Last updated: 2022-12-06

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Knit directory: synovialscrnaseq/

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html b5b139f Reto Gerber 2022-03-29 Update analysis
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html 7d99571 Reto Gerber 2022-03-21 update analysis
Rmd 9133ed1 Reto Gerber 2022-03-04 update to v6
html 9133ed1 Reto Gerber 2022-03-04 update to v6
html f2e34e1 Reto Gerber 2021-07-29 Update navbar
Rmd ee1face Reto Gerber 2021-07-29 Add missing scripts
html 222b0d1 Reto Gerber 2021-07-29 Update analysis to v5

Set up

suppressPackageStartupMessages({
  library(dplyr)
  library(ggplot2)
  library(purrr)
  library(stringr)
  library(SummarizedExperiment)
  library(SingleCellExperiment)
  library(scater)
  library(scran)
  library(igraph)
  library(scuttle)
  library(tidySingleCellExperiment)
  library(bluster)
  library(BiocParallel)
})
n_workers <- 10
RhpcBLASctl::blas_set_num_threads(n_workers)
bpparam <- MulticoreParam(workers = n_workers, RNGseed = 123)

here::here()
[1] "/home/retger/Synovial/synovialscrnaseq"
remove_low_quality_samples <- TRUE
analysis_version <- 7

set.seed(100)

clusters_lookup <- list()

Indiviual analysis

Repeat analysis for the four main cell types of interest.

Subclustering - SF

celltype_name_pre <- "sf"

load

tmpfilename <- paste0("syn_v",analysis_version,"_sce_",celltype_name_pre,dplyr::if_else(remove_low_quality_samples, "_invivo",""),".rds")
sce_sub <- readRDS(file = here::here("output",tmpfilename))

clustering

graph_clustering <- function(x, k) {
  bpstart(bpparam)
  g <- buildSNNGraph(sce_sub, use.dimred="corrected", k=k, BPPARAM = bpparam)
  bpstop(bpparam)
  igraph::cluster_louvain(g)$membership
}
ratios_ls <- list()
sub_clusters_lookup <- list()
knns <- c(5,10,20,30)
for(k in knns){
  set.seed(123)
  clusters <- graph_clustering(sce_sub, k)
  colData(sce_sub)[[paste0(celltype_name_pre,"_clusters_k",k)]] <- clusters
  sub_clusters_lookup[[paste0(celltype_name_pre,"_clusters_k",k)]] <- 
    data.frame(cell_id = colnames(sce_sub), 
               cluster = paste0(toupper(celltype_name_pre),"_", as.character(clusters)))
  
  # ratios_ls[[as.character(k)]] <- bootstrapStability(reducedDim(sce_sub, "corrected"), 
  #                              FUN=graph_clustering, k=k, 
  #                              clusters = clusters,
  #                              iterations = 20)
}
clusters_lookup[[celltype_name_pre]] <- sub_clusters_lookup
cat("### UMAP cluster {.tabset}\n\n")

UMAP cluster

for(k in knns){
  cat("#### k=",k,"\n\n")
  print(plotReducedDim(sce_sub,"UMAP_corrected", 
               colour_by=paste0(celltype_name_pre,"_clusters_k",k), 
               text_by=paste0(celltype_name_pre,"_clusters_k",k)))
  cat("\n\n")
}

k= 5

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k= 10

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k= 20

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k= 30

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cat("### {-}")

cluster purity

save

tmpfilename <- paste0("syn_v",analysis_version,"_sce_",celltype_name_pre,dplyr::if_else(remove_low_quality_samples, "_invivo",""),".rds")
saveRDS(sce_sub, file = here::here("output",tmpfilename))

