Last updated: 2021-07-07
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Knit directory: scRNA_NB_comparison/
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suppressPackageStartupMessages({
library(Seurat)
})
set.seed(42)download_dir <- here::here("data/raw_data/Smart-seq3/E-MTAB")
dir.create(download_dir, showWarnings = F, recursive = T)
file_location <- here::here(download_dir, "E-MTAB-8735.processed.3.zip")
if(! file.exists(file_location)){
download.file("https://www.ebi.ac.uk/arrayexpress/files/E-MTAB-8735/E-MTAB-8735.processed.3.zip", file_location)
}
unzip(file_location, exdir=download_dir)pbmc <- read.csv(here::here(download_dir, "HCA.UMIcounts.PBMC.txt"), stringsAsFactors = F, sep="\t")
dim(pbmc)[1] 38630 3129pbmc[1:5, 1:5] AACGTGACAAAACTGACCAA AACGTGACAAACCGATTAGA AACGTGACAAAGTCTAGAGA
ENSG00000000003 0 0 0
ENSG00000000005 0 0 0
ENSG00000000419 0 0 0
ENSG00000000457 0 0 0
ENSG00000000460 0 0 0
AACGTGACAACCTCCTAGGT AACGTGACAACGATTACGTA
ENSG00000000003 0 0
ENSG00000000005 0 0
ENSG00000000419 1 0
ENSG00000000457 0 0
ENSG00000000460 0 0gene_ids <- rownames(pbmc)
ensembl <- biomaRt::useEnsembl(biomart = "ensembl", dataset = "hsapiens_gene_ensembl")
symbols <- biomaRt::getBM(attributes = c("ensembl_gene_id", "external_gene_name"), filters = "ensembl_gene_id",
values = gene_ids, mart = ensembl)
rownames(symbols) <- symbols$ensembl_gene_id
ids.use <- intersect(rownames(pbmc), rownames(symbols))
symbols <- symbols[ids.use, ]
pbmc.counts <- pbmc[ids.use, ]
symbols[symbols$external_gene_name=="", "external_gene_name"] <- symbols[symbols$external_gene_name=="", "ensembl_gene_id"]
rownames(pbmc.counts) <- make.unique(symbols$external_gene_name)
dim(pbmc.counts)[1] 38452 3129pbmc.seu <- CreateSeuratObject(pbmc.counts, project="PBMC__Smart-seq3", min.cells = 1, min.features = 1)Warning: Feature names cannot have underscores ('_'), replacing with dashes
('-')pbmc.seu[["percent.mt"]] <- PercentageFeatureSet(pbmc.seu, pattern = "^MT-")
dir.create(here::here("data/rds_raw"), showWarnings = F, recursive = T)
saveRDS(pbmc.seu, here::here("data/rds_raw/PBMC__Smart-seq3.rds"))
dim(pbmc.seu)[1] 30783 3129rm(pbmc.seu)
rm(pbmc.counts)fibroblasts <- read.csv(here::here(download_dir, "Smartseq3.Fibroblasts.NovaSeq.UMIcounts.txt"), stringsAsFactors = F, sep="\t")
dim(fibroblasts)[1] 24824 369fibroblasts[1:5, 1:5] AAGAGACGAACCGCAA AAGAGACGAATGCGGA AAGAGACGACAGTGGA
ENSMUSG00000000001 16 15 32
ENSMUSG00000000028 0 0 0
ENSMUSG00000000031 0 0 0
ENSMUSG00000000037 0 0 0
ENSMUSG00000000049 0 0 0
AAGAGACGACCTCACA AAGAGACGCCAACCAA
ENSMUSG00000000001 34 24
