Last updated: 2024-02-26

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suppressPackageStartupMessages({
    library(here)
    library(BiocStyle)
    library(ggplot2)
    library(cowplot)
    library(patchwork)
    library(tidyverse)
    library(SingleCellExperiment)
    library(DropletUtils)
    library(scater)
})

Overview

Bronchoalveolar lavage (BAL) samples were collected from 4 individuals: 1 control sample and 3 cystic fibrosis (CF) samples. The samples were run on teh 10X Chromium and sequenced at the Garvan-Weizmann Centre for Cellular Genomics (GWCCG). The multiplexed samples were sequenced on an Illumina NovaSeq 6000 (NovaSeq Control Software v1.3.1 / Real Time Analysis v3.3.3) ) using a NovaSeq S1 200 cycle kit (Illumina, 20012864). The cellranger count pipeline (version 6.0.2) was used for alignment, filtering, barcode counting, and UMI counting from FASTQ files. The GRCh38 reference was used for the alignment. The number of cells from the pipeline was forced to 10,000. View the CellRanger capture-specific web summaries: A, B, C, D.

sample_metadata_df <- read_csv(
  here("data/C133_Neeland_batch0/data/sample_sheets/Sample_information.csv"))

knitr::kable(sample_metadata_df)

Participant	Sample	Sex	Age	Disease
A1_Ctrl	C	M	3.00	Ctrl
B1_CF	A	M	2.99	CF
C1_CF	B	M	2.99	CF
D1_CF	D	M	3.03	CF

Set up the data

sce <- readRDS(here("data", "C133_Neeland_batch0",
                    "data", "SCEs", "C133_Neeland_batch0.CellRanger.SCE.rds"))
sce$Capture <- factor(sce$Sample)
capture_names <- levels(sce$Capture)
capture_names <- setNames(capture_names, capture_names)
sce$Sample <- NULL
sce

class: SingleCellExperiment 
dim: 33538 8853584 
metadata(1): Samples
assays(1): counts
rownames(33538): ENSG00000243485 ENSG00000237613 ... ENSG00000277475
  ENSG00000268674
rowData names(3): ID Symbol Type
colnames(8853584): 1_AAACCCAAGAAACACT-1 1_AAACCCAAGAAACCCG-1 ...
  4_TTTGTTGTCTTTGGAG-1 4_TTTGTTGTCTTTGGCT-1
colData names(2): Barcode Capture
reducedDimNames(0):
mainExpName: Gene Expression
altExpNames(0):

Call cells from empty droplets

par(mfrow = c(2, 2))
lapply(capture_names, function(cn) {
  sce <- sce[, sce$Capture == cn]
  bcrank <- barcodeRanks(counts(sce))
  # Only showing unique points for plotting speed.
  uniq <- !duplicated(bcrank$rank)
  plot(
    x = bcrank$rank[uniq],
    y = bcrank$total[uniq],
    log = "xy",
    xlab = "Rank",
    ylab = "Total UMI count",
    main = cn,
    cex.lab = 1.2,
    xlim = c(1, 500000),
    ylim = c(1, 200000))
  abline(h = metadata(bcrank)$inflection, col = "darkgreen", lty = 2)
  abline(h = metadata(bcrank)$knee, col = "dodgerblue", lty = 2)
})

Total UMI count for each barcode in the dataset, plotted against its rank (in decreasing order of total counts). The inferred locations of the inflection (dark green dashed lines) and knee points (blue dashed lines) are also shown.

Version	Author	Date
da8df16	Jovana Maksimovic	2024-02-26

Remove empty droplets.

empties <- do.call(rbind, lapply(capture_names, function(cn) {
  message(cn)
  empties <- readRDS(
    here("data",
         "C133_Neeland_batch0",
         "data",
         "emptyDrops", paste0(cn, ".emptyDrops.rds")))
  empties$Capture <- cn
  empties
}))
tapply(
  empties$FDR,
  empties$Capture,
  function(x) sum(x <= 0.001, na.rm = TRUE)) |>
  knitr::kable(
    caption = "Number of non-empty droplets identified using `emptyDrops()` from **DropletUtils**.")

Number of non-empty droplets identified using `emptyDrops()` from **DropletUtils**.
	x
A	4980
B	6093
C	11197
D	12313

sce <- sce[, which(empties$FDR <= 0.001)]
sce

class: SingleCellExperiment 
dim: 33538 34583 
metadata(1): Samples
assays(1): counts
rownames(33538): ENSG00000243485 ENSG00000237613 ... ENSG00000277475
  ENSG00000268674
rowData names(3): ID Symbol Type
colnames(34583): 1_AAACCCAAGCTAGTTC-1 1_AAACCCACAAGATTGA-1 ...
  4_TTTGTTGTCTAGTACG-1 4_TTTGTTGTCTCGAACA-1
colData names(2): Barcode Capture
reducedDimNames(0):
mainExpName: Gene Expression
altExpNames(0):

Add per cell quality control information

sce <- scuttle::addPerCellQC(sce)
head(colData(sce)) %>%
  data.frame %>%
  knitr::kable()

