "ChP.data")])
gc()

##            used  (Mb) gc trigger   (Mb)  max used   (Mb)
## Ncells  6109035 326.3   11352458  606.3  11352458  606.3
## Vcells 55782339 425.6  386538338 2949.1 620030699 4730.5

Import progenitors cycling coordinates in the full dataset

ChP.data$Cycling.axis <- sapply(ChP.data$Barcodes,
                              FUN = function(x) {
                                if (x %in% Trajectories.progenitors$Barcodes) {
                                  x = Trajectories.progenitors[x, "Cycling.axis"]
                                } else {
                                  x = NA
                                  }
                              })

FeaturePlot(object = ChP.data,
            features = "Pseudotime",
            pt.size = 2,
            cols = rev(colorRampPalette(brewer.pal(n =10, name = "Spectral"))(100)),
            reduction = "spring",
            order = T) & NoAxes()

Use monocle2 to model gene expression along differentition axis

Initialize a monocle object

# Transfer metadata
meta.data <- data.frame(Barcode= ChP.data$Barcodes,
                        Pseudotime= ChP.data$Pseudotime,
                        Cell.cycle= ChP.data$Phase)

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

# Transfer counts data
ChP.data <- FindVariableFeatures(ChP.data, selection.method = "vst", nfeatures = 2000)
var.genes <- VariableFeatures(ChP.data)
count.data = data.frame(gene_short_name = rownames(ChP.data[["RNA"]]@data[var.genes,]),
                        row.names = rownames(ChP.data[["RNA"]]@data[var.genes,]))

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

# Create the CellDataSet object including variable genes only
gbm_cds <- newCellDataSet(ChP.data[["RNA"]]@counts[var.genes,],
                          phenoData = Annot.data,
                          featureData = feature.data,
                          lowerDetectionLimit = 0,
                          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("ChP.data", "gbm_cds")])
gc()

##            used  (Mb) gc trigger   (Mb)  max used   (Mb)
## Ncells  6163153 329.2   11352458  606.3  11352458  606.3
## Vcells 57165822 436.2  309230671 2359.3 620030699 4730.5

Test each gene trend over pseudotime score

pseudo.maturation.diff <- differentialGeneTest(gbm_cds[fData(gbm_cds)$num_cells_expressed >= 30,], 
                                                 fullModelFormulaStr = "~sm.ns(Pseudotime, df = 3)", 
                                                 reducedModelFormulaStr = "~1", 
                                                 cores = parallel::detectCores() - 2)

# Filter genes based on FDR
pseudo.maturation.diff.filtered <- pseudo.maturation.diff %>% filter(qval < 5e-5)

Smooth expression of significative genes

# Create a new vector of 200 points
nPoints <- 200
new_data <- data.frame(Pseudotime = seq(min(pData(gbm_cds)$Pseudotime), max(pData(gbm_cds)$Pseudotime), length.out = nPoints))

# Smooth gene expression
Smooth.curve.matrix <- genSmoothCurves(gbm_cds[as.character(pseudo.maturation.diff.filtered$gene_short_name),],
                                       trend_formula = "~sm.ns(Pseudotime, df = 3)",
                                       relative_expr = TRUE,
                                       new_data = new_data,
                                       cores= parallel::detectCores() - 2)

Cluster genes and plot heatmap

## Cluster gene by expression profiles
Pseudotime.genes.clusters <- cluster::pam(as.dist((1 - cor(Matrix::t(Smooth.curve.matrix),method = "pearson"))), k= 4)

ChP.Gene.dynamique <- data.frame(Gene= names(Pseudotime.genes.clusters$clustering),
                                 Waves= Pseudotime.genes.clusters$clustering,
                                 Gene.Clusters = Pseudotime.genes.clusters$clustering,
                                 q.val = pseudo.maturation.diff.filtered$qval
                                 ) %>% arrange(Gene.Clusters)

row.names(ChP.Gene.dynamique) <- ChP.Gene.dynamique$Gene
ChP.Gene.dynamique$Gene.Clusters <- paste0("Clust.", ChP.Gene.dynamique$Gene.Clusters)