ENSMUSG00000000028 0 1
ENSMUSG00000000031 0 0
ENSMUSG00000000037 0 0
ENSMUSG00000000049 0 0gene_ids <- rownames(fibroblasts)
ensembl <- biomaRt::useEnsembl(biomart = "ensembl", dataset = "mmusculus_gene_ensembl")
symbols <- biomaRt::getBM(attributes = c("ensembl_gene_id", "external_gene_name"), filters = "ensembl_gene_id",
values = gene_ids, mart = ensembl)
rownames(symbols) <- symbols$ensembl_gene_id
ids.use <- intersect(rownames(fibroblasts), rownames(symbols))
symbols <- symbols[ids.use, ]
fibroblasts.counts <- fibroblasts[ids.use, ]
symbols[symbols$external_gene_name=="", "external_gene_name"] <- symbols[symbols$external_gene_name=="", "ensembl_gene_id"]
rownames(fibroblasts.counts) <- make.unique(symbols$external_gene_name)
dim(fibroblasts.counts)[1] 24302 369fibroblasts.seu <- CreateSeuratObject(fibroblasts.counts, project="Fibroblasts__Smart-seq3")
fibroblasts.seu[["percent.mt"]] <- PercentageFeatureSet(fibroblasts.seu, pattern = "^mt-")
saveRDS(fibroblasts.seu, here::here("data/rds_raw/Fibroblasts__Smart-seq3.rds"))
dim(fibroblasts.seu)[1] 24302 369rm(fibroblasts.seu)
rm(fibroblasts.counts)hek <- read.csv(here::here(download_dir, "Smartseq3.HEK.fwdprimer.UMIcounts.txt"), stringsAsFactors = F, sep="\t")
dim(hek)[1] 27604 117hek[1:5, 1:5] AAGAGACGCCGTGTAT AAGAGACGCCTCTCTT AAGAGACGCCTCTTCA
ENSG00000000003 8 17 29
ENSG00000000005 1 0 0
ENSG00000000419 9 12 11
ENSG00000000457 0 0 0
ENSG00000000460 0 0 6
AAGAGACGCTACGAGT AAGAGACGCTGAGACT
ENSG00000000003 24 12
ENSG00000000005 0 0
ENSG00000000419 18 7
ENSG00000000457 0 0
ENSG00000000460 1 0gene_ids <- rownames(hek)
ensembl <- biomaRt::useEnsembl(biomart = "ensembl", dataset = "hsapiens_gene_ensembl")
symbols <- biomaRt::getBM(attributes = c("ensembl_gene_id", "external_gene_name"), filters = "ensembl_gene_id",
values = gene_ids, mart = ensembl)
rownames(symbols) <- symbols$ensembl_gene_id
ids.use <- intersect(rownames(hek), rownames(symbols))
symbols <- symbols[ids.use, ]
hek.counts <- hek[ids.use, ]
symbols[symbols$external_gene_name=="", "external_gene_name"] <- symbols[symbols$external_gene_name=="", "ensembl_gene_id"]
rownames(hek.counts) <- make.unique(symbols$external_gene_name)hek.seu <- CreateSeuratObject(hek.counts, project="HEK__Smart-seq3", min.cells = 1, min.features = 1)Warning: Feature names cannot have underscores ('_'), replacing with dashes
('-')hek.seu[["percent.mt"]] <- PercentageFeatureSet(hek.seu, pattern = "^MT-")
saveRDS(hek.seu, here::here("data/rds_raw/HEK__Smart-seq3.rds"))
dim(hek.seu)[1] 27491 117rm(hek.seu)
