SPRING P0 Gmnc KO

Generation of SPRING coordinates for an object previously generated containing WT and Gmnc KO cells from P0.

Load libraries

library(Seurat)
library(cowplot)
library(dplyr)
library(ggplot2)
library(ggExtra)
library(ggrepel)
library(reticulate)
library(Matrix)
library(viridis)
library(RColorBrewer)
library(MetBrewer)
library(wesanderson)
library(R.utils)

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

Load the dataset and calculate QC metrics

Gmnc.combined <- readRDS("Gmnc.combined.RDS")

DimPlot(Gmnc.combined, reduction = "umap", label = T, label.size = 4, pt.size = 0.1, group.by = "seurat_clusters",  cols = met.brewer("Klimt", 23)) + NoAxes()

Export files for Spring

Export raw expression matrix and gene list for spring plot generation

ExprsMatrix <- as.matrix(GetAssayData(Gmnc.combined))
exprData <- Matrix(ExprsMatrix, sparse = TRUE)
writeMM(exprData, "Spring_files/ExprData.mtx")

## NULL

gzip("Spring_files/ExprData.mtx", overwrite=T)
Genelist <- row.names(ExprsMatrix)
write.table(Genelist, "Spring_files/Genelist.csv", sep="\t", col.names = F, row.names = F)

rm(ExprsMatrix, exprData, Genelist)

Export continuous metadata

S.Score <- c("S.Score",Gmnc.combined$S.Score)
S.Score <- paste(S.Score, sep=",", collapse=",")
G2M.Score <- c("G2M.Score",Gmnc.combined$G2M.Score)
G2M.Score <- paste(G2M.Score, sep=",", collapse=",")
Percent.mt <- c("Percent.mt", Gmnc.combined$percent.mt)
Percent.mt <- paste(Percent.mt, sep = ",", collapse = ",")
Percent.rb <- c("Percent.rb", Gmnc.combined$percent.rb)
Percent.rb <- paste(Percent.rb, sep = ",", collapse = ",")
nCount <- c("nCount", Gmnc.combined$nCount_RNA)
nCount <- paste(nCount, sep = ",", collapse = ",") 
nFeature <- c("nFeature", Gmnc.combined$nFeature_RNA)
nFeature <- paste(nFeature, sep = ",", collapse = ",")

ColorTrack <- rbind(S.Score, G2M.Score, Percent.mt, Percent.rb, nCount, nFeature)
write.table(ColorTrack, "Spring_files/ColorTrack.csv", quote =F, row.names = F, col.names = F)

rm(S.Score, G2M.Score, Percent.mt, Percent.rb, nCount, nFeature, ColorTrack)

Export discrete metadata

Seurat.clusters <- c("Seurat Clusters", paste0("Cluster",as.character(Gmnc.combined$seurat_clusters)))
Seurat.clusters <- paste(Seurat.clusters, sep=",", collapse=",")
Phase <- c("Phase", Gmnc.combined$Phase)
Phase <- paste(Phase, sep=",", collapse=",")
orig.ident <- c("orig.ident", Gmnc.combined$orig.ident) 
orig.ident <- paste(orig.ident, sep = ",", collapse = ",") 
Cellgrouping <- rbind(Seurat.clusters, Phase, orig.ident)
write.table(Cellgrouping, "Spring_files/Cellgrouping.csv", quote =F, row.names = F, col.names = F)

rm(Cellgrouping, Seurat.clusters, Phase, orig.ident)

Export UMAP coordinates

Coordinates <- cbind(c(0:dim(Gmnc.combined)[2]-1),Gmnc.combined@reductions[["umap"]]@cell.embeddings)

# Symmetry transform of the coordinates
Spring.Sym <- function(x){
  x = abs(max(Coordinates[,3])-x)
 }

Coordinates[,3] <- sapply(Coordinates[,3] , function(x) Spring.Sym(x))

# Change x and y axis values for a better display in SPRING
Coordinates <- Coordinates * 100

write.table(Coordinates, "Spring_files/umap.txt", quote =F, row.names = F, col.names = F, sep = ",")
rm(Coordinates)

ExprData.mtx, Genelist.csv, ColorTrack.csv, Cellgrouping.csv and umap.txt are then used as input for the Spring App

A doublet score is calculated using the SPRING app built-in function (based on scrublet) using default parameters (k=50, r=2, f=0.1)

Import doublet score (scrublet)

doublet.score <- read.table("Spring_files/doublet_results.tsv", header = T)
doublet.score <- filter(doublet.score, Observed_or_Simulated == "Observed")
Gmnc.combined$doublet.score <-doublet.score$Score

FeaturePlot(object = Gmnc.combined,
            features = "doublet.score",
            reduction = "umap",
            pt.size = 0.4) +  scale_color_gradientn(colors= c("grey90", brewer.pal(9,"RdPu"))) + NoAxes()

ggplot(doublet.score, aes(x = Score, stat(ndensity))) +
    geom_histogram(bins = 200, colour ="lightgrey")+
    geom_vline(xintercept = 0.25, colour = "red", linetype = 2)

rm(doublet.score)

Cell coordinates of the Spring dimensionality reduction are generated using the following parameters (excluding cells with a doublet score >0.25): Min expressing cells (gene filtering): 3 Min number of UMIs (gene filtering): 3 Gene variability %ile (gene filtering): 90 Number of principal components: 20 Number of nearest neighbors: 8 Number of force layout iterations: 500

Import Spring coordinates

Spring.Sym <- function(x){x = abs(max(Coordinates[,2])-x)} # Symmetry transform of the coordinates