# Order the rows using seriation
dst <- as.dist((1-cor(scale(t(Smooth.curve.matrix)), method = "spearman")))
row.ser <- seriation::seriate(dst, method ="R2E") #"R2E" #TSP #"GW" "GW_ward"
gene.order <- rownames(Smooth.curve.matrix[seriation::get_order(row.ser),])

pal <- wes_palette("Darjeeling1")
anno.colors <- list(Cell.state = c(Cycling_RG="#046c9a", Differentiating_cells="#ebcb2e"),
                    Gene.Clusters = c(Clust.1 =pal[1] , Clust.2=pal[2], Clust.3=pal[3], Clust.4=pal[4], Clust.5=pal[5]))

pheatmap::pheatmap(Smooth.curve.matrix[gene.order,],
                   scale = "row",
                   cluster_rows = F,
                   cluster_cols = F,
                   annotation_row = ChP.Gene.dynamique %>% dplyr::select(Gene.Clusters),
                   annotation_colors = anno.colors,
                   show_colnames = F,
                   show_rownames = F,
                   fontsize_row = 8,
                   color =  viridis::viridis(9),
                   breaks = seq(-2.5,2.5, length.out = 9),
                   main = "")

GO term enrichment in gene clusters using gprofiler2

ChP.gostres <- gost(query = list("Clust.1" = ChP.Gene.dynamique %>% filter(Gene.Clusters == "Clust.1") %>% pull(Gene) %>% as.character(),
                             "Clust.2" = ChP.Gene.dynamique %>% filter(Gene.Clusters == "Clust.2") %>% pull(Gene) %>% as.character(),
                             "Clust.3" = ChP.Gene.dynamique %>% filter(Gene.Clusters == "Clust.3") %>% pull(Gene) %>% as.character(),
                             "Clust.4" = ChP.Gene.dynamique %>% filter(Gene.Clusters == "Clust.4") %>% pull(Gene) %>% as.character(),
                             "Clust.5" = ChP.Gene.dynamique %>% filter(Gene.Clusters == "Clust.5") %>% pull(Gene) %>% as.character()),
                organism = "mmusculus", ordered_query = F, 
                multi_query = F, significant = T, exclude_iea = T, 
                measure_underrepresentation = F, evcodes = T, 
                user_threshold = 0.05, correction_method = "fdr", 
                domain_scope = "annotated", custom_bg = NULL, 
                numeric_ns = "", sources = c("GO:MF", "GO:BP"), as_short_link = F)

ChP.gostres <- gost(query = as.character(ChP.Gene.dynamique$Gene),
                organism = "mmusculus", ordered_query = F, 
                multi_query = F, significant = T, exclude_iea = T, 
                measure_underrepresentation = F, evcodes = T, 
                user_threshold = 0.05, correction_method = "fdr", 
                domain_scope = "annotated", custom_bg = NULL, 
                numeric_ns = "", sources = c("GO:MF", "GO:BP"), as_short_link = F)

Save results

write.table(ChP.Gene.dynamique, "ChP.Gene.dynamique.csv", sep = ";")

write.table(apply(ChP.gostres$result,2,as.character),
            "ChP.gostres.csv", sep = ";", quote = F, row.names = F)

Session Info

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

## [1] "24 février, 2022, 16,21"

#Packages used
sessionInfo()

