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| File | Version | Author | Date | Message |
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suppressPackageStartupMessages({
library(Seurat)
library(SingleCellExperiment)
})
set.seed(42)download_dir <- here::here("data/raw_data/Mereu")
dir.create(download_dir, showWarnings = F, recursive = T)
dir.create(here::here("data/rds_raw"), showWarnings = F, recursive = T)
file_location <- here::here(download_dir, "sce.all_classified.technologies.RData")
if (!file.exists(file_location)) {
download.file("https://www.dropbox.com/s/i8mwmyymchx8mn8/sce.all_classified.technologies.RData?dl=0", file_location, method = "wget", extra = "--content-disposition")
}load(file = file_location)
sceclass: SingleCellExperiment
dim: 23381 20237
metadata(0):
assays(2): counts logcounts
rownames(23381): TSPAN6 DPM1 ... RPL31P58 RP11-553E24.1
rowData names(0):
colnames(20237): 10X2x5K_64221_AAACCTGCACTTCGAA
10X2x5K_64221_AAACCTGCAGTACACT ...
SMARTseqFINAL_allLanes_TTGTCGTGTCTCGGAA
SMARTseqFINAL_allLanes_TTGTCGTGTGATCCGA
colData names(3): nnet2 ident batch
reducedDimNames(2): UMAP PCA
mainExpName: NULL
altExpNames(0):metadata <- as.data.frame(colData(sce))
metadata$nnet2 <- as.character(metadata$nnet2)
metadata$ident <- as.character(metadata$ident)
metadata$batch <- as.character(metadata$batch)
head(metadata) nnet2 ident batch
10X2x5K_64221_AAACCTGCACTTCGAA B cells B cells Chromium
10X2x5K_64221_AAACCTGCAGTACACT CD4 T cells CD4 T cells 2 Chromium
10X2x5K_64221_AAACCTGTCCACTGGG CD4 T cells CD4 T cells 1 Chromium
10X2x5K_64221_AAACGGGAGAGCTTCT HEK cells HEK cells 2 Chromium
10X2x5K_64221_AAACGGGAGGTGGGTT HEK cells HEK cells 2 Chromium
10X2x5K_64221_AAACGGGCACACGCTG CD4 T cells CD4 T cells 1 Chromiumcounts_matrix <- counts(sce)
counts_matrix <- as(object = counts_matrix, Class = "dgCMatrix")
common_cols <- intersect(rownames(metadata), colnames(counts_matrix))
counts_matrix <- counts_matrix[, common_cols]
metadata <- metadata[common_cols, ]
colnames(counts_matrix) <- paste0("cell-", colnames(counts_matrix))
rownames(metadata) <- colnames(counts_matrix)
dim(counts_matrix)[1] 23381 20237seu <- CreateSeuratObject(counts_matrix, meta.data = metadata, project = "Mereu_2021_scBenchmark_Rdata", min.cells = 1, min.features = 1)
seuAn object of class Seurat
23381 features across 20237 samples within 1 assay
Active assay: RNA (23381 features, 0 variable features)nonumi.techs <- c("C1HT-medium", "C1HT-small", "ICELL8", "Smart-Seq2")
table(seu@meta.data$batch)
C1HT-medium C1HT-small CEL-Seq2 Chromium Chromium(sn) ddSEQ
2216 1606 1083 1604 1515 2109
Drop-Seq ICELL8 inDrop MARS-Seq mcSCRB-Seq Quartz-Seq2
2261 1927 686 1481 1684 1333
Smart-Seq2
732 table(seu@meta.data$ident)
Ambiguous B cells
165 1562
CD14 and FCGR3A Monocytes CD14 Monocytes
55 262
CD14+ and FCGR3A+ Monocytes CD14+ Monocytes
849 2113
CD4 T cells 1 CD4 T cells 2
602 270
CD4+ cells CD4+ T cells
432 797
CD8 T cells CD8+ T cells
140 88
CD8+ T cells and NK Cytotoxic T cells
1336 2885
Cytotoxic T cells 1 Cytotoxic T cells 2
1022 400
FCGR3A Monocytes FCGR3A+ Monocytes
37 181
HEK cells HEK cells 1
1665 1265
HEK cells 2 HEK cells 3
579 132
HEK cells1 HEK cells3
519 58
NK and Cytotoxic T cells NK cells
2244 249
unclear unknown
294 36 table(seu@meta.data$nnet2)
B cells CD14+ Monocytes CD4 T cells CD8 T cells
1657 2782 5693 2522