Coordinates <- read.table("Spring_files/coordinates.txt", sep = ",", header = F)[,c(2,3)]
Gmnc.cleanup <- subset(Gmnc.combined, doublet.score < 0.25)
rownames(Coordinates) <- colnames(Gmnc.cleanup)
colnames(Coordinates) <- paste0("Spring_", 1:2)
Coordinates[,2] <- sapply(Coordinates[,2] , function(x) Spring.Sym(x))
Gmnc.cleanup[["Spring"]] <- CreateDimReducObject(embeddings = as.matrix(Coordinates), key = "Spring_")

rm(Coordinates)

# Spring visualization 
DimPlot(Gmnc.cleanup, reduction = "Spring", pt.size = 0.2, label = T, label.size = 3, cols = met.brewer("Klimt", 23)) + NoAxes() + NoLegend()

DimPlot(Gmnc.cleanup, reduction = "Spring", pt.size = 0.2, label = F, group.by = "orig.ident", cols = met.brewer("Egypt", 2)) + NoAxes() 

Save Seurat objects

saveRDS(Gmnc.cleanup, "Gmnc.cleanup.RDS")

Session Info

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

## [1] "09 January, 2024, %:44"

#Packages used
sessionInfo()

## R version 4.1.1 (2021-08-10)
## Platform: x86_64-conda-linux-gnu (64-bit)
## Running under: CentOS Linux 7 (Core)
## 
## Matrix products: default
## BLAS/LAPACK: /shared/ifbstor1/software/miniconda/envs/r-4.1.1/lib/libopenblasp-r0.3.18.so
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] R.utils_2.11.0     R.oo_1.24.0        R.methodsS3_1.8.1  wesanderson_0.3.6 
##  [5] MetBrewer_0.2.0    RColorBrewer_1.1-3 viridis_0.6.2      viridisLite_0.4.1 
##  [9] Matrix_1.6-4       reticulate_1.24    ggrepel_0.9.1      ggExtra_0.9       
## [13] ggplot2_3.3.6      dplyr_1.0.10       cowplot_1.1.1      Seurat_5.0.1      
## [17] SeuratObject_5.0.1 sp_2.1-2          
## 
## loaded via a namespace (and not attached):
##   [1] Rtsne_0.16             colorspace_2.0-3       deldir_1.0-6          
##   [4] ellipsis_0.3.2         ggridges_0.5.3         RcppHNSW_0.3.0        
##   [7] spatstat.data_3.0-3    rstudioapi_0.13        farver_2.1.1          
##  [10] leiden_0.3.10          listenv_0.8.0          RSpectra_0.16-1       
##  [13] fansi_1.0.3            codetools_0.2-18       splines_4.1.1         
##  [16] knitr_1.40             polyclip_1.10-0        spam_2.7-0            
##  [19] jsonlite_1.8.2         ica_1.0-2              cluster_2.1.2         
##  [22] png_0.1-7              uwot_0.1.11            spatstat.sparse_3.0-3 
##  [25] shiny_1.7.1            sctransform_0.4.1      compiler_4.1.1        
##  [28] httr_1.4.4             assertthat_0.2.1       fastmap_1.1.0         
##  [31] lazyeval_0.2.2         cli_3.4.1              later_1.3.0           
##  [34] htmltools_0.5.2        tools_4.1.1            igraph_1.3.1          
##  [37] dotCall64_1.0-1        gtable_0.3.1           glue_1.6.2            
##  [40] RANN_2.6.1             reshape2_1.4.4         Rcpp_1.0.9            
##  [43] scattermore_1.2        jquerylib_0.1.4        vctrs_0.4.2           
##  [46] nlme_3.1-153           spatstat.explore_3.2-5 progressr_0.10.0      
##  [49] lmtest_0.9-40          spatstat.random_3.2-2  xfun_0.34             
##  [52] stringr_1.4.1          globals_0.14.0         mime_0.12             
##  [55] miniUI_0.1.1.1         lifecycle_1.0.3        irlba_2.3.5.1         
##  [58] goftest_1.2-3          future_1.25.0          MASS_7.3-54           
##  [61] zoo_1.8-10             scales_1.2.1           spatstat.utils_3.0-4  
##  [64] promises_1.2.0.1       parallel_4.1.1         yaml_2.3.6            
##  [67] pbapply_1.5-0          gridExtra_2.3          sass_0.4.1            
##  [70] stringi_1.7.8          highr_0.9              fastDummies_1.7.3     
##  [73] rlang_1.0.6            pkgconfig_2.0.3        matrixStats_0.62.0    
##  [76] evaluate_0.17          lattice_0.20-45        tensor_1.5            
##  [79] ROCR_1.0-11            purrr_0.3.5            labeling_0.4.2        
##  [82] patchwork_1.1.1        htmlwidgets_1.5.4      tidyselect_1.2.0      
##  [85] parallelly_1.31.1      RcppAnnoy_0.0.19       plyr_1.8.7            
##  [88] magrittr_2.0.3         R6_2.5.1               generics_0.1.3        
##  [91] DBI_1.1.2              withr_2.5.0            pillar_1.8.1          
##  [94] fitdistrplus_1.1-8     abind_1.4-5            survival_3.2-13       
##  [97] tibble_3.1.8           future.apply_1.9.0     crayon_1.5.2          
## [100] KernSmooth_2.23-20     utf8_1.2.2             spatstat.geom_3.2-7   
## [103] plotly_4.10.0          rmarkdown_2.11         grid_4.1.1            
## [106] data.table_1.14.2      digest_0.6.30          xtable_1.8-4          
## [109] tidyr_1.2.1            httpuv_1.6.5           munsell_0.5.0         
## [112] bslib_0.3.1

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1. IPNP & Imagine Institute, Paris, France, frederic.causeret@inserm.fr↩︎