Paedriatic airway atlas: all Tissues
Overview of batches and per sample filtered counts
Gunjan Dixit
October 18, 2023
Last updated: 2023-10-18
Checks: 6 1
Knit directory: paed-airway-allTissues/
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INTRODUCTION
This RMarkdown reads CellRanger output i.e filtered count matrix for
each sample in a batch and creates a Seurat object for filtered
CellRanger output for each batch. Batch 1- Nasal
brushings (n=16)
Batch 2_1- Tonsils (n=16)
Batch 2_2- Nasal brushings (n=9) (repeats)
Batch 3- Adenoids (n=16)
Batch 4-
Bronchial brushings (n=16)
Batch 5- Nasal
brushings (n=16)
Batch 6- BAL (n=16) #NOTE: Only 8
samples were collected. 8 samples with 2 barcodes were run using the
same 16-plex assay workflow , therefore targeting 16k cells per sample.
Batch 7- Bronchial brushings_2 (n=16)
Batch 8- Adenoids_2 (n=16)
Batch
9- Tonsils_2 (n=16)
Load libraries
suppressPackageStartupMessages({
library(BiocStyle)
library(tidyverse)
library(here)
library(dplyr)
library(glue)
library(Seurat)
library(DropletUtils)
library(kableExtra)
library(RColorBrewer)
library(scater)
library(cowplot)
})List batch files
files <- list.files(here("output",
"RDS",
"AllBatches"),
pattern = "filtered_CellRanger_SEU",
full.names = TRUE)
batches <- sub("_filtered_CellRanger_SEU\\.rds$", "", basename(files))
batches [1] "G000231_Neeland_batch1" "G000231_Neeland_batch2_1"
[3] "G000231_Neeland_batch2_2" "G000231_Neeland_batch3"
[5] "G000231_Neeland_batch4" "G000231_Neeland_batch5"
[7] "G000231_Neeland_batch6" "G000231_Neeland_batch7"
[9] "G000231_Neeland_batch8" "G000231_Neeland_batch9" tissue_types <- c("Nasal_brushings", "Tonsils", "Nasal_brushings_repeat", "Adenoids", "Bronchial_brushings", "Nasal_brushings_2", "BAL", "Bronchial_brushings_2", "Adeoids_2", "Tonsils_2")
tissue_types [1] "Nasal_brushings" "Tonsils" "Nasal_brushings_repeat"
[4] "Adenoids" "Bronchial_brushings" "Nasal_brushings_2"
[7] "BAL" "Bronchial_brushings_2" "Adeoids_2"
[10] "Tonsils_2" make_filtered_seurat <- function(sample_name, batch) {
sub_batch <- paste0(gsub("Neeland_", "", batch), "_1")
seu <- Read10X_h5(here("data", batch, "extdata/CellRanger", sub_batch, "outs", "per_sample_outs", sample_name, "count", "sample_filtered_feature_bc_matrix.h5")) %>%
CreateSeuratObject()
seu$Sample <- sample_name
return(seu)
}for (batch in batches) {
sub_batch <- paste0(gsub("Neeland_", "", batch), "_1")
out <- here("output/RDS/AllBatches", paste0(batch, "_filtered_CellRanger_SEU.rds"))
if(!file.exists(out)){
sample_dir <- here("data", batch, "extdata/CellRanger", sub_batch, "outs/per_sample_outs/")
sample_names <- list.dirs(sample_dir, recursive = FALSE, full.names = FALSE)
seu1 <- lapply(sample_names, function(sn) make_filtered_seurat(sn, batch))
seu1 <- merge(seu1[[1]], unlist(seu1[2:length(seu1)]))
saveRDS(seu1, file = out)
}
} Generate barplot for counts per sample
plot_list <- list()