	Barcode	Capture	sum	detected	total
1_AAACCCAAGCTAGTTC-1	AAACCCAAGCTAGTTC-1	A	8126	2657	8126
1_AAACCCACAAGATTGA-1	AAACCCACAAGATTGA-1	A	193	157	193
1_AAACCCACAGTCGCTG-1	AAACCCACAGTCGCTG-1	A	22141	4536	22141
1_AAACCCAGTACCTAAC-1	AAACCCAGTACCTAAC-1	A	17230	4084	17230
1_AAACCCATCGATTGGT-1	AAACCCATCGATTGGT-1	A	32312	5092	32312
1_AAACCCATCGTCTCAC-1	AAACCCATCGTCTCAC-1	A	28301	5044	28301

Save data

saveRDS(
   sce,
   here("data",
        "C133_Neeland_batch0",
        "data",
        "SCEs",
        "C133_Neeland_batch0.preprocessed.SCE.rds"))

Session info

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] scater_1.30.1               scuttle_1.12.0             
 [3] DropletUtils_1.22.0         SingleCellExperiment_1.24.0
 [5] SummarizedExperiment_1.32.0 Biobase_2.62.0             
 [7] GenomicRanges_1.54.1        GenomeInfoDb_1.38.6        
 [9] IRanges_2.36.0              S4Vectors_0.40.2           
[11] BiocGenerics_0.48.1         MatrixGenerics_1.14.0      
[13] matrixStats_1.2.0           lubridate_1.9.3            
[15] forcats_1.0.0               stringr_1.5.1              
[17] dplyr_1.1.4                 purrr_1.0.2                
[19] readr_2.1.5                 tidyr_1.3.1                
[21] tibble_3.2.1                tidyverse_2.0.0            
[23] patchwork_1.2.0             cowplot_1.1.3              
[25] ggplot2_3.4.4               BiocStyle_2.30.0           
[27] here_1.0.1                  workflowr_1.7.1            

loaded via a namespace (and not attached):
  [1] bitops_1.0-7              gridExtra_2.3            
  [3] rlang_1.1.3               magrittr_2.0.3           
  [5] git2r_0.33.0              compiler_4.3.2           
  [7] getPass_0.2-4             DelayedMatrixStats_1.24.0
  [9] callr_3.7.3               vctrs_0.6.5              
 [11] pkgconfig_2.0.3           crayon_1.5.2             
 [13] fastmap_1.1.1             XVector_0.42.0           
 [15] utf8_1.2.4                promises_1.2.1           
 [17] rmarkdown_2.25            tzdb_0.4.0               
 [19] ggbeeswarm_0.7.2          ps_1.7.6                 
 [21] bit_4.0.5                 xfun_0.42                
 [23] zlibbioc_1.48.0           cachem_1.0.8             
 [25] beachmat_2.18.1           jsonlite_1.8.8           
 [27] highr_0.10                later_1.3.2              
 [29] rhdf5filters_1.14.1       DelayedArray_0.28.0      
 [31] Rhdf5lib_1.24.2           BiocParallel_1.36.0      
 [33] irlba_2.3.5.1             parallel_4.3.2           
 [35] R6_2.5.1                  bslib_0.6.1              
 [37] stringi_1.8.3             limma_3.58.1             
 [39] jquerylib_0.1.4           Rcpp_1.0.12              
 [41] knitr_1.45                R.utils_2.12.3           
 [43] httpuv_1.6.14             Matrix_1.6-5             
 [45] timechange_0.3.0          tidyselect_1.2.0         
 [47] viridis_0.6.5             rstudioapi_0.15.0        
 [49] abind_1.4-5               yaml_2.3.8               
 [51] codetools_0.2-19          processx_3.8.3           
 [53] lattice_0.22-5            withr_3.0.0              
 [55] evaluate_0.23             pillar_1.9.0             
 [57] BiocManager_1.30.22       whisker_0.4.1            
 [59] renv_1.0.3                generics_0.1.3           
 [61] vroom_1.6.5               rprojroot_2.0.4          
 [63] RCurl_1.98-1.14           hms_1.1.3                
 [65] sparseMatrixStats_1.14.0  munsell_0.5.0            
 [67] scales_1.3.0              glue_1.7.0               
 [69] tools_4.3.2               BiocNeighbors_1.20.2     
 [71] ScaledMatrix_1.10.0       locfit_1.5-9.8           
 [73] fs_1.6.3                  rhdf5_2.46.1             
 [75] grid_4.3.2                edgeR_4.0.15             
 [77] colorspace_2.1-0          GenomeInfoDbData_1.2.11  
 [79] beeswarm_0.4.0            BiocSingular_1.18.0      
 [81] HDF5Array_1.30.0          vipor_0.4.7              
 [83] rsvd_1.0.5                cli_3.6.2                
 [85] fansi_1.0.6               viridisLite_0.4.2        
 [87] S4Arrays_1.2.0            gtable_0.3.4             
 [89] R.methodsS3_1.8.2         sass_0.4.8               
 [91] digest_0.6.34             ggrepel_0.9.5            
 [93] SparseArray_1.2.4         dqrng_0.3.2              
 [95] htmltools_0.5.7           R.oo_1.26.0              
 [97] lifecycle_1.0.4           httr_1.4.7               
 [99] statmod_1.5.0             bit64_4.0.5

Preprocessing the C133_Neeland_batch0 data set

Jovana Maksimovic