TabulaMurisData 1.0.0
The TabulaMurisData data package provides access to the 10x and SmartSeq2
single-cell RNA-seq data sets from
the Tabula Muris Consortium.
The contents of the package can be seen by querying the ExperimentHub for the
package name.
suppressPackageStartupMessages({
library(ExperimentHub)
library(SingleCellExperiment)
library(TabulaMurisData)
})
#> snapshotDate(): 2018-10-31
eh <- ExperimentHub()
#> snapshotDate(): 2018-10-31
query(eh, "TabulaMurisData")
#> ExperimentHub with 2 records
#> # snapshotDate(): 2018-10-31
#> # $dataprovider: Tabula Muris Consortium
#> # $species: Mus musculus
#> # $rdataclass: SingleCellExperiment
#> # additional mcols(): taxonomyid, genome, description,
#> # coordinate_1_based, maintainer, rdatadateadded, preparerclass,
#> # tags, rdatapath, sourceurl, sourcetype
#> # retrieve records with, e.g., 'object[["EH1617"]]'
#>
#> title
#> EH1617 | TabulaMurisDroplet
#> EH1618 | TabulaMurisSmartSeq2The individual data sets can be accessed using either their ExperimentHub accession number, or the convenience functions provided in this package. For example, for the 10x data:
droplet <- eh[["EH1617"]]
#> see ?TabulaMurisData and browseVignettes('TabulaMurisData') for documentation
#> downloading 0 resources
#> loading from cache
#> '/home/biocbuild//.ExperimentHub/1617'
droplet
#> class: SingleCellExperiment
#> dim: 23341 70118
#> metadata(0):
#> assays(1): counts
#> rownames(23341): 0610005C13Rik 0610007C21Rik ... Zzef1 Zzz3
#> rowData names(2): ID Symbol
#> colnames(70118): 10X_P4_0_AAACCTGAGATTACCC 10X_P4_0_AAACCTGAGTGCCAGA
#> ... 10X_P8_15_TTTGTCATCTTACCGC 10X_P8_15_TTTGTCATCTTGTTTG
#> colData names(10): cell channel ... cell_ontology_id free_annotation
#> reducedDimNames(0):
#> spikeNames(0):
droplet <- TabulaMurisDroplet()
#> snapshotDate(): 2018-10-31
#> see ?TabulaMurisData and browseVignettes('TabulaMurisData') for documentation
#> downloading 0 resources
#> loading from cache
#> '/home/biocbuild//.ExperimentHub/1617'
droplet
#> class: SingleCellExperiment
#> dim: 23341 70118
#> metadata(0):
#> assays(1): counts
#> rownames(23341): 0610005C13Rik 0610007C21Rik ... Zzef1 Zzz3
#> rowData names(2): ID Symbol
#> colnames(70118): 10X_P4_0_AAACCTGAGATTACCC 10X_P4_0_AAACCTGAGTGCCAGA
#> ... 10X_P8_15_TTTGTCATCTTACCGC 10X_P8_15_TTTGTCATCTTGTTTG
#> colData names(10): cell channel ... cell_ontology_id free_annotation
#> reducedDimNames(0):
#> spikeNames(0):iSEEEach data set is provided in the form of a SingleCellExperiment object. To
gain further insights into the contents of the data sets, they can be explored
using, e.g., the iSEE package. For the purposes of this vignette,
we first subsample a small subset of the cells in the 10x data set, to reduce
the run time.
set.seed(1234)
se <- droplet[, sample(seq_len(ncol(droplet)), 250, replace = FALSE)]
se
#> class: SingleCellExperiment
#> dim: 23341 250
#> metadata(0):
#> assays(1): counts
#> rownames(23341): 0610005C13Rik 0610007C21Rik ... Zzef1 Zzz3
#> rowData names(2): ID Symbol
#> colnames(250): 10X_P4_4_GGGACCTCATTATCTC 10X_P8_12_GTAACTGTCGCCAGCA
#> ... 10X_P8_15_GCTGCGAAGTGCGTGA 10X_P8_14_CTCTAATGTTGTGGCC
#> colData names(10): cell channel ... cell_ontology_id free_annotation
#> reducedDimNames(0):
#> spikeNames(0):Next, we calculate size factors and normalize the data using the scran and scater packages, and perform dimension reduction using PCA and t-SNE.
se <- scran::computeSumFactors(se)
se <- scater::normalize(se)
se <- scater::runPCA(se)
se <- scater::runTSNE(se)Finally, we call iSEE with the subsampled SingleCellExperiment object. This
opens up an instance of iSEE containing the provided data set.
if (require(iSEE)) {
iSEE(se)
}sessionInfo()
#> R version 3.5.1 Patched (2018-07-12 r74967)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 16.04.5 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.8-bioc/R/lib/libRblas.so
#> LAPACK: /home/biocbuild/bbs-3.8-bioc/R/lib/libRlapack.so
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
#> [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] stats4 parallel stats graphics grDevices utils datasets
#> [8] methods base
#>
#> other attached packages:
#> [1] TabulaMurisData_1.0.0 SingleCellExperiment_1.4.0
#> [3] SummarizedExperiment_1.12.0 DelayedArray_0.8.0
#> [5] BiocParallel_1.16.0 matrixStats_0.54.0
#> [7] Biobase_2.42.0 GenomicRanges_1.34.0
#> [9] GenomeInfoDb_1.18.0 IRanges_2.16.0
#> [11] S4Vectors_0.20.0 ExperimentHub_1.8.0
#> [13] AnnotationHub_2.14.0 BiocGenerics_0.28.0
#> [15] BiocStyle_2.10.0
#>
#> loaded via a namespace (and not attached):
#> [1] bitops_1.0-6 bit64_0.9-7
#> [3] httr_1.3.1 rprojroot_1.3-2
#> [5] dynamicTreeCut_1.63-1 tools_3.5.1
#> [7] backports_1.1.2 R6_2.3.0
#> [9] vipor_0.4.5 HDF5Array_1.10.0
#> [11] DBI_1.0.0 lazyeval_0.2.1
#> [13] colorspace_1.3-2 gridExtra_2.3
#> [15] tidyselect_0.2.5 bit_1.1-14
#> [17] curl_3.2 compiler_3.5.1
#> [19] BiocNeighbors_1.0.0 bookdown_0.7
#> [21] scales_1.0.0 stringr_1.3.1
#> [23] digest_0.6.18 rmarkdown_1.10
#> [25] XVector_0.22.0 scater_1.10.0
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#> [31] RSQLite_2.1.1 shiny_1.1.0
#> [33] DelayedMatrixStats_1.4.0 bindr_0.1.1
#> [35] dplyr_0.7.7 RCurl_1.95-4.11
#> [37] magrittr_1.5 GenomeInfoDbData_1.2.0
#> [39] Matrix_1.2-14 ggbeeswarm_0.6.0
#> [41] Rcpp_0.12.19 munsell_0.5.0
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#> [47] edgeR_3.24.0 zlibbioc_1.28.0
#> [49] Rtsne_0.13 rhdf5_2.26.0
#> [51] plyr_1.8.4 grid_3.5.1
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#> [57] locfit_1.5-9.1 knitr_1.20
#> [59] pillar_1.3.0 igraph_1.2.2
#> [61] reshape2_1.4.3 glue_1.3.0
#> [63] evaluate_0.12 scran_1.10.0
#> [65] BiocManager_1.30.3 httpuv_1.4.5
#> [67] gtable_0.2.0 purrr_0.2.5
#> [69] assertthat_0.2.0 ggplot2_3.1.0
#> [71] xfun_0.4 mime_0.6
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