Identify doublets in C133_Neeland data

Batches 0-6

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Last updated: 2024-02-27

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Knit directory: paed-inflammation-CITEseq/

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File	Version	Author	Date	Message
Rmd	989c163	Jovana Maksimovic	2024-02-27	wflow_publish("analysis/03.0_identify_doublets.Rmd")
html	746b138	Jovana Maksimovic	2024-02-27	Build site.
Rmd	27b8dbf	Jovana Maksimovic	2024-02-27	wflow_publish("analysis/03.0_identify_doublets.Rmd")

---

Load libraries

suppressPackageStartupMessages({
  library(BiocStyle)
  library(tidyverse)
  library(here)
  library(glue)
  library(patchwork)
  library(scran)
  library(scater)
  library(scuttle)
  library(scDblFinder)
  library(scds)
  library(scMerge)
  library(ggupset)
})

Load data

outs <- list.files(here("data",
       paste0("C133_Neeland_batch", 0:6),
       "data", 
       "SCEs"), pattern = "doublets_called",
                   full.names = TRUE)

if(length(outs) < 7){
  files <- list.files(here("data",
                           paste0("C133_Neeland_batch", 0:6),
                           "data", 
                           "SCEs"), 
                      pattern = "quality_filtered",
                      full.names = TRUE)
  
  sceLst <- sapply(files, function(fn){
    readRDS(file = fn)
  })
  
} else {
  sceLst <- sapply(outs, function(fn){
    readRDS(file = fn)
  })
  
}

sceLst

$`/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch0/data/SCEs/C133_Neeland_batch0.quality_filtered.SCE.rds`
class: SingleCellExperiment 
dim: 33538 26939 
metadata(1): Samples
assays(1): counts
rownames(33538): ENSG00000243485 ENSG00000237613 ... ENSG00000277475
  ENSG00000268674
rowData names(20): ID Symbol ... is_mito is_pseudogene
colnames(26939): 1_AAACCCAAGCTAGTTC-1 1_AAACCCACAGTCGCTG-1 ...
  4_TTTGTTGTCTAGTACG-1 4_TTTGTTGTCTCGAACA-1
colData names(14): Barcode Capture ... mito_drop dmmHTO
reducedDimNames(0):
mainExpName: Gene Expression
altExpNames(0):

$`/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch1/data/SCEs/C133_Neeland_batch1.quality_filtered.SCE.rds`
class: SingleCellExperiment 
dim: 36601 21970 
metadata(1): Samples
assays(1): counts
rownames(36601): ENSG00000243485 ENSG00000237613 ... ENSG00000278817
  ENSG00000277196
rowData names(20): ID Symbol ... is_mito is_pseudogene
colnames(21970): 1_AAACCCACACTTCCTG-1 1_AAACCCACAGACAAAT-1 ...
  2_TTTGTTGTCATTGGTG-1 2_TTTGTTGTCGATGGAG-1
colData names(21): Barcode Capture ... total mito_drop
reducedDimNames(0):
mainExpName: Gene Expression
altExpNames(2): HTO ADT

$`/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch2/data/SCEs/C133_Neeland_batch2.quality_filtered.SCE.rds`
class: SingleCellExperiment 
dim: 36601 45711 
metadata(1): Samples
assays(1): counts
rownames(36601): ENSG00000243485 ENSG00000237613 ... ENSG00000278817
  ENSG00000277196
rowData names(20): ID Symbol ... is_mito is_pseudogene
colnames(45711): 1_AAACCCAAGACCTGGA-1 1_AAACCCAAGACTGTTC-1 ...
  2_TTTGTTGTCTCATGGA-1 2_TTTGTTGTCTCCAAGA-1
colData names(21): Barcode Capture ... total mito_drop
reducedDimNames(0):
mainExpName: Gene Expression
altExpNames(2): HTO ADT