Subclustering - EC

celltype_name_pre <- "ec"

load

tmpfilename <- paste0("syn_v",analysis_version,"_sce_",celltype_name_pre,dplyr::if_else(remove_low_quality_samples, "_invivo",""),".rds")
sce_sub <- readRDS(file = here::here("output",tmpfilename))

clustering

graph_clustering <- function(x, k) {
  bpstart(bpparam)
  g <- buildSNNGraph(sce_sub, use.dimred="corrected", k=k, BPPARAM = bpparam)
  bpstop(bpparam)
  igraph::cluster_louvain(g)$membership
}
ratios_ls <- list()
sub_clusters_lookup <- list()
knns <- c(5,10,20,30)
for(k in knns){
  set.seed(123)
  clusters <- graph_clustering(sce_sub, k)
  colData(sce_sub)[[paste0(celltype_name_pre,"_clusters_k",k)]] <- clusters
  sub_clusters_lookup[[paste0(celltype_name_pre,"_clusters_k",k)]] <- 
    data.frame(cell_id = colnames(sce_sub), 
               cluster = paste0(toupper(celltype_name_pre),"_", as.character(clusters)))
  
  # ratios_ls[[as.character(k)]] <- bootstrapStability(reducedDim(sce_sub, "corrected"), 
  #                              FUN=graph_clustering, k=k, 
  #                              clusters = clusters,
  #                              iterations = 20)
}
clusters_lookup[[celltype_name_pre]] <- sub_clusters_lookup
cat("### UMAP cluster {.tabset}\n\n")

UMAP cluster

for(k in knns){
  cat("#### k=",k,"\n\n")
  print(plotReducedDim(sce_sub,"UMAP_corrected", 
               colour_by=paste0(celltype_name_pre,"_clusters_k",k), 
               text_by=paste0(celltype_name_pre,"_clusters_k",k)))
  cat("\n\n")
}

k= 5

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k= 10

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k= 20

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k= 30

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cat("### {-}")

cluster purity

save

tmpfilename <- paste0("syn_v",analysis_version,"_sce_",celltype_name_pre,dplyr::if_else(remove_low_quality_samples, "_invivo",""),".rds")
saveRDS(sce_sub, file = here::here("output",tmpfilename))

Subclustering - Macrophages

celltype_name_pre <- "mp"

load

tmpfilename <- paste0("syn_v",analysis_version,"_sce_",celltype_name_pre,dplyr::if_else(remove_low_quality_samples, "_invivo",""),".rds")
sce_sub <- readRDS(file = here::here("output",tmpfilename))

clustering

graph_clustering <- function(x, k) {
  bpstart(bpparam)
  g <- buildSNNGraph(sce_sub, use.dimred="corrected", k=k, BPPARAM = bpparam)
  bpstop(bpparam)
  igraph::cluster_louvain(g)$membership
}
ratios_ls <- list()
sub_clusters_lookup <- list()
knns <- c(5,10,20,30)
for(k in knns){
  set.seed(123)
  clusters <- graph_clustering(sce_sub, k)
  colData(sce_sub)[[paste0(celltype_name_pre,"_clusters_k",k)]] <- clusters
  sub_clusters_lookup[[paste0(celltype_name_pre,"_clusters_k",k)]] <- 
    data.frame(cell_id = colnames(sce_sub), 
               cluster = paste0(toupper(celltype_name_pre),"_", as.character(clusters)))
  
  # ratios_ls[[as.character(k)]] <- bootstrapStability(reducedDim(sce_sub, "corrected"), 
  #                              FUN=graph_clustering, k=k, 
  #                              clusters = clusters,
  #                              iterations = 20)
}
clusters_lookup[[celltype_name_pre]] <- sub_clusters_lookup
cat("### UMAP cluster {.tabset}\n\n")

UMAP cluster

for(k in knns){
  cat("#### k=",k,"\n\n")
  print(plotReducedDim(sce_sub,"UMAP_corrected", 
               colour_by=paste0(celltype_name_pre,"_clusters_k",k), 
               text_by=paste0(celltype_name_pre,"_clusters_k",k)))
  cat("\n\n")
}

k= 5

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222b0d1 Reto Gerber 2021-07-29

k= 10

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k= 20

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k= 30

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cat("### {-}")

cluster purity

save

tmpfilename <- paste0("syn_v",analysis_version,"_sce_",celltype_name_pre,dplyr::if_else(remove_low_quality_samples, "_invivo",""),".rds")
saveRDS(sce_sub, file = here::here("output",tmpfilename))