for (i in seq_along(batches)){
batch <- batches[i]
tissue <- tissue_types[i]
seu <- readRDS(here("output/RDS/AllBatches", paste0(batch, "_filtered_CellRanger_SEU.rds")))
sce <- as.SingleCellExperiment(seu)
num_unique_samples <- length(unique(sce$Sample))
color_palette <- colorRampPalette(brewer.pal(20, "Set3"))(num_unique_samples)
# Extract unique sample names and assign colors from the color palette
names(color_palette) <- sort(unique(sce$Sample))
# Create the plot for the current batch
p <- ggcells(sce) +
geom_bar(aes(x = Sample, fill = Sample)) +
coord_flip() +
ylab("Number of droplets") +
theme_cowplot(font_size = 10) +
geom_text(stat='count', aes(x = Sample, label=..count..), hjust=1.5, size=3) +
guides(fill = "none") +
scale_fill_manual(values = color_palette) +
labs(title = paste0(batch, ": ", tissue))
# Store the plot in the list
plot_list[[batch]] <- p
}for (i in seq_along(batches)){
batch_name <- batches[i]
tissue_type <- tissue_types[i]
cat('### ', batch_name, " - ", tissue_type, '\n')
print(plot_list[[batch_name]])
cat('\n\n')
}G000231_Neeland_batch1 - Nasal_brushings
G000231_Neeland_batch2_1 - Tonsils
G000231_Neeland_batch2_2 - Nasal_brushings_repeat
G000231_Neeland_batch3 - Adenoids
G000231_Neeland_batch4 - Bronchial_brushings
G000231_Neeland_batch5 - Nasal_brushings_2
G000231_Neeland_batch6 - BAL
G000231_Neeland_batch7 - Bronchial_brushings_2
G000231_Neeland_batch8 - Adeoids_2
G000231_Neeland_batch9 - Tonsils_2
Session Info
sessionInfo()R version 4.3.1 (2023-06-16)
Platform: x86_64-apple-darwin20 (64-bit)
Running under: macOS Sonoma 14.0
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
locale:
[1] en_AU.UTF-8/en_AU.UTF-8/en_AU.UTF-8/C/en_AU.UTF-8/en_AU.UTF-8
time zone: Australia/Melbourne
tzcode source: internal
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] cowplot_1.1.1 scater_1.28.0
[3] scuttle_1.10.1 RColorBrewer_1.1-3
[5] kableExtra_1.3.4 DropletUtils_1.20.0
[7] SingleCellExperiment_1.22.0 SummarizedExperiment_1.30.2
[9] Biobase_2.60.0 GenomicRanges_1.52.0
[11] GenomeInfoDb_1.36.1 IRanges_2.34.1
[13] S4Vectors_0.38.1 BiocGenerics_0.46.0
[15] MatrixGenerics_1.12.2 matrixStats_1.0.0
[17] SeuratObject_4.1.3 Seurat_4.3.0.1
[19] glue_1.6.2 here_1.0.1
[21] lubridate_1.9.2 forcats_1.0.0
[23] stringr_1.5.0 dplyr_1.1.2
[25] purrr_1.0.1 readr_2.1.4
[27] tidyr_1.3.0 tibble_3.2.1
[29] ggplot2_3.4.2 tidyverse_2.0.0
[31] BiocStyle_2.28.0 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] RcppAnnoy_0.0.21 splines_4.3.1
[3] later_1.3.1 bitops_1.0-7
[5] R.oo_1.25.0 polyclip_1.10-4
[7] lifecycle_1.0.3 edgeR_3.42.4
[9] rprojroot_2.0.3 globals_0.16.2
[11] processx_3.8.2 lattice_0.21-8
[13] MASS_7.3-60 magrittr_2.0.3
[15] limma_3.56.2 plotly_4.10.2
[17] sass_0.4.7 rmarkdown_2.23
[19] jquerylib_0.1.4 yaml_2.3.7
[21] httpuv_1.6.11 sctransform_0.3.5
[23] sp_2.0-0 spatstat.sparse_3.0-2
[25] reticulate_1.30 pbapply_1.7-2