$`/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch3/data/SCEs/C133_Neeland_batch3.quality_filtered.SCE.rds`
class: SingleCellExperiment 
dim: 36601 58038 
metadata(1): Samples
assays(1): counts
rownames(36601): ENSG00000243485 ENSG00000237613 ... ENSG00000278817
  ENSG00000277196
rowData names(20): ID Symbol ... is_mito is_pseudogene
colnames(58038): 1_AAACCCAAGCAGCACA-1 1_AAACCCAAGCATCTTG-1 ...
  2_TTTGTTGTCTAGGCCG-1 2_TTTGTTGTCTCGGCTT-1
colData names(21): Barcode Capture ... total mito_drop
reducedDimNames(0):
mainExpName: Gene Expression
altExpNames(2): HTO ADT

$`/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch4/data/SCEs/C133_Neeland_batch4.quality_filtered.SCE.rds`
class: SingleCellExperiment 
dim: 36601 45760 
metadata(1): Samples
assays(1): counts
rownames(36601): ENSG00000243485 ENSG00000237613 ... ENSG00000278817
  ENSG00000277196
rowData names(20): ID Symbol ... is_mito is_pseudogene
colnames(45760): 1_AAACCCAAGCGTTAGG-1 1_AAACCCAAGGATTTGA-1 ...
  2_TTTGTTGTCGACGATT-1 2_TTTGTTGTCTAGGCCG-1
colData names(21): Barcode Capture ... total mito_drop
reducedDimNames(0):
mainExpName: Gene Expression
altExpNames(2): HTO ADT

$`/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch5/data/SCEs/C133_Neeland_batch5.quality_filtered.SCE.rds`
class: SingleCellExperiment 
dim: 36601 44715 
metadata(1): Samples
assays(1): counts
rownames(36601): ENSG00000243485 ENSG00000237613 ... ENSG00000278817
  ENSG00000277196
rowData names(20): ID Symbol ... is_mito is_pseudogene
colnames(44715): 1_AAACCCAAGAAGATCT-1 1_AAACCCAAGATGCAGC-1 ...
  2_TTTGTTGTCGGATTAC-1 2_TTTGTTGTCTGAGAGG-1
colData names(21): Barcode Capture ... total mito_drop
reducedDimNames(0):
mainExpName: Gene Expression
altExpNames(2): HTO ADT

$`/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch6/data/SCEs/C133_Neeland_batch6.quality_filtered.SCE.rds`
class: SingleCellExperiment 
dim: 36601 46485 
metadata(1): Samples
assays(1): counts
rownames(36601): ENSG00000243485 ENSG00000237613 ... ENSG00000278817
  ENSG00000277196
rowData names(20): ID Symbol ... is_mito is_pseudogene
colnames(46485): 1_AAACCCAAGAAGCGCT-1 1_AAACCCAAGACTCATC-1 ...
  2_TTTGTTGTCGAGAATA-1 2_TTTGTTGTCTACTGAG-1
colData names(21): Barcode Capture ... total mito_drop
reducedDimNames(0):
mainExpName: Gene Expression
altExpNames(2): HTO ADT

Call doublets

Use scds and scDblFinder to try to identify within-sample doublets. Doublets are called on each capture separately.

if(length(outs) < length(sceLst)){
  
  sceLst <- sapply(sceLst, function(sce){
    colnames(colData(sce))[grepl("^C|^c", 
                                   colnames(colData(sce)), 
                                   perl = TRUE)] <- "Capture"
    capture_names <- levels(sce$Capture)
    
    ## Annotate doublets for one capture at a time
    capLst <- sapply(capture_names, function(cn){
      keep <- sce$Capture == cn
      
      ## Annotate doublets using scds three step process as run in Demuxafy
      cap <- bcds(sce[, keep], 
                  retRes = TRUE, estNdbl = TRUE)
      cap <- cxds(cap, retRes = TRUE, estNdbl = TRUE)
      cap <- cxds_bcds_hybrid(cap, estNdbl = TRUE)
      ## Annotate doublets using scDblFInder with rate estimate from Demuxafy
      cap <- scDblFinder(cap, dbr = ncol(cap)/1000*0.008)
      cap
      