rm(hek.counts)sessionInfo()R version 4.0.0 (2020-04-24)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.5 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1
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] stats graphics grDevices utils datasets methods base
other attached packages:
[1] SeuratObject_4.0.2 Seurat_4.0.3 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] BiocFileCache_1.14.0 plyr_1.8.6 igraph_1.2.6
[4] lazyeval_0.2.2 splines_4.0.0 listenv_0.8.0
[7] scattermore_0.7 ggplot2_3.3.5 digest_0.6.27
[10] htmltools_0.5.1.1 fansi_0.5.0 magrittr_2.0.1
[13] memoise_2.0.0 tensor_1.5 cluster_2.1.0
[16] ROCR_1.0-11 globals_0.14.0 matrixStats_0.59.0
[19] askpass_1.1 spatstat.sparse_2.0-0 prettyunits_1.1.1
[22] colorspace_2.0-2 rappdirs_0.3.3 blob_1.2.1
[25] ggrepel_0.9.1 xfun_0.24 dplyr_1.0.7
[28] crayon_1.4.1 jsonlite_1.7.2 spatstat.data_2.1-0
[31] survival_3.2-7 zoo_1.8-9 glue_1.4.2
[34] polyclip_1.10-0 gtable_0.3.0 leiden_0.3.8
[37] future.apply_1.7.0 BiocGenerics_0.36.1 abind_1.4-5
[40] scales_1.1.1 DBI_1.1.1 miniUI_0.1.1.1
[43] Rcpp_1.0.6 viridisLite_0.4.0 xtable_1.8-4
[46] progress_1.2.2 reticulate_1.20 spatstat.core_2.2-0
[49] bit_4.0.4 stats4_4.0.0 htmlwidgets_1.5.3
[52] httr_1.4.2 RColorBrewer_1.1-2 ellipsis_0.3.2
[55] ica_1.0-2 pkgconfig_2.0.3 XML_3.99-0.6
[58] dbplyr_2.1.1 sass_0.4.0 uwot_0.1.10
[61] deldir_0.2-10 utf8_1.2.1 here_1.0.1
[64] tidyselect_1.1.1 rlang_0.4.11 reshape2_1.4.4
[67] later_1.2.0 AnnotationDbi_1.52.0 munsell_0.5.0
[70] tools_4.0.0 cachem_1.0.5 generics_0.1.0
[73] RSQLite_2.2.7 ggridges_0.5.3 evaluate_0.14
[76] stringr_1.4.0 fastmap_1.1.0 yaml_2.2.1
[79] goftest_1.2-2 knitr_1.33 bit64_4.0.5
[82] fs_1.5.0 fitdistrplus_1.1-5 purrr_0.3.4
[85] RANN_2.6.1 pbapply_1.4-3 future_1.21.0
[88] nlme_3.1-152 whisker_0.4 mime_0.11
[91] xml2_1.3.2 biomaRt_2.46.3 compiler_4.0.0
[94] curl_4.3.2 plotly_4.9.4.1 png_0.1-7
[97] spatstat.utils_2.2-0 tibble_3.1.2 bslib_0.2.5.1
[100] stringi_1.6.2 lattice_0.20-41 Matrix_1.3-4
[103] vctrs_0.3.8 pillar_1.6.1 lifecycle_1.0.0
[106] spatstat.geom_2.2-0 lmtest_0.9-38 jquerylib_0.1.4
[109] RcppAnnoy_0.0.18 data.table_1.14.0 cowplot_1.1.1
[112] irlba_2.3.3 httpuv_1.6.1 patchwork_1.1.1
[115] R6_2.5.0 promises_1.2.0.1 KernSmooth_2.23-17
[118] gridExtra_2.3 IRanges_2.24.1 parallelly_1.26.0
[121] codetools_0.2-16 MASS_7.3-51.6 assertthat_0.2.1
[124] openssl_1.4.4 rprojroot_2.0.2 withr_2.4.2
[127] sctransform_0.3.2.9008 S4Vectors_0.28.1 mgcv_1.8-33
[130] parallel_4.0.0 hms_1.1.0 grid_4.0.0
[133] rpart_4.1-15 tidyr_1.1.3 rmarkdown_2.9
[136] Rtsne_0.15 git2r_0.26.1 Biobase_2.50.0
[139] shiny_1.6.0