Subclustering - T-cells

celltype_name_pre <- "tc"

load

tmpfilename <- paste0("syn_v",analysis_version,"_sce_",celltype_name_pre,dplyr::if_else(remove_low_quality_samples, "_invivo",""),".rds")
sce_sub <- readRDS(file = here::here("output",tmpfilename))

clustering

graph_clustering <- function(x, k) {
  bpstart(bpparam)
  g <- buildSNNGraph(sce_sub, use.dimred="corrected", k=k, BPPARAM = bpparam)
  bpstop(bpparam)
  igraph::cluster_louvain(g)$membership
}
ratios_ls <- list()
sub_clusters_lookup <- list()
knns <- c(5,10,20,30)
for(k in knns){
  set.seed(123)
  clusters <- graph_clustering(sce_sub, k)
  colData(sce_sub)[[paste0(celltype_name_pre,"_clusters_k",k)]] <- clusters
  sub_clusters_lookup[[paste0(celltype_name_pre,"_clusters_k",k)]] <- 
    data.frame(cell_id = colnames(sce_sub), 
               cluster = paste0(toupper(celltype_name_pre),"_", as.character(clusters)))
  
  # ratios_ls[[as.character(k)]] <- bootstrapStability(reducedDim(sce_sub, "corrected"), 
  #                              FUN=graph_clustering, k=k, 
  #                              clusters = clusters,
  #                              iterations = 20)
}
clusters_lookup[[celltype_name_pre]] <- sub_clusters_lookup
cat("### UMAP cluster {.tabset}\n\n")

UMAP cluster

for(k in knns){
  cat("#### k=",k,"\n\n")
  print(plotReducedDim(sce_sub,"UMAP_corrected", 
               colour_by=paste0(celltype_name_pre,"_clusters_k",k), 
               text_by=paste0(celltype_name_pre,"_clusters_k",k)))
  cat("\n\n")
}

k= 5

Version Author Date
9133ed1 Reto Gerber 2022-03-04
222b0d1 Reto Gerber 2021-07-29

k= 10

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9133ed1 Reto Gerber 2022-03-04
222b0d1 Reto Gerber 2021-07-29

k= 20

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9133ed1 Reto Gerber 2022-03-04
222b0d1 Reto Gerber 2021-07-29

k= 30

Version Author Date
9133ed1 Reto Gerber 2022-03-04
222b0d1 Reto Gerber 2021-07-29 
cat("### {-}")

cluster purity

save

tmpfilename <- paste0("syn_v",analysis_version,"_sce_",celltype_name_pre,dplyr::if_else(remove_low_quality_samples, "_invivo",""),".rds")
saveRDS(sce_sub, file = here::here("output",tmpfilename))

Combine clustering results

tmpfilename <- paste0("syn_v",analysis_version,"_clustering_lookup_multiple",dplyr::if_else(remove_low_quality_samples, "_invivo",""),".rds")
saveRDS(clusters_lookup, here::here("output",tmpfilename))

tmpfilename <- paste0("syn_v",analysis_version,"_sce_hvg_cms_doublet_subcluster",dplyr::if_else(remove_low_quality_samples, "_invivo",""),".rds")
syn_sce_tidy_hvg_cms <- readRDS(file = here::here("output",tmpfilename))


for(k in knns){
  colData(syn_sce_tidy_hvg_cms)[[paste0("combined_clusters_k",k)]] <- ""
  for(sub_name in names(clusters_lookup)){
    celllabelmatch <- match(clusters_lookup[[sub_name]][[paste0(sub_name,"_clusters_k",k)]]$cell_id,
                          colnames(syn_sce_tidy_hvg_cms))
    celllabelmatch <- celllabelmatch[!is.na(celllabelmatch)]
    colData(syn_sce_tidy_hvg_cms)[[paste0("combined_clusters_k",k)]][celllabelmatch] <-  clusters_lookup[[sub_name]][[paste0(sub_name,"_clusters_k",k)]]$cluster
  }
  colData(syn_sce_tidy_hvg_cms)[[paste0("combined_clusters_k",k)]][colData(syn_sce_tidy_hvg_cms)[[paste0("combined_clusters_k",k)]] == ""] <-
  colData(syn_sce_tidy_hvg_cms)[[paste0("combined_clusters_k",k)]][colData(syn_sce_tidy_hvg_cms)[[paste0("combined_clusters_k",k)]] == ""]
}
tmpfilename <- paste0("syn_v",analysis_version,"_sce_hvg_cms_doublet_subcluster",dplyr::if_else(remove_low_quality_samples, "_invivo",""),".rds")
saveRDS(syn_sce_tidy_hvg_cms, file = here::here("output",tmpfilename))

