TabulaMurisData 1.14.0
的TabulaMurisData数据包提供对10x和SmartSeq2单细胞RNA-seq数据集的访问Tabula Muris财团．可以通过在ExperimentHub中查询包名来查看包的内容。
suppressPackageStartupMessages({library(ExperimentHub) library(singlecel实验实验)library(TabulaMurisData)}) #> snapshotDate(): 2022-04-19 eh <- ExperimentHub() #> snapshotDate(): 2022-04-19 query(eh，“TabulaMurisData”)#> ExperimentHub with 2 records #> # snapshotDate(): 2022-04-19 #> # $dataprovider: TabulaMuris Consortium #> # $species: Mus musculus #> # $rdataclass: singlecel实验实验#> #附加mcols():taxonomyid，基因组，描述，#> # coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags， #> # rdatapath, sourceurl, sourcetype #> #检索记录，例如，'object[["EH1617"]]]' #> #> title #> EH1617 | TabulaMurisDroplet #> EH1618 | TabulaMurisSmartSeq2可以使用它们的ExperimentHub登录号或此包中提供的方便函数访问各个数据集。例如，对于10x数据:
TabulaMurisData and browseVignettes('TabulaMurisData') for documentation #> load from cache droplet #> class: singlecellexper实验组#> 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): #> mainExpName: NULL #> altExpNames(0): droplet <- TabulaMurisDroplet() #> see ?TabulaMurisData和browseVignettes('TabulaMurisData') for documentation #>加载从缓存droplet #>类:SingleCellExperiment #> dim: 23341 70118 #>元数据(0):#> assays(1):计数#> 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): #> mainExpName: NULL #> altExpNames(0):iSEE每个数据集以a的形式提供SingleCellExperiment对象。为了进一步了解数据集的内容，可以使用iSEE包中。为了实现本文的目的，我们首先对10x数据集中的一小部分单元格进行子抽样，以减少运行时间。
set.seed(1234) se <- droplet[， sample(seq_len(ncol(droplet))， 250, replace = FALSE)] se #> class: singlecel实验#> dim: 23341 250 #> metadata(0): #> assays(1): counts #> rownames(23341): 0610005C13Rik 0610007C21Rik…Zzef1 Zzz3 #> rowData names(2): ID Symbol #> colnames(250): 10X_P8_12_ACGGGCTGTCAGAGGT 10X_P7_10_CGTCCATGTTATGCGT #>…10X_P7_9_TGACAACGTGTAAGTA #> colData names(10): cell channel…cell_ontology_id free_annotation #> reducedDimNames(0): #> mainExpName: NULL #> altExpNames(0):接下来，我们计算大小因子并使用食物而且嘘包，并使用PCA和t-SNE进行降维。
se <- scran::computeSumFactors(se) se <- scater::logNormCounts(se) se <- scater::runPCA(se) se <- scater::runTSNE(se)最后，我们调用iSEE下采样SingleCellExperiment对象。的实例iSEE包含所提供的数据集。
