In this tutorial, we are going to download the PBMC data from this publication. This is a collection of immune cell types profiled from peripherial blood from two human donors across six single-cell RNA-seq technologies (10x Chromium v2 and v3, CEL-seq2, Drop-seq, inDrops, Seq-Well and Smart-seq2).
library(SingleCellExperiment)
library(HDF5Array)
library(GEDI)
library(SeuratData)
set.seed(43)The raw count matrices and metadata are available through the SeuratData package:
InstallData("pbmcsca") # Install pbmcsca objectWe will generate a Single Cell Experiment (SCE) Object.
data("pbmcsca") # Load the Seurat Object
meta<- pbmcsca[[]] # Obtaining metadata
meta$Barcode<- rownames(meta) # Naming Barcode
sce<- SingleCellExperiment(list(counts=Seurat::GetAssayData(object = pbmcsca, slot = "counts")), colData=meta) # create SCE
sce## class: SingleCellExperiment
## dim: 33694 31021
## metadata(0):
## assays(1): counts
## rownames(33694): TSPAN6 TNMD ... RP11-107E5.4 RP11-299P2.2
## rowData names(0):
## colnames(31021): pbmc1_SM2_Cell_108 pbmc1_SM2_Cell_115 ...
## pbmc2_inDrops_1_TGAATCCT.TTATGCGA.CATCTCCC
## pbmc2_inDrops_1_TGAGCACA.GAGCCTTA.CGAGTCTG
## colData names(11): orig.ident nCount_RNA ... Method Barcode
## reducedDimNames(0):
## altExpNames(0):We will also load the GEDI model for the pbmc data, which can be accessed in here.
model<- readRDS("pbmc_gedi_model_bothDonors.rds")We can then match with the cell barcodes that were used in the manuscript.
length(model$aux$cellIDs)## [1] 28035ncol(sce)## [1] 31021length(intersect(model$aux$cellIDs, colnames(sce) ) )## [1] 28035sce<- sce[,model$aux$cellIDs]The single cell experiment then can be saved.
saveHDF5SummarizedExperiment(sce, dir="../data_objects/pbmc_SCE")sessionInfo()## R version 4.0.0 (2020-04-24)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: CentOS Linux 7 (Core)
##
## Matrix products: default
## BLAS/LAPACK: /cvmfs/soft.computecanada.ca/easybuild/software/2020/Core/imkl/2020.1.217/compilers_and_libraries_2020.1.217/linux/mkl/lib/intel64_lin/libmkl_gf_lp64.so
##
## locale:
## [1] LC_CTYPE=en_CA.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_CA.UTF-8 LC_COLLATE=en_CA.UTF-8
## [5] LC_MONETARY=en_CA.UTF-8 LC_MESSAGES=en_CA.UTF-8
## [7] LC_PAPER=en_CA.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_CA.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] pbmcsca.SeuratData_3.0.0 pancreasref.SeuratData_1.0.0
## [3] panc8.SeuratData_3.0.2 kidneyref.SeuratData_1.0.1
## [5] ifnb.SeuratData_3.1.0 hcabm40k.SeuratData_3.0.0
## [7] SeuratData_0.2.2 GEDI_0.0.0.9000
## [9] HDF5Array_1.18.1 rhdf5_2.34.0
## [11] DelayedArray_0.16.3 Matrix_1.3-3
## [13] SingleCellExperiment_1.12.0 SummarizedExperiment_1.20.0
## [15] Biobase_2.50.0 GenomicRanges_1.42.0
## [17] GenomeInfoDb_1.26.7 IRanges_2.24.1
## [19] S4Vectors_0.28.1 BiocGenerics_0.36.0
## [21] MatrixGenerics_1.2.1 matrixStats_0.58.0
##
## loaded via a namespace (and not attached):
## [1] RcppEigen_0.3.3.9.1 plyr_1.8.6 igraph_1.3.4
## [4] lazyeval_0.2.2 sp_1.4-5 splines_4.0.0
## [7] listenv_0.8.0 scattermore_0.7 ggplot2_3.4.2
## [10] digest_0.6.33 htmltools_0.5.6 fansi_1.0.4
## [13] magrittr_2.0.3 tensor_1.5 cluster_2.1.0
## [16] ROCR_1.0-11 globals_0.16.2 spatstat.sparse_2.0-0
## [19] colorspace_2.0-0 rappdirs_0.3.3 ggrepel_0.9.2
## [22] xfun_0.22 dplyr_1.1.1 crayon_1.5.2
## [25] RCurl_1.98-1.3 jsonlite_1.8.7 progressr_0.10.1
## [28] spatstat.data_2.1-0 survival_3.2-11 zoo_1.8-9
## [31] glue_1.6.2 polyclip_1.10-0 gtable_0.3.0
## [34] zlibbioc_1.36.0 XVector_0.30.0 leiden_0.3.8
## [37] Rhdf5lib_1.12.1 future.apply_1.7.0 abind_1.4-5
## [40] scales_1.2.1 DBI_1.1.1 miniUI_0.1.1.1
## [43] Rcpp_1.0.8.3 viridisLite_0.4.2 xtable_1.8-4
## [46] reticulate_1.18 spatstat.core_2.1-2 htmlwidgets_1.5.3
## [49] httr_1.4.5 RColorBrewer_1.1-2 ellipsis_0.3.2
## [52] Seurat_4.1.1 ica_1.0-2 pkgconfig_2.0.3
## [55] sass_0.4.7 uwot_0.1.10 deldir_0.2-10
## [58] utf8_1.2.3 tidyselect_1.2.0 rlang_1.1.1
## [61] reshape2_1.4.4 later_1.1.0.1 munsell_0.5.0
## [64] tools_4.0.0 cachem_1.0.4 cli_3.6.1
## [67] generics_0.1.3 ggridges_0.5.3 evaluate_0.22
## [70] stringr_1.5.0 fastmap_1.1.1 yaml_2.2.1
## [73] goftest_1.2-2 knitr_1.31 fitdistrplus_1.1-3
## [76] purrr_1.0.1 RANN_2.6.1 pbapply_1.4-3
## [79] future_1.33.0 nlme_3.1-147 mime_0.10
## [82] compiler_4.0.0 plotly_4.9.3 png_0.1-7
## [85] spatstat.utils_2.1-0 tibble_3.2.1 bslib_0.5.1
## [88] stringi_1.5.3 rgeos_0.5-9 lattice_0.20-41
## [91] vctrs_0.6.1 pillar_1.9.0 lifecycle_1.0.3
## [94] rhdf5filters_1.2.0 spatstat.geom_2.1-0 lmtest_0.9-38
## [97] jquerylib_0.1.3 RcppAnnoy_0.0.18 data.table_1.14.0
## [100] cowplot_1.1.1 bitops_1.0-6 irlba_2.3.3
## [103] httpuv_1.5.5 patchwork_1.1.1 R6_2.5.0
## [106] promises_1.2.0.1 KernSmooth_2.23-16 gridExtra_2.3
## [109] parallelly_1.36.0 codetools_0.2-16 MASS_7.3-51.5
## [112] SeuratObject_4.1.0 sctransform_0.3.3 GenomeInfoDbData_1.2.4
## [115] mgcv_1.8-31 grid_4.0.0 rpart_4.1-15
## [118] tidyr_1.3.0 rmarkdown_2.7 Rtsne_0.15
## [121] shiny_1.6.0