    })
    
    tmp <- sce_cbind(capLst, 
                     method = "intersect", 
                     exprs = c("counts"), 
                     cut_off_batch = 0,
                     cut_off_overall = 0,
                     colData_names = TRUE)
    
    if(all(rownames(tmp) == rownames(sce))) rowData(tmp) <- rowData(sce)
    
    if(!is_empty(altExpNames(sce))){
      altExp(tmp, "HTO") <- altExp(sce, "HTO")
      altExp(tmp, "ADT") <- altExp(sce, "ADT")
      
    }
    tmp
    
  })
  
}

Number of doublet called made by different methods

p <- lapply(sceLst, function(sce){
colData(sce) %>%
  data.frame %>%
    mutate(scds = ifelse(hybrid_call, "Doublet", "Singlet"),
         scdf = ifelse(scDblFinder.class == "doublet", "Doublet", "Singlet")) %>%
  dplyr::select(GeneticDonor, scds, scdf) %>% 
  rownames_to_column(var = "cell") %>%
  mutate(vireo_dbl = (GeneticDonor == "Doublet"),
         scds_dbl = (scds == "Doublet"),
         scdf_dbl = (scdf == "Doublet")) %>%
  dplyr::select(cell, vireo_dbl, scds_dbl, scdf_dbl) %>% 
  pivot_longer(cols = c(vireo_dbl, scds_dbl, scdf_dbl), names_to = "method") %>%
  dplyr::filter(value == TRUE) %>%
  group_by(cell) %>%
  summarise(data = list(method)) %>%
  rowwise() -> dat

  ggplot(dat, aes(x = data)) +
    geom_bar() +
    scale_x_upset(n_intersections = 20) +
    geom_text(stat = 'count', aes(label = after_stat(count)), 
              vjust = -0.5, size = 2.5)
})

p

$`/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch0/data/SCEs/C133_Neeland_batch0.quality_filtered.SCE.rds`

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Version	Author	Date
746b138	Jovana Maksimovic	2024-02-27

$`/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch1/data/SCEs/C133_Neeland_batch1.quality_filtered.SCE.rds`

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Version	Author	Date
746b138	Jovana Maksimovic	2024-02-27

$`/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch2/data/SCEs/C133_Neeland_batch2.quality_filtered.SCE.rds`

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Version	Author	Date
746b138	Jovana Maksimovic	2024-02-27

$`/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch3/data/SCEs/C133_Neeland_batch3.quality_filtered.SCE.rds`

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Version	Author	Date
746b138	Jovana Maksimovic	2024-02-27

$`/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch4/data/SCEs/C133_Neeland_batch4.quality_filtered.SCE.rds`

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Version	Author	Date
746b138	Jovana Maksimovic	2024-02-27

$`/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch5/data/SCEs/C133_Neeland_batch5.quality_filtered.SCE.rds`

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Version	Author	Date
746b138	Jovana Maksimovic	2024-02-27

$`/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch6/data/SCEs/C133_Neeland_batch6.quality_filtered.SCE.rds`

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Version	Author	Date
746b138	Jovana Maksimovic	2024-02-27

Vireo doublets as percentage

sapply(sceLst, function(sce){
  sum(sce$GeneticDonor == "Doublet")/nrow(sce)*100
})