4th Part: Annotation


sessionInfo()
R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04 LTS

Matrix products: default
BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.8.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=C             
 [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] parallel  stats4    stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] gdtools_0.2.3                  BiocParallel_1.24.1           
 [3] bluster_1.0.0                  tidySingleCellExperiment_1.0.0
 [5] scuttle_1.0.4                  igraph_1.2.6                  
 [7] scran_1.18.7                   scater_1.18.6                 
 [9] SingleCellExperiment_1.12.0    SummarizedExperiment_1.20.0   
[11] Biobase_2.50.0                 GenomicRanges_1.42.0          
[13] GenomeInfoDb_1.26.7            IRanges_2.24.1                
[15] S4Vectors_0.28.1               BiocGenerics_0.36.1           
[17] MatrixGenerics_1.2.1           matrixStats_0.58.0            
[19] stringr_1.4.0                  purrr_0.3.4                   
[21] ggplot2_3.3.3                  dplyr_1.0.4                   
[23] workflowr_1.6.2               

loaded via a namespace (and not attached):
 [1] bitops_1.0-6              fs_1.5.0                 
 [3] httr_1.4.2                rprojroot_2.0.2          
 [5] tools_4.0.3               R6_2.5.0                 
 [7] irlba_2.3.3               vipor_0.4.5              
 [9] lazyeval_0.2.2            DBI_1.1.1                
[11] colorspace_2.0-0          withr_2.4.1              
[13] tidyselect_1.1.0          gridExtra_2.3            
[15] compiler_4.0.3            git2r_0.28.0             
[17] cli_2.3.0                 BiocNeighbors_1.8.2      
[19] DelayedArray_0.16.3       plotly_4.9.3             
[21] labeling_0.4.2            scales_1.1.1             
[23] systemfonts_1.0.1         digest_0.6.27            
[25] svglite_1.2.3.2           rmarkdown_2.6            
[27] RhpcBLASctl_0.20-137      XVector_0.30.0           
[29] pkgconfig_2.0.3           htmltools_0.5.1.1        
[31] sparseMatrixStats_1.2.1   highr_0.8                
[33] limma_3.46.0              htmlwidgets_1.5.3        
[35] rlang_0.4.10              DelayedMatrixStats_1.12.3
[37] farver_2.0.3              generics_0.1.0           
[39] jsonlite_1.7.2            RCurl_1.98-1.2           
[41] magrittr_2.0.1            BiocSingular_1.6.0       
[43] GenomeInfoDbData_1.2.4    Matrix_1.3-2             
[45] Rcpp_1.0.6                ggbeeswarm_0.6.0         
[47] munsell_0.5.0             fansi_0.4.2              
[49] viridis_0.5.1             lifecycle_1.0.0          
[51] stringi_1.5.3             whisker_0.4              
[53] yaml_2.2.1                edgeR_3.32.1             
[55] zlibbioc_1.36.0           grid_4.0.3               
[57] promises_1.2.0.1          dqrng_0.2.1              
[59] crayon_1.4.1              lattice_0.20-41          
[61] cowplot_1.1.1             beachmat_2.6.4           
[63] locfit_1.5-9.4            knitr_1.31               
[65] pillar_1.4.7              glue_1.4.2               
[67] evaluate_0.14             data.table_1.13.6        
[69] vctrs_0.3.6               httpuv_1.5.5             
[71] tidyr_1.1.2               gtable_0.3.0             
[73] assertthat_0.2.1          xfun_0.21                
[75] rsvd_1.0.3                later_1.1.0.1            
[77] viridisLite_0.3.0         tibble_3.0.6             
[79] beeswarm_0.2.3            statmod_1.4.35           
[81] ellipsis_0.3.1            here_1.0.1