1安装

如果(!requireNamespace("BiocManager"， quiet = TRUE)) install.packages("BiocManager")::install("SingleCellMultiModal")

2加载库

库(MultiAssayExperiment)库(SingleCellMultiModal)

3.CITE-seq数据集

CITE-seq数据是同时从同一个单元格中提取的两种数据类型的组合。第一种数据类型是scRNA-seq数据，而第二种数据类型由大约100个抗体衍生标签(ADT)组成。这个数据集是由Stoeckius等人(2017)．

CITEseq(DataType="cord_blood"， modes="*"， dry.run=TRUE, version="1.0.0")

数据集:cord_blood

## snapshotDate(): 2022-10-24

## ah_id mode file_size rdataclass rdatadateadded rdatadateremoved ## 1 EH3795 scADT_Counts 0.2 Mb matrix 2020-09-23  ## 2 EH3796 scRNAseq_Counts 22.2 Mb matrix 2020-09-23 

- CITEseq(DataType="cord_blood"， modes="*"， dry.run=FALSE, version="1.0.0"

一个MultiAssayExperiment对象，包含2个列出的实验，使用用户自定义名称和各自的类。##包含长度为2的ExperimentList类对象:## [1]scADT: 13行8617列的矩阵## [2]scRNAseq: 36280行8617列的矩阵## experiments() -获取ExperimentList实例## colData() -主/表型DataFrame ## sampleMap() -样本协调DataFrame ## ' $ '， '['， '[[' -提取colData列，子集，或实验## *格式()-转换为长或宽的DataFrame ## assays() -转换ExperimentList为矩阵的SimpleList ## exportClass() -保存数据到平面文件

实验(美)

## [1] scADT: 13行8617列的矩阵

rownames(美)

##字符长度2 ## [["scADT"]] CD3 CD4 CD8 CD45RA CD56 CD16 CD10 CD11c CD14 CD19 CD34 CCR5 CCR7 ## [[" scadeq "]] ERCC_ERCC-00104 HUMAN_A1BG…MOUSE_n-R5s25 MOUSE_n-R5s31

sampleMap(美)

##数据帧，17234行和3列##分析主colname ## <因子> <字符> <字符> ## 1 scADT CTGTTTACACCGCTAG CTGTTTACACCGCTAG ## 2 scADT CTCTACGGTGTGGCTC CTCTACGGTGTGGCTC ## 3 scADT AGCAGCCAGGCTCATT agcagccatcccat gaataagagagatcccat ## 5 scADT gtgcatagagtcatgtgcatagggtgcatgcat ## ... ... ... ...17231 scRNAseq GGCGTGTAGTGTACTC GGCGTGTAGTGTACTC ## 17232 scRNAseq CGTATGCCGTCTTCTG CGTATGCCGTCTTCTG ## 17233 scRNAseq TACACGACGCTCTTCC TACACGACGCTCTTCC ## 17234 scRNAseq ACACGACGCTCTTCCG ACACGACGCTCTTCCG

头(实验(mae) scRNAseq美元)[1:4]

## ctgttaccgctag ctctacggtgtggctc agcagccaggctcatt ## ercc_ercc-00104 00 00 ## human_a1bg 00 0 ## human_a1bg 00 00 ## human_a1bg 00 00 ## human_a1bg 0 ## human_a1bg - as1 00 0 ## gaataagagatcccat ## ercc_ercc-00104 0 ## human_a1bg 0 ## human_a1bg 0 ## human_a2m 0 ## human_a2m - as1 00 0 ## human_a1bg 0 ## human_a2m 0 ## human_a2m - as1 00 0 ## human_a1bg 0 ## human_a2m 0 ## human_a2m - as1 00

头(实验(mae) scADT美元)[1:4]

## cd3 60 52 89 55 ## cd4 72 49 112 66 ## cd8 76 59 61 56 ## cd45ra 575 3943 682 378 ## cd56 64 68 87 58 ## cd16 161 107 117 82

4singlecel实验对象转换

因为一些方法已经大量使用(如在SingleCellExperiment装饰图案或CiteFuse装饰图案在哪里SingleCellExperiment对象用于CITE-seq数据时，我们提供了一个转换CITE-seq的函数MultiAssayExperiment对象转换为SingleCellExperiment对象，scRNA-seq数据为计数，scADT数据为计数altExp年代。

sce <- CITEseq(DataType="cord_blood"， modes="*"， dry.run=FALSE, version="1.0.0"， DataClass=" singlecel实验"