[27] abind_1.4-5 zlibbioc_1.46.0
[29] rvest_1.0.3 Rtsne_0.16
[31] R.utils_2.12.2 RCurl_1.98-1.12
[33] git2r_0.32.0 GenomeInfoDbData_1.2.10
[35] ggrepel_0.9.3 irlba_2.3.5.1
[37] listenv_0.9.0 spatstat.utils_3.0-3
[39] goftest_1.2-3 dqrng_0.3.0
[41] spatstat.random_3.1-5 fitdistrplus_1.1-11
[43] parallelly_1.36.0 svglite_2.1.1
[45] DelayedMatrixStats_1.22.1 leiden_0.4.3
[47] codetools_0.2-19 DelayedArray_0.26.6
[49] xml2_1.3.5 tidyselect_1.2.0
[51] farver_2.1.1 viridis_0.6.4
[53] ScaledMatrix_1.8.1 spatstat.explore_3.2-1
[55] webshot_0.5.5 jsonlite_1.8.7
[57] BiocNeighbors_1.18.0 ellipsis_0.3.2
[59] progressr_0.13.0 ggridges_0.5.4
[61] survival_3.5-5 systemfonts_1.0.4
[63] tools_4.3.1 ica_1.0-3
[65] Rcpp_1.0.11 gridExtra_2.3
[67] xfun_0.39 HDF5Array_1.28.1
[69] withr_2.5.0 BiocManager_1.30.21.1
[71] fastmap_1.1.1 rhdf5filters_1.12.1
[73] fansi_1.0.4 rsvd_1.0.5
[75] callr_3.7.3 digest_0.6.33
[77] timechange_0.2.0 R6_2.5.1
[79] mime_0.12 colorspace_2.1-0
[81] scattermore_1.2 tensor_1.5
[83] spatstat.data_3.0-1 R.methodsS3_1.8.2
[85] utf8_1.2.3 generics_0.1.3
[87] data.table_1.14.8 httr_1.4.6
[89] htmlwidgets_1.6.2 S4Arrays_1.0.4
[91] whisker_0.4.1 uwot_0.1.16
[93] pkgconfig_2.0.3 gtable_0.3.3
[95] lmtest_0.9-40 XVector_0.40.0
[97] htmltools_0.5.5 scales_1.2.1
[99] png_0.1-8 knitr_1.43
[101] rstudioapi_0.15.0 tzdb_0.4.0
[103] reshape2_1.4.4 nlme_3.1-162
[105] cachem_1.0.8 zoo_1.8-12
[107] rhdf5_2.44.0 KernSmooth_2.23-22
[109] vipor_0.4.5 parallel_4.3.1
[111] miniUI_0.1.1.1 pillar_1.9.0
[113] grid_4.3.1 vctrs_0.6.3
[115] RANN_2.6.1 promises_1.2.0.1
[117] BiocSingular_1.16.0 beachmat_2.16.0
[119] xtable_1.8-4 cluster_2.1.4
[121] beeswarm_0.4.0 evaluate_0.21
[123] locfit_1.5-9.8 cli_3.6.1
[125] compiler_4.3.1 rlang_1.1.1
[127] crayon_1.5.2 future.apply_1.11.0
[129] labeling_0.4.2 ps_1.7.5
[131] ggbeeswarm_0.7.2 getPass_0.2-2
[133] plyr_1.8.8 fs_1.6.3
[135] stringi_1.7.12 viridisLite_0.4.2
[137] deldir_1.0-9 BiocParallel_1.34.2
[139] munsell_0.5.0 lazyeval_0.2.2
[141] spatstat.geom_3.2-4 Matrix_1.6-0
[143] hms_1.1.3 patchwork_1.1.2
[145] sparseMatrixStats_1.12.2 future_1.33.0
[147] Rhdf5lib_1.22.0 shiny_1.7.4.1
[149] highr_0.10 ROCR_1.0-11
[151] igraph_1.5.0 bslib_0.5.0
sessionInfo()R version 4.3.1 (2023-06-16)
Platform: x86_64-apple-darwin20 (64-bit)
Running under: macOS Sonoma 14.0
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
locale:
[1] en_AU.UTF-8/en_AU.UTF-8/en_AU.UTF-8/C/en_AU.UTF-8/en_AU.UTF-8
time zone: Australia/Melbourne
tzcode source: internal
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] cowplot_1.1.1 scater_1.28.0
[3] scuttle_1.10.1 RColorBrewer_1.1-3
[5] kableExtra_1.3.4 DropletUtils_1.20.0
[7] SingleCellExperiment_1.22.0 SummarizedExperiment_1.30.2
[9] Biobase_2.60.0 GenomicRanges_1.52.0
[11] GenomeInfoDb_1.36.1 IRanges_2.34.1
[13] S4Vectors_0.38.1 BiocGenerics_0.46.0
[15] MatrixGenerics_1.12.2 matrixStats_1.0.0
[17] SeuratObject_4.1.3 Seurat_4.3.0.1
