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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] 38436 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] 30768 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")Ensembl site unresponsive, trying uswest mirrorsymbols <- 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] 24301 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] 24301 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] 27482 117rm(hek.seu)
rm(hek.counts)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] stats graphics grDevices utils datasets methods base
other attached packages:
[1] SeuratObject_4.0.4 Seurat_4.0.5 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] BiocFileCache_2.0.0 plyr_1.8.6 igraph_1.2.9
[4] lazyeval_0.2.2 splines_4.1.2 listenv_0.8.0
[7] scattermore_0.7 GenomeInfoDb_1.28.4 ggplot2_3.3.5
[10] digest_0.6.29 htmltools_0.5.2 fansi_0.5.0
[13] magrittr_2.0.1 memoise_2.0.0 tensor_1.5
[16] cluster_2.1.2 ROCR_1.0-11 globals_0.14.0
[19] Biostrings_2.60.2 matrixStats_0.61.0 spatstat.sparse_2.0-0
[22] prettyunits_1.1.1 colorspace_2.0-2 rappdirs_0.3.3
[25] blob_1.2.2 ggrepel_0.9.1 xfun_0.28
[28] dplyr_1.0.7 crayon_1.4.2 RCurl_1.98-1.5
[31] jsonlite_1.7.2 spatstat.data_2.1-0 survival_3.2-13
[34] zoo_1.8-9 glue_1.5.1 polyclip_1.10-0
[37] gtable_0.3.0 zlibbioc_1.38.0 XVector_0.32.0
[40] leiden_0.3.9 future.apply_1.8.1 BiocGenerics_0.38.0
[43] abind_1.4-5 scales_1.1.1 DBI_1.1.1
[46] miniUI_0.1.1.1 Rcpp_1.0.7 progress_1.2.2
[49] viridisLite_0.4.0 xtable_1.8-4 reticulate_1.22
[52] spatstat.core_2.3-2 bit_4.0.4 stats4_4.1.2
[55] htmlwidgets_1.5.4 httr_1.4.2 RColorBrewer_1.1-2
[58] ellipsis_0.3.2 ica_1.0-2 pkgconfig_2.0.3
[61] XML_3.99-0.8 dbplyr_2.1.1 sass_0.4.0
[64] uwot_0.1.11 deldir_1.0-6 utf8_1.2.2
[67] here_1.0.1 tidyselect_1.1.1 rlang_0.4.12
[70] reshape2_1.4.4 later_1.3.0 AnnotationDbi_1.54.1
[73] munsell_0.5.0 tools_4.1.2 cachem_1.0.6
[76] generics_0.1.1 RSQLite_2.2.9 ggridges_0.5.3
[79] evaluate_0.14 stringr_1.4.0 fastmap_1.1.0
[82] yaml_2.2.1 goftest_1.2-3 knitr_1.36
[85] bit64_4.0.5 fs_1.5.2 fitdistrplus_1.1-6
[88] purrr_0.3.4 RANN_2.6.1 KEGGREST_1.32.0
[91] pbapply_1.5-0 future_1.23.0 nlme_3.1-152
[94] whisker_0.4 mime_0.12 xml2_1.3.3
[97] biomaRt_2.48.3 compiler_4.1.2 filelock_1.0.2
[100] curl_4.3.2 plotly_4.10.0 png_0.1-7
[103] spatstat.utils_2.3-0 tibble_3.1.6 bslib_0.3.1
[106] stringi_1.7.6 lattice_0.20-45 Matrix_1.4-0
[109] vctrs_0.3.8 pillar_1.6.4 lifecycle_1.0.1
[112] spatstat.geom_2.3-1 lmtest_0.9-39 jquerylib_0.1.4
[115] RcppAnnoy_0.0.19 bitops_1.0-7 data.table_1.14.2
[118] cowplot_1.1.1 irlba_2.3.5 httpuv_1.6.3
[121] patchwork_1.1.1 R6_2.5.1 promises_1.2.0.1
[124] KernSmooth_2.23-20 gridExtra_2.3 IRanges_2.26.0
[127] parallelly_1.29.0 codetools_0.2-18 MASS_7.3-54
[130] assertthat_0.2.1 rprojroot_2.0.2 withr_2.4.3