/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch0/data/SCEs/C133_Neeland_batch0.quality_filtered.SCE.rds 
                                                                                                                                                            0.000000 
/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch1/data/SCEs/C133_Neeland_batch1.quality_filtered.SCE.rds 
                                                                                                                                                            5.202044 
/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch2/data/SCEs/C133_Neeland_batch2.quality_filtered.SCE.rds 
                                                                                                                                                           23.832682 
/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch3/data/SCEs/C133_Neeland_batch3.quality_filtered.SCE.rds 
                                                                                                                                                           29.034726 
/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch4/data/SCEs/C133_Neeland_batch4.quality_filtered.SCE.rds 
                                                                                                                                                           17.182591 
/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch5/data/SCEs/C133_Neeland_batch5.quality_filtered.SCE.rds 
                                                                                                                                                           16.854731 
/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch6/data/SCEs/C133_Neeland_batch6.quality_filtered.SCE.rds 
                                                                                                                                                           17.453075 

Number of droplets per capture

sapply(sceLst, function(sce){
  table(sce$Capture)
})

$`/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch0/data/SCEs/C133_Neeland_batch0.quality_filtered.SCE.rds`

   A    B    C    D 
3620 4370 9129 9820 

$`/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch1/data/SCEs/C133_Neeland_batch1.quality_filtered.SCE.rds`

C133_batch1_1 C133_batch1_2 
        10556         11414 

$`/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch2/data/SCEs/C133_Neeland_batch2.quality_filtered.SCE.rds`

C133_batch2_1 C133_batch2_2 
        21248         24463 

$`/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch3/data/SCEs/C133_Neeland_batch3.quality_filtered.SCE.rds`

C133_batch3_1 C133_batch3_2 
        29156         28882 

$`/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch4/data/SCEs/C133_Neeland_batch4.quality_filtered.SCE.rds`

C133_batch4_1 C133_batch4_2 
        22702         23058 

$`/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch5/data/SCEs/C133_Neeland_batch5.quality_filtered.SCE.rds`

C133_batch5_1 C133_batch5_2 
        22097         22618 

$`/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch6/data/SCEs/C133_Neeland_batch6.quality_filtered.SCE.rds`

C133_batch6_1 C133_batch6_2 
        23468         23017 

Doublet QC metrics

p <- lapply(sceLst, function(sce){
  if(!"sum" %in% colnames(colData(sce))){
    ## add per cell QC metrics if they are missing
    sce <- addPerCellQC(sce)
    
  }
  
  colData(sce) %>%
    data.frame %>%
    mutate(scds = ifelse(hybrid_call, "Doublet", "Singlet"),
           scdf = ifelse(scDblFinder.class == "doublet", "Doublet", "Singlet")) %>%
    rownames_to_column(var = "cell") %>%
    mutate(vireo_dbl = (GeneticDonor == "Doublet"),
           scds_dbl = (scds == "Doublet"),
           scdf_dbl = (scdf == "Doublet")) %>%
    pivot_longer(cols = c(vireo_dbl, scds_dbl, scdf_dbl),
                 names_to = "method") -> dat
  
  p1 <- ggplot(dat, aes(x = method, y = sum, fill = value)) +
    geom_violin(scale = "count") +
    scale_y_log10() +
    labs(fill = "Doublet")
  
  p2 <- ggplot(dat, aes(x = method, y = detected, fill = value)) +
    geom_violin(scale = "count")  +
    labs(fill = "Doublet")
  
  (p1 | p2) + plot_layout(guides = "collect")
})

p

$`/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch0/data/SCEs/C133_Neeland_batch0.quality_filtered.SCE.rds`

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Version	Author	Date
746b138	Jovana Maksimovic	2024-02-27

$`/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch1/data/SCEs/C133_Neeland_batch1.quality_filtered.SCE.rds`

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746b138	Jovana Maksimovic	2024-02-27

$`/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch2/data/SCEs/C133_Neeland_batch2.quality_filtered.SCE.rds`

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Version	Author	Date
746b138	Jovana Maksimovic	2024-02-27

$`/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch3/data/SCEs/C133_Neeland_batch3.quality_filtered.SCE.rds`

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Version	Author	Date
746b138	Jovana Maksimovic	2024-02-27