[19] glue_1.6.2 here_1.0.1
[21] lubridate_1.9.2 forcats_1.0.0
[23] stringr_1.5.0 dplyr_1.1.2
[25] purrr_1.0.1 readr_2.1.4
[27] tidyr_1.3.0 tibble_3.2.1
[29] ggplot2_3.4.2 tidyverse_2.0.0
[31] BiocStyle_2.28.0 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] RcppAnnoy_0.0.21 splines_4.3.1
[3] later_1.3.1 bitops_1.0-7
[5] R.oo_1.25.0 polyclip_1.10-4
[7] lifecycle_1.0.3 edgeR_3.42.4
[9] rprojroot_2.0.3 globals_0.16.2
[11] processx_3.8.2 lattice_0.21-8
[13] MASS_7.3-60 magrittr_2.0.3
[15] limma_3.56.2 plotly_4.10.2
[17] sass_0.4.7 rmarkdown_2.23
[19] jquerylib_0.1.4 yaml_2.3.7
[21] httpuv_1.6.11 sctransform_0.3.5
[23] sp_2.0-0 spatstat.sparse_3.0-2
[25] reticulate_1.30 pbapply_1.7-2
[27] abind_1.4-5 zlibbioc_1.46.0
[29] rvest_1.0.3 Rtsne_0.16
[31] R.utils_2.12.2 RCurl_1.98-1.12
[33] git2r_0.32.0 GenomeInfoDbData_1.2.10
[35] ggrepel_0.9.3 irlba_2.3.5.1
[37] listenv_0.9.0 spatstat.utils_3.0-3
[39] goftest_1.2-3 dqrng_0.3.0
[41] spatstat.random_3.1-5 fitdistrplus_1.1-11
[43] parallelly_1.36.0 svglite_2.1.1
[45] DelayedMatrixStats_1.22.1 leiden_0.4.3
[47] codetools_0.2-19 DelayedArray_0.26.6
[49] xml2_1.3.5 tidyselect_1.2.0
[51] farver_2.1.1 viridis_0.6.4
[53] ScaledMatrix_1.8.1 spatstat.explore_3.2-1
[55] webshot_0.5.5 jsonlite_1.8.7
[57] BiocNeighbors_1.18.0 ellipsis_0.3.2
[59] progressr_0.13.0 ggridges_0.5.4
[61] survival_3.5-5 systemfonts_1.0.4
[63] tools_4.3.1 ica_1.0-3
[65] Rcpp_1.0.11 gridExtra_2.3
[67] xfun_0.39 HDF5Array_1.28.1
[69] withr_2.5.0 BiocManager_1.30.21.1
[71] fastmap_1.1.1 rhdf5filters_1.12.1
[73] fansi_1.0.4 rsvd_1.0.5
[75] callr_3.7.3 digest_0.6.33
[77] timechange_0.2.0 R6_2.5.1
[79] mime_0.12 colorspace_2.1-0
[81] scattermore_1.2 tensor_1.5
[83] spatstat.data_3.0-1 R.methodsS3_1.8.2
[85] utf8_1.2.3 generics_0.1.3
[87] data.table_1.14.8 httr_1.4.6
[89] htmlwidgets_1.6.2 S4Arrays_1.0.4
[91] whisker_0.4.1 uwot_0.1.16
[93] pkgconfig_2.0.3 gtable_0.3.3
[95] lmtest_0.9-40 XVector_0.40.0
[97] htmltools_0.5.5 scales_1.2.1
[99] png_0.1-8 knitr_1.43
[101] rstudioapi_0.15.0 tzdb_0.4.0
[103] reshape2_1.4.4 nlme_3.1-162
[105] cachem_1.0.8 zoo_1.8-12
[107] rhdf5_2.44.0 KernSmooth_2.23-22
[109] vipor_0.4.5 parallel_4.3.1
[111] miniUI_0.1.1.1 pillar_1.9.0
[113] grid_4.3.1 vctrs_0.6.3
[115] RANN_2.6.1 promises_1.2.0.1
[117] BiocSingular_1.16.0 beachmat_2.16.0
[119] xtable_1.8-4 cluster_2.1.4
[121] beeswarm_0.4.0 evaluate_0.21
[123] locfit_1.5-9.8 cli_3.6.1
[125] compiler_4.3.1 rlang_1.1.1
[127] crayon_1.5.2 future.apply_1.11.0
[129] labeling_0.4.2 ps_1.7.5
[131] ggbeeswarm_0.7.2 getPass_0.2-2
[133] plyr_1.8.8 fs_1.6.3
[135] stringi_1.7.12 viridisLite_0.4.2
[137] deldir_1.0-9 BiocParallel_1.34.2
[139] munsell_0.5.0 lazyeval_0.2.2
[141] spatstat.geom_3.2-4 Matrix_1.6-0
[143] hms_1.1.3 patchwork_1.1.2
[145] sparseMatrixStats_1.12.2 future_1.33.0
[147] Rhdf5lib_1.22.0 shiny_1.7.4.1
[149] highr_0.10 ROCR_1.0-11
[151] igraph_1.5.0 bslib_0.5.0