sessionInfo()R version 4.0.0 (2020-04-24)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.5 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1
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] stats graphics grDevices utils datasets methods base
other attached packages:
[1] SeuratObject_4.0.2 Seurat_4.0.3 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] BiocFileCache_1.14.0 plyr_1.8.6 igraph_1.2.6
[4] lazyeval_0.2.2 splines_4.0.0 listenv_0.8.0
[7] scattermore_0.7 ggplot2_3.3.5 digest_0.6.27
[10] htmltools_0.5.1.1 fansi_0.5.0 magrittr_2.0.1
[13] memoise_2.0.0 tensor_1.5 cluster_2.1.0
[16] ROCR_1.0-11 globals_0.14.0 matrixStats_0.59.0
[19] askpass_1.1 spatstat.sparse_2.0-0 prettyunits_1.1.1
[22] colorspace_2.0-2 rappdirs_0.3.3 blob_1.2.1
[25] ggrepel_0.9.1 xfun_0.24 dplyr_1.0.7
[28] crayon_1.4.1 jsonlite_1.7.2 spatstat.data_2.1-0
[31] survival_3.2-7 zoo_1.8-9 glue_1.4.2
[34] polyclip_1.10-0 gtable_0.3.0 leiden_0.3.8
[37] future.apply_1.7.0 BiocGenerics_0.36.1 abind_1.4-5
[40] scales_1.1.1 DBI_1.1.1 miniUI_0.1.1.1
[43] Rcpp_1.0.6 viridisLite_0.4.0 xtable_1.8-4
[46] progress_1.2.2 reticulate_1.20 spatstat.core_2.2-0
[49] bit_4.0.4 stats4_4.0.0 htmlwidgets_1.5.3
[52] httr_1.4.2 RColorBrewer_1.1-2 ellipsis_0.3.2
[55] ica_1.0-2 pkgconfig_2.0.3 XML_3.99-0.6
[58] dbplyr_2.1.1 sass_0.4.0 uwot_0.1.10
[61] deldir_0.2-10 utf8_1.2.1 here_1.0.1
[64] tidyselect_1.1.1 rlang_0.4.11 reshape2_1.4.4
[67] later_1.2.0 AnnotationDbi_1.52.0 munsell_0.5.0
[70] tools_4.0.0 cachem_1.0.5 generics_0.1.0
[73] RSQLite_2.2.7 ggridges_0.5.3 evaluate_0.14
[76] stringr_1.4.0 fastmap_1.1.0 yaml_2.2.1
[79] goftest_1.2-2 knitr_1.33 bit64_4.0.5
[82] fs_1.5.0 fitdistrplus_1.1-5 purrr_0.3.4
[85] RANN_2.6.1 pbapply_1.4-3 future_1.21.0
[88] nlme_3.1-152 whisker_0.4 mime_0.11
[91] xml2_1.3.2 biomaRt_2.46.3 compiler_4.0.0
[94] curl_4.3.2 plotly_4.9.4.1 png_0.1-7
[97] spatstat.utils_2.2-0 tibble_3.1.2 bslib_0.2.5.1
[100] stringi_1.6.2 lattice_0.20-41 Matrix_1.3-4
[103] vctrs_0.3.8 pillar_1.6.1 lifecycle_1.0.0
[106] spatstat.geom_2.2-0 lmtest_0.9-38 jquerylib_0.1.4
[109] RcppAnnoy_0.0.18 data.table_1.14.0 cowplot_1.1.1
[112] irlba_2.3.3 httpuv_1.6.1 patchwork_1.1.1
[115] R6_2.5.0 promises_1.2.0.1 KernSmooth_2.23-17
[118] gridExtra_2.3 IRanges_2.24.1 parallelly_1.26.0
[121] codetools_0.2-16 MASS_7.3-51.6 assertthat_0.2.1
[124] openssl_1.4.4 rprojroot_2.0.2 withr_2.4.2
[127] sctransform_0.3.2.9008 S4Vectors_0.28.1 mgcv_1.8-33
[130] parallel_4.0.0 hms_1.1.0 grid_4.0.0
[133] rpart_4.1-15 tidyr_1.1.3 rmarkdown_2.9
[136] Rtsne_0.15 git2r_0.26.1 Biobase_2.50.0
[139] shiny_1.6.0