[133] sctransform_0.3.2.9008 GenomeInfoDbData_1.2.6 S4Vectors_0.30.2
[136] hms_1.1.1 mgcv_1.8-38 parallel_4.1.2
[139] grid_4.1.2 rpart_4.1-15 tidyr_1.1.4
[142] rmarkdown_2.11 Rtsne_0.15 git2r_0.29.0
[145] Biobase_2.52.0 shiny_1.7.1
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] stats graphics grDevices utils datasets methods base
other attached packages:
[1] SeuratObject_4.0.4 Seurat_4.0.5 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] BiocFileCache_2.0.0 plyr_1.8.6 igraph_1.2.9
[4] lazyeval_0.2.2 splines_4.1.2 listenv_0.8.0
[7] scattermore_0.7 GenomeInfoDb_1.28.4 ggplot2_3.3.5
[10] digest_0.6.29 htmltools_0.5.2 fansi_0.5.0
[13] magrittr_2.0.1 memoise_2.0.0 tensor_1.5
[16] cluster_2.1.2 ROCR_1.0-11 globals_0.14.0
[19] Biostrings_2.60.2 matrixStats_0.61.0 spatstat.sparse_2.0-0
[22] prettyunits_1.1.1 colorspace_2.0-2 rappdirs_0.3.3
[25] blob_1.2.2 ggrepel_0.9.1 xfun_0.28
[28] dplyr_1.0.7 crayon_1.4.2 RCurl_1.98-1.5
[31] jsonlite_1.7.2 spatstat.data_2.1-0 survival_3.2-13
[34] zoo_1.8-9 glue_1.5.1 polyclip_1.10-0
[37] gtable_0.3.0 zlibbioc_1.38.0 XVector_0.32.0
[40] leiden_0.3.9 future.apply_1.8.1 BiocGenerics_0.38.0
[43] abind_1.4-5 scales_1.1.1 DBI_1.1.1
[46] miniUI_0.1.1.1 Rcpp_1.0.7 progress_1.2.2
[49] viridisLite_0.4.0 xtable_1.8-4 reticulate_1.22
[52] spatstat.core_2.3-2 bit_4.0.4 stats4_4.1.2
[55] htmlwidgets_1.5.4 httr_1.4.2 RColorBrewer_1.1-2
[58] ellipsis_0.3.2 ica_1.0-2 pkgconfig_2.0.3
[61] XML_3.99-0.8 dbplyr_2.1.1 sass_0.4.0
[64] uwot_0.1.11 deldir_1.0-6 utf8_1.2.2
[67] here_1.0.1 tidyselect_1.1.1 rlang_0.4.12
[70] reshape2_1.4.4 later_1.3.0 AnnotationDbi_1.54.1
[73] munsell_0.5.0 tools_4.1.2 cachem_1.0.6
[76] generics_0.1.1 RSQLite_2.2.9 ggridges_0.5.3
[79] evaluate_0.14 stringr_1.4.0 fastmap_1.1.0
[82] yaml_2.2.1 goftest_1.2-3 knitr_1.36
[85] bit64_4.0.5 fs_1.5.2 fitdistrplus_1.1-6
[88] purrr_0.3.4 RANN_2.6.1 KEGGREST_1.32.0
[91] pbapply_1.5-0 future_1.23.0 nlme_3.1-152
[94] whisker_0.4 mime_0.12 xml2_1.3.3
[97] biomaRt_2.48.3 compiler_4.1.2 filelock_1.0.2
[100] curl_4.3.2 plotly_4.10.0 png_0.1-7
[103] spatstat.utils_2.3-0 tibble_3.1.6 bslib_0.3.1
[106] stringi_1.7.6 lattice_0.20-45 Matrix_1.4-0
[109] vctrs_0.3.8 pillar_1.6.4 lifecycle_1.0.1
[112] spatstat.geom_2.3-1 lmtest_0.9-39 jquerylib_0.1.4
[115] RcppAnnoy_0.0.19 bitops_1.0-7 data.table_1.14.2
[118] cowplot_1.1.1 irlba_2.3.5 httpuv_1.6.3
[121] patchwork_1.1.1 R6_2.5.1 promises_1.2.0.1
[124] KernSmooth_2.23-20 gridExtra_2.3 IRanges_2.26.0
[127] parallelly_1.29.0 codetools_0.2-18 MASS_7.3-54
[130] assertthat_0.2.1 rprojroot_2.0.2 withr_2.4.3
[133] sctransform_0.3.2.9008 GenomeInfoDbData_1.2.6 S4Vectors_0.30.2
[136] hms_1.1.1 mgcv_1.8-38 parallel_4.1.2
[139] grid_4.1.2 rpart_4.1-15 tidyr_1.1.4
[142] rmarkdown_2.11 Rtsne_0.15 git2r_0.29.0
[145] Biobase_2.52.0 shiny_1.7.1