Dendritic cells FCGR3A+ Monocytes HEK cells Megakaryocytes
274 897 4942 45
NK cells
1425 Idents(seu) <- "ident"
hek <- subset(seu, idents = c("HEK cells", "HEK cells 2", "HEK cells1", "HEK cells 1", "HEK cells3", "HEK cells 3"))
table(hek@meta.data$ident)
HEK cells HEK cells 1 HEK cells 2 HEK cells 3 HEK cells1 HEK cells3
1665 1265 579 132 519 58 table(hek@meta.data$nnet2)
B cells CD14+ Monocytes CD4 T cells CD8 T cells
88 216 243 38
Dendritic cells FCGR3A+ Monocytes HEK cells Megakaryocytes
9 59 3526 7
NK cells
32 Idents(hek) <- "nnet2"
hek <- subset(hek, idents = c("HEK cells"))
table(hek@meta.data$ident)
HEK cells HEK cells 1 HEK cells 2 HEK cells 3 HEK cells1 HEK cells3
1435 996 454 124 467 50 table(hek@meta.data$nnet2)
HEK cells
3526 Idents(hek) <- "batch"
hek.umi <- subset(hek, idents = c(nonumi.techs), invert=TRUE)
table(hek.umi@meta.data$batch)
CEL-Seq2 Chromium Chromium(sn) ddSEQ Drop-Seq inDrop
101 326 47 517 473 49
MARS-Seq mcSCRB-Seq Quartz-Seq2
88 74 269 umi_techs <- sort(unique(hek.umi$batch))
umi_techs[1] "CEL-Seq2" "Chromium" "Chromium(sn)" "ddSEQ" "Drop-Seq"
[6] "inDrop" "MARS-Seq" "mcSCRB-Seq" "Quartz-Seq2" clean_named_techs <- c("CEL-seq2", "ChromiumV2", "ChromiumV2_sn", "ddSeq", "Drop-seq", "inDrops", "MARS-seq", "mcSCRB-seq", "Quartz-Seq2")
names(clean_named_techs) <- umi_techs
clean_named_techs CEL-Seq2 Chromium Chromium(sn) ddSEQ Drop-Seq
"CEL-seq2" "ChromiumV2" "ChromiumV2_sn" "ddSeq" "Drop-seq"
inDrop MARS-Seq mcSCRB-Seq Quartz-Seq2
"inDrops" "MARS-seq" "mcSCRB-seq" "Quartz-Seq2" hek_split <- SplitObject(hek.umi, split.by = "batch")
for (given_tech in names(hek_split)){
seu <- hek_split[[given_tech]]
seu[["percent.mt"]] <- PercentageFeatureSet(seu, pattern = "^MT-")
clean_tech <- clean_named_techs[[given_tech]]
saveRDS(seu, here::here("data/rds_raw", paste0("Mereu-HEK__", clean_tech,".rds")))
gc()
}sessionInfo()R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.3 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
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] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] SingleCellExperiment_1.14.1 SummarizedExperiment_1.22.0
[3] Biobase_2.52.0 GenomicRanges_1.44.0
[5] GenomeInfoDb_1.28.4 IRanges_2.26.0
[7] S4Vectors_0.30.2 BiocGenerics_0.38.0
[9] MatrixGenerics_1.4.3 matrixStats_0.61.0
[11] SeuratObject_4.0.4 Seurat_4.0.5
[13] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rtsne_0.15 colorspace_2.0-2 deldir_1.0-6
[4] ellipsis_0.3.2 ggridges_0.5.3 rprojroot_2.0.2
[7] XVector_0.32.0 fs_1.5.2 spatstat.data_2.1-0
[10] leiden_0.3.9 listenv_0.8.0 ggrepel_0.9.1
[13] fansi_0.5.0 codetools_0.2-18 splines_4.1.2
[16] knitr_1.36 polyclip_1.10-0 jsonlite_1.7.2
[19] ica_1.0-2 cluster_2.1.2 png_0.1-7
[22] uwot_0.1.11 shiny_1.7.1 sctransform_0.3.2.9008
[25] spatstat.sparse_2.0-0 compiler_4.1.2 httr_1.4.2
[28] assertthat_0.2.1 Matrix_1.4-0 fastmap_1.1.0
[31] lazyeval_0.2.2 later_1.3.0 htmltools_0.5.2
[34] tools_4.1.2 igraph_1.2.9 GenomeInfoDbData_1.2.6
[37] gtable_0.3.0 glue_1.5.1 RANN_2.6.1
[40] reshape2_1.4.4 dplyr_1.0.7 Rcpp_1.0.7
[43] scattermore_0.7 jquerylib_0.1.4 vctrs_0.3.8
[46] nlme_3.1-152 lmtest_0.9-39 xfun_0.28
[49] stringr_1.4.0 globals_0.14.0 mime_0.12
[52] miniUI_0.1.1.1 lifecycle_1.0.1 irlba_2.3.5
[55] goftest_1.2-3 future_1.23.0 zlibbioc_1.38.0
[58] MASS_7.3-54 zoo_1.8-9 scales_1.1.1