## R version 4.1.2 (2021-11-01)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.3 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
## 
## locale:
##  [1] LC_CTYPE=fr_FR.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=fr_FR.UTF-8        LC_COLLATE=fr_FR.UTF-8    
##  [5] LC_MONETARY=fr_FR.UTF-8    LC_MESSAGES=fr_FR.UTF-8   
##  [7] LC_PAPER=fr_FR.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=fr_FR.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] splines   stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] wesanderson_0.3.6      cowplot_1.1.1          ggExtra_0.9           
##  [4] RColorBrewer_1.1-2     dplyr_1.0.7            orthologsBioMART_0.1.0
##  [7] data.table_1.14.2      biomaRt_2.50.1         gprofiler2_0.2.1      
## [10] monocle_2.22.0         DDRTree_0.1.5          irlba_2.3.3           
## [13] VGAM_1.1-5             ggplot2_3.3.5          Biobase_2.54.0        
## [16] BiocGenerics_0.40.0    Matrix_1.4-0           princurve_2.1.6       
## [19] Revelio_0.1.0          SeuratObject_4.0.4     Seurat_4.0.5          
## 
## loaded via a namespace (and not attached):
##   [1] utf8_1.2.2             reticulate_1.22        tidyselect_1.1.1      
##   [4] RSQLite_2.2.8          AnnotationDbi_1.56.2   htmlwidgets_1.5.4     
##   [7] TSP_1.1-11             grid_4.1.2             combinat_0.0-8        
##  [10] docopt_0.7.1           Rtsne_0.15             munsell_0.5.0         
##  [13] codetools_0.2-18       ica_1.0-2              future_1.23.0         
##  [16] miniUI_0.1.1.1         withr_2.4.3            colorspace_2.0-2      
##  [19] fastICA_1.2-3          filelock_1.0.2         highr_0.9             
##  [22] knitr_1.36             ROCR_1.0-11            tensor_1.5            
##  [25] listenv_0.8.0          labeling_0.4.2         slam_0.1-49           
##  [28] GenomeInfoDbData_1.2.7 polyclip_1.10-0        bit64_4.0.5           
##  [31] farver_2.1.0           pheatmap_1.0.12        parallelly_1.29.0     
##  [34] vctrs_0.3.8            generics_0.1.1         xfun_0.28             
##  [37] BiocFileCache_2.2.0    R6_2.5.1               GenomeInfoDb_1.30.0   
##  [40] seriation_1.3.1        bitops_1.0-7           spatstat.utils_2.2-0  
##  [43] cachem_1.0.6           assertthat_0.2.1       promises_1.2.0.1      
##  [46] scales_1.1.1           gtable_0.3.0           globals_0.14.0        
##  [49] goftest_1.2-3          rlang_0.4.12           lazyeval_0.2.2        
##  [52] spatstat.geom_2.3-0    yaml_2.2.1             reshape2_1.4.4        
##  [55] abind_1.4-5            httpuv_1.6.3           tools_4.1.2           
##  [58] ellipsis_0.3.2         spatstat.core_2.3-1    jquerylib_0.1.4       
##  [61] ggridges_0.5.3         Rcpp_1.0.8             plyr_1.8.6            
##  [64] progress_1.2.2         zlibbioc_1.40.0        purrr_0.3.4           
##  [67] RCurl_1.98-1.5         densityClust_0.3       prettyunits_1.1.1     
##  [70] rpart_4.1.16           deldir_1.0-6           pbapply_1.5-0         
##  [73] viridis_0.6.2          S4Vectors_0.32.3       zoo_1.8-9             
##  [76] ggrepel_0.9.1          cluster_2.1.2          magrittr_2.0.2        
##  [79] scattermore_0.7        lmtest_0.9-39          RANN_2.6.1            
##  [82] fitdistrplus_1.1-6     matrixStats_0.61.0     hms_1.1.1             
##  [85] patchwork_1.1.1        mime_0.12              evaluate_0.14         
##  [88] xtable_1.8-4           XML_3.99-0.8           sparsesvd_0.2         
##  [91] IRanges_2.28.0         gridExtra_2.3          HSMMSingleCell_1.14.0 
##  [94] compiler_4.1.2         tibble_3.1.6           KernSmooth_2.23-20    
##  [97] crayon_1.4.2           htmltools_0.5.2        mgcv_1.8-38           
## [100] later_1.3.0            tidyr_1.1.4            DBI_1.1.1             
## [103] dbplyr_2.1.1           MASS_7.3-55            rappdirs_0.3.3        
## [106] parallel_4.1.2         igraph_1.2.11          pkgconfig_2.0.3       
## [109] registry_0.5-1         plotly_4.10.0          spatstat.sparse_2.0-0 
## [112] foreach_1.5.1          xml2_1.3.3             bslib_0.3.1           
## [115] XVector_0.34.0         stringr_1.4.0          digest_0.6.29         
## [118] sctransform_0.3.2      RcppAnnoy_0.0.19       spatstat.data_2.1-0   
## [121] Biostrings_2.62.0      rmarkdown_2.11         leiden_0.3.9          
## [124] uwot_0.1.10            curl_4.3.2             shiny_1.7.1           
## [127] lifecycle_1.0.1        nlme_3.1-153           jsonlite_1.7.2        
## [130] viridisLite_0.4.0      limma_3.50.0           fansi_0.5.0           
## [133] pillar_1.6.4           lattice_0.20-45        KEGGREST_1.34.0       
## [136] fastmap_1.1.0          httr_1.4.2             survival_3.2-13       
## [139] glue_1.5.1             qlcMatrix_0.9.7        FNN_1.1.3             
## [142] iterators_1.0.13       png_0.1-7              bit_4.0.4             
## [145] stringi_1.7.6          sass_0.4.0             blob_1.2.2            
## [148] memoise_2.0.1          future.apply_1.8.1

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