if (require(iSEE)) {iSEE(se)}sessionInfo() #> R version 4.2.0 RC (2022-04-19 r82224) #>平台:x86_64-pc-linux-gnu (64-bit) #>运行在:Ubuntu 20.04.4 LTS #> #>矩阵产品:默认#> BLAS: /home/biocbuild/bbs-3.15-bioc/R/lib/libRblas。所以#> LAPACK: /home/biocbuild/bbs-3.15-bioc/R/lib/libRlapack。so #> #> locale: #> [1] LC_CTYPE=en_US。UTF-8 LC_NUMERIC= c# > [3] LC_TIME=en_GB 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# > #>附加基础包:#> [1]stats4 stats graphics grDevices utils datasets methods #>[8]基础#> #>其他附加包:#> [1] TabulaMurisData_1.14.0 SingleCellExperiment_1.18.0 #> [3] SummarizedExperiment_1.26.0 Biobase_2.56.0 #> [5] GenomicRanges_1.48.0 GenomeInfoDb_1.32.0 #> [7] IRanges_2.30.0 S4Vectors_0.34.0 # [9] MatrixGenerics_1.8.0 matrixStats_0.62.0 #> [11] ExperimentHub_2.4.0 AnnotationHub_3.4.0 #> [13] BiocFileCache_2.4.0 dbplyr_2.1.1 #> [15] BiocGenerics_0.42.0 BiocStyle_2.24.0 #> #>通过命名空间加载(并且没有附加):#> [1] Rtsne_0.16 ggbeeswarm_0.3.2 #> [3] colorspace_2.0-3 elliptsis_0.3.2 #> [5] sccuttle_1 .6.0 bluster_1.6.0 #> [7] XVector_0.36.0 BiocNeighbors_1.14.0 #> b[9] ggrepel_0.9.1 bit64_4.0.5 #> [11] interactiveDisplayBase_1.34.0 AnnotationDbi_1.58.0 #> [13] fansi_1.0.3 sparseMatrixStats_1.8.0 #> [15] cachem_1.0.6 knitr_1.39 #> [17] scater_1.24.0 jsonlite_1.8.0 #> [19] cluster_1.3 png_0.1-7 #> [21] shiny_1.7.1 BiocManager_1.30.17 #> [23] compiler_4.2.0 httr_1.4.2 #> [25] dqrng_0.3.0 assertthat_0.2.1#> [27] Matrix_1.4-1 fastmap_1.1.0 #> [29] limma_3.52.0 cli_3.3.0 #> [31] later_1.3.0 BiocSingular_1.12.0 #> [33] htmltools_0.5.2 tools_4.2.0 #> [37] gtable_0.3.0 glue_1.6.2 #> [39] GenomeInfoDbData_1.2.8 dplyr_1.0.8 #> [41] rappdirs_0.3.3 rcpp_0.8.3 #> [43] jquerylib_0.1.4 vctrs_0.4.1 #> [45] Biostrings_2.64.0 DelayedMatrixStats_1.18.0 #> [47] xfun_0.30 string_1 .4.0 #> [51] lifecycle_1.0.1 irlba_2.3.5 #> [53] statmod_1.4.36edgeR_3.38.0 #> [55] zlibbioc_1.42.0 scaltic_1 .2.0 #> [57] [57] promisl_1 .2.0.1 parallel_4.2.0 #> [59] yaml_2.3.5 curl_4.3.2 #> [61] gridExtra_2.3 memoise_2.0.1 #> [63] ggplot2_3.3.5 sass_0.4.1 #> [65] stringi_1.7.6 RSQLite_2.2.12 #> [67] BiocVersion_3.15.2 scalsqlite_1.4.0 #> [69] scran_1.24.0 filelock_1.0.2 #> [71] BiocParallel_1.30.0 rlang_1.0.2 #> [73] pkgconfig_2.0.3 bitops_1.0-7 #> [75] evaluate_0.15 lattice_0.20-45 #> [77] purrr_0.3.4 bit_4.0.4 #> [79] tidyselect_1.1.2 magrittr_2.0.3 #> [81]bookdown_0.26 R6_2.5.1 #> [83] generics_0.1.2 metapod_1.4.0 #> [85] DelayedArray_0.22.0 DBI_1.1.2 #> [87] pillar_1.7.0 withr_2.5.0 #> [89] KEGGREST_1.36.0 RCurl_1.98-1.6 #> [91] tibble_3.1.6 crayon_1.5.1 #> [93] utf8_1.2.2 rmarkdown_2.14 #> [95] viridis_0.6.2 locfit_1.5-9.5 #> [97] grid_4.2.0 blob_1.2.3 #> [99] digest_0.6.29 xtable_1.8-4 #> [101] httpuv_1.6.5 munsell_0.5.0 #> [103] viridisLite_0.4.0 beeswarm_0.4.0 #> [105] vipor_0.4.5 bslib_0.3.1