$`/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch4/data/SCEs/C133_Neeland_batch4.quality_filtered.SCE.rds`

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Version	Author	Date
746b138	Jovana Maksimovic	2024-02-27

$`/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch5/data/SCEs/C133_Neeland_batch5.quality_filtered.SCE.rds`

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Version	Author	Date
746b138	Jovana Maksimovic	2024-02-27

$`/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch6/data/SCEs/C133_Neeland_batch6.quality_filtered.SCE.rds`

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Version	Author	Date
746b138	Jovana Maksimovic	2024-02-27

Save data

batches <- str_extract(names(sceLst), "batch[0-6]")

sapply(1:length(sceLst), function(i){
  out <- here("data",
              paste0("C133_Neeland_", batches[i]),
              "data", 
              "SCEs", 
              glue("C133_Neeland_{batches[i]}.doublets_called.SCE.rds"))
  if(!file.exists(out)) saveRDS(sceLst[[i]], out)
  fs::file_chmod(out, "664")
  if(any(str_detect(fs::group_ids()$group_name, 
                    "oshlack_lab"))) fs::file_chown(out, 
                                                    group_id = "oshlack_lab")
})

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Session info

Session information

sessionInfo()

R version 4.3.2 (2023-10-31)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Sonoma 14.3

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: Australia/Melbourne
tzcode source: internal

attached base packages:
[1] stats4    stats     graphics  grDevices datasets  utils     methods  
[8] base     

other attached packages:
 [1] ggupset_0.3.0               scMerge_1.18.0             
 [3] scds_1.18.0                 scDblFinder_1.16.0         
 [5] scater_1.30.1               scran_1.30.2               
 [7] scuttle_1.12.0              SingleCellExperiment_1.24.0
 [9] SummarizedExperiment_1.32.0 Biobase_2.62.0             
[11] GenomicRanges_1.54.1        GenomeInfoDb_1.38.6        
[13] IRanges_2.36.0              S4Vectors_0.40.2           
[15] BiocGenerics_0.48.1         MatrixGenerics_1.14.0      
[17] matrixStats_1.2.0           patchwork_1.2.0            
[19] glue_1.7.0                  here_1.0.1                 
[21] lubridate_1.9.3             forcats_1.0.0              
[23] stringr_1.5.1               dplyr_1.1.4                
[25] purrr_1.0.2                 readr_2.1.5                
[27] tidyr_1.3.1                 tibble_3.2.1               
[29] ggplot2_3.4.4               tidyverse_2.0.0            
[31] BiocStyle_2.30.0            workflowr_1.7.1            