[61] spatstat.core_2.3-2 promises_1.2.0.1 spatstat.utils_2.3-0
[64] RColorBrewer_1.1-2 yaml_2.2.1 reticulate_1.22
[67] pbapply_1.5-0 gridExtra_2.3 ggplot2_3.3.5
[70] sass_0.4.0 rpart_4.1-15 stringi_1.7.6
[73] bitops_1.0-7 rlang_0.4.12 pkgconfig_2.0.3
[76] evaluate_0.14 lattice_0.20-45 ROCR_1.0-11
[79] purrr_0.3.4 tensor_1.5 patchwork_1.1.1
[82] htmlwidgets_1.5.4 cowplot_1.1.1 tidyselect_1.1.1
[85] here_1.0.1 parallelly_1.29.0 RcppAnnoy_0.0.19
[88] plyr_1.8.6 magrittr_2.0.1 R6_2.5.1
[91] generics_0.1.1 DelayedArray_0.18.0 DBI_1.1.1
[94] mgcv_1.8-38 pillar_1.6.4 whisker_0.4
[97] fitdistrplus_1.1-6 RCurl_1.98-1.5 survival_3.2-13
[100] abind_1.4-5 tibble_3.1.6 future.apply_1.8.1
[103] crayon_1.4.2 KernSmooth_2.23-20 utf8_1.2.2
[106] spatstat.geom_2.3-1 plotly_4.10.0 rmarkdown_2.11
[109] grid_4.1.2 data.table_1.14.2 git2r_0.29.0
[112] digest_0.6.29 xtable_1.8-4 tidyr_1.1.4
[115] httpuv_1.6.3 munsell_0.5.0 viridisLite_0.4.0
[118] bslib_0.3.1
sessionInfo()R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.3 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
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] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] SingleCellExperiment_1.14.1 SummarizedExperiment_1.22.0
[3] Biobase_2.52.0 GenomicRanges_1.44.0
[5] GenomeInfoDb_1.28.4 IRanges_2.26.0
[7] S4Vectors_0.30.2 BiocGenerics_0.38.0
[9] MatrixGenerics_1.4.3 matrixStats_0.61.0
[11] SeuratObject_4.0.4 Seurat_4.0.5
[13] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rtsne_0.15 colorspace_2.0-2 deldir_1.0-6
[4] ellipsis_0.3.2 ggridges_0.5.3 rprojroot_2.0.2
[7] XVector_0.32.0 fs_1.5.2 spatstat.data_2.1-0
[10] leiden_0.3.9 listenv_0.8.0 ggrepel_0.9.1
[13] fansi_0.5.0 codetools_0.2-18 splines_4.1.2
[16] knitr_1.36 polyclip_1.10-0 jsonlite_1.7.2
[19] ica_1.0-2 cluster_2.1.2 png_0.1-7
[22] uwot_0.1.11 shiny_1.7.1 sctransform_0.3.2.9008
[25] spatstat.sparse_2.0-0 compiler_4.1.2 httr_1.4.2
[28] assertthat_0.2.1 Matrix_1.4-0 fastmap_1.1.0
[31] lazyeval_0.2.2 later_1.3.0 htmltools_0.5.2
[34] tools_4.1.2 igraph_1.2.9 GenomeInfoDbData_1.2.6
[37] gtable_0.3.0 glue_1.5.1 RANN_2.6.1
[40] reshape2_1.4.4 dplyr_1.0.7 Rcpp_1.0.7
[43] scattermore_0.7 jquerylib_0.1.4 vctrs_0.3.8
[46] nlme_3.1-152 lmtest_0.9-39 xfun_0.28
[49] stringr_1.4.0 globals_0.14.0 mime_0.12
[52] miniUI_0.1.1.1 lifecycle_1.0.1 irlba_2.3.5
[55] goftest_1.2-3 future_1.23.0 zlibbioc_1.38.0
[58] MASS_7.3-54 zoo_1.8-9 scales_1.1.1
[61] spatstat.core_2.3-2 promises_1.2.0.1 spatstat.utils_2.3-0
[64] RColorBrewer_1.1-2 yaml_2.2.1 reticulate_1.22
[67] pbapply_1.5-0 gridExtra_2.3 ggplot2_3.3.5
[70] sass_0.4.0 rpart_4.1-15 stringi_1.7.6
[73] bitops_1.0-7 rlang_0.4.12 pkgconfig_2.0.3
[76] evaluate_0.14 lattice_0.20-45 ROCR_1.0-11
[79] purrr_0.3.4 tensor_1.5 patchwork_1.1.1
[82] htmlwidgets_1.5.4 cowplot_1.1.1 tidyselect_1.1.1
[85] here_1.0.1 parallelly_1.29.0 RcppAnnoy_0.0.19
[88] plyr_1.8.6 magrittr_2.0.1 R6_2.5.1
[91] generics_0.1.1 DelayedArray_0.18.0 DBI_1.1.1
[94] mgcv_1.8-38 pillar_1.6.4 whisker_0.4
[97] fitdistrplus_1.1-6 RCurl_1.98-1.5 survival_3.2-13
[100] abind_1.4-5 tibble_3.1.6 future.apply_1.8.1
[103] crayon_1.4.2 KernSmooth_2.23-20 utf8_1.2.2
[106] spatstat.geom_2.3-1 plotly_4.10.0 rmarkdown_2.11
[109] grid_4.1.2 data.table_1.14.2 git2r_0.29.0
[112] digest_0.6.29 xtable_1.8-4 tidyr_1.1.4
[115] httpuv_1.6.3 munsell_0.5.0 viridisLite_0.4.0
[118] bslib_0.3.1