loaded via a namespace (and not attached):
  [1] batchelor_1.18.1          splines_4.3.2            
  [3] later_1.3.2               BiocIO_1.12.0            
  [5] bitops_1.0-7              pROC_1.18.5              
  [7] XML_3.99-0.16.1           rpart_4.1.23             
  [9] lifecycle_1.0.4           StanHeaders_2.32.5       
 [11] edgeR_4.0.15              rprojroot_2.0.4          
 [13] processx_3.8.3            lattice_0.22-5           
 [15] MASS_7.3-60.0.1           backports_1.4.1          
 [17] magrittr_2.0.3            limma_3.58.1             
 [19] Hmisc_5.1-1               sass_0.4.8               
 [21] rmarkdown_2.25            jquerylib_0.1.4          
 [23] yaml_2.3.8                metapod_1.10.1           
 [25] httpuv_1.6.14             pkgbuild_1.4.3           
 [27] RColorBrewer_1.1-3        ResidualMatrix_1.12.0    
 [29] abind_1.4-5               zlibbioc_1.48.0          
 [31] sfsmisc_1.1-17            RCurl_1.98-1.14          
 [33] nnet_7.3-19               git2r_0.33.0             
 [35] GenomeInfoDbData_1.2.11   inline_0.3.19            
 [37] ggrepel_0.9.5             densEstBayes_1.0-2.2     
 [39] irlba_2.3.5.1             cvTools_0.3.2            
 [41] dqrng_0.3.2               DelayedMatrixStats_1.24.0
 [43] codetools_0.2-19          DelayedArray_0.28.0      
 [45] tidyselect_1.2.0          farver_2.1.1             
 [47] ScaledMatrix_1.10.0       viridis_0.6.5            
 [49] base64enc_0.1-3           GenomicAlignments_1.38.2 
 [51] jsonlite_1.8.8            BiocNeighbors_1.20.2     
 [53] Formula_1.2-5             bbmle_1.0.25.1           
 [55] tools_4.3.2               startupmsg_0.9.6.1       
 [57] Rcpp_1.0.12               gridExtra_2.3            
 [59] SparseArray_1.2.4         mgcv_1.9-1               
 [61] xfun_0.42                 loo_2.6.0                
 [63] withr_3.0.0               numDeriv_2016.8-1.1      
 [65] BiocManager_1.30.22       fastmap_1.1.1            
 [67] bluster_1.12.0            fansi_1.0.6              
 [69] callr_3.7.3               caTools_1.18.2           
 [71] digest_0.6.34             rsvd_1.0.5               
 [73] timechange_0.3.0          R6_2.5.1                 
 [75] colorspace_2.1-0          reldist_1.7-2            
 [77] gtools_3.9.5              utf8_1.2.4               
 [79] generics_0.1.3            renv_1.0.3               
 [81] data.table_1.15.0         robustbase_0.99-2        
 [83] rtracklayer_1.62.0        httr_1.4.7               
 [85] htmlwidgets_1.6.4         S4Arrays_1.2.0           
 [87] whisker_0.4.1             pkgconfig_2.0.3          
 [89] gtable_0.3.4              XVector_0.42.0           
 [91] htmltools_0.5.7           ruv_0.9.7.1              
 [93] scales_1.3.0              knitr_1.45               
 [95] rstudioapi_0.15.0         tzdb_0.4.0               
 [97] rjson_0.2.21              nlme_3.1-164             
 [99] checkmate_2.3.1           bdsmatrix_1.3-6          
[101] M3Drop_1.28.0             cachem_1.0.8             
[103] KernSmooth_2.23-22        parallel_4.3.2           
[105] vipor_0.4.7               foreign_0.8-86           
[107] restfulr_0.0.15           proxyC_0.3.4             
[109] pillar_1.9.0              grid_4.3.2               
[111] vctrs_0.6.5               gplots_3.1.3.1           
[113] promises_1.2.1            BiocSingular_1.18.0      
[115] beachmat_2.18.1           cluster_2.1.6            
[117] beeswarm_0.4.0            htmlTable_2.4.2          
[119] evaluate_0.23             mvtnorm_1.2-4            
[121] cli_3.6.2                 locfit_1.5-9.8           
[123] compiler_4.3.2            Rsamtools_2.18.0         
[125] rlang_1.1.3               crayon_1.5.2             
[127] rstantools_2.4.0          labeling_0.4.3           
[129] ps_1.7.6                  getPass_0.2-4            
[131] plyr_1.8.9                fs_1.6.3                 
[133] ggbeeswarm_0.7.2          rstan_2.32.5             
[135] stringi_1.8.3             QuickJSR_1.1.3           
[137] viridisLite_0.4.2         BiocParallel_1.36.0      
[139] munsell_0.5.0             Biostrings_2.70.2        
[141] Matrix_1.6-5              hms_1.1.3                
[143] sparseMatrixStats_1.14.0  statmod_1.5.0            
[145] highr_0.10                igraph_2.0.1.1           
[147] RcppParallel_5.1.7        bslib_0.6.1              
[149] DEoptimR_1.1-3            xgboost_1.7.7.1          
[151] distr_2.9.3