##类:singlecel实验## dim: 36280 8617 ##元数据(0):## assays(1):计数## rownames(36280): ERCC_ERCC-00104 HUMAN_A1BG…MOUSE_n-R5s25 ## MOUSE_n-R5s31 ## rowData names(0): ## colnames(8617): CTGTTTACACCGCTAG CTCTACGGTGTGGCTC…TACACGACGCTCTTCC ## acacgctcttccg# # colData names(0): ## reducedDimNames(0): ## mainExpName: NULL ## altExpNames(1): scADT

5会话信息

sessionInfo ()

## R正在开发中(不稳定)(2022-10-25 r83175) ##平台:x86_64-pc-linux-gnu(64位)##运行在Ubuntu 22.04.1 LTS ## ##矩阵产品:默认## BLAS: /home/biocbuild/bbs-3.17-bioc/R/lib/libRblas。so ## LAPACK: /usr/lib/x86_64-linux-gnu/ LAPACK /liblapack.so.3.10.0 ## ## 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_phone = c# # [11] LC_MEASUREMENT=en_US。UTF-8 LC_IDENTIFICATION=C ## ##附加的基本包:## [1]stats4 stats graphics grDevices utils datasets methods ##[8]基础## ##其他附加包:[1] SingleCellMultiModal_1.11.0 MultiAssayExperiment_1.25.1 ## [3] SummarizedExperiment_1.29.0 Biobase_2.59.0 ## [5] genomicranges_1.1.1.0 GenomeInfoDb_1.35.0 ## [7] IRanges_2.33.0 S4Vectors_0.37.0 ## [9] BiocGenerics_0.45.0 MatrixGenerics_1.11.0 ## [11] matrixStats_0.62.0 BiocStyle_2.27.0 ## ##通过命名空间加载(并且没有附加):[1] [3] formatr_1 .1.3 bitops_1.0-7 ## [5] magrittr_2.0.3 compiler_4.3.0 ## [9] png_0.1-7 vctrs_0.5.0 ## [11] string_1 .4.1 pkgconfig_2.0.3 ## [13] SpatialExperiment_1.9.0 crayon_1.5.2 ## [15] fastmap_1.1.0 magick_2.7.3 ## [17] dbplyr_2.2.1 XVector_0.39.0 ## [23] rmarkdown_2. 2.2 promises_1.2.0.1 ## [27] zlibbioc_1.45.0 ## [27] zlibbioc_1. 3.5 ## [25] bit_4.0.4 xfun_0.34 ## [27] zlibbioc_1. 0 ##cachem_1.0.6 # # [29] beachmat_2.15.0 jsonlite_1.8.3 # # [31] blob_1.2.3 later_1.3.0 # # [33] rhdf5filters_1.11.0 DelayedArray_0.25.0 # # [35] Rhdf5lib_1.21.0 BiocParallel_1.33.0 # # [37] interactiveDisplayBase_1.37.0 parallel_4.3.0 # # [39] R6_2.5.1 bslib_0.4.0 # # [41] stringi_1.7.8 limma_3.55.0 # # [43] jquerylib_0.1.4 Rcpp_1.0.9 # # [45] bookdown_0.29 assertthat_0.2.1 # # [47] knitr_1.40 R.utils_2.12.1 # # [49] httpuv_1.6.6 Matrix_1.5-1 # # [51] tidyselect_1.2.0 yaml_2.3.6 # # [53] codetools_0.2-18curl_4.3.3 # # [55] lattice_0.20-45 tibble_3.1.8 # # [57] withr_2.5.0 shiny_1.7.3 # # [59] KEGGREST_1.39.0 evaluate_0.17 # # [61] BiocFileCache_2.7.0 ExperimentHub_2.7.0 # # [63] Biostrings_2.67.0 pillar_1.8.1 # # [65] BiocManager_1.30.19 filelock_1.0.2 # # [67] generics_0.1.3 rcurl_1.98 - 1.9 # # [69] BiocVersion_3.17.0 sparseMatrixStats_1.11.0 # # [71] xtable_1.8-4 glue_1.6.2 # # [73] tools_4.3.0 AnnotationHub_3.7.0 # # [75] locfit_1.5 - 9.6 rhdf5_2.43.0 # # [77] grid_4.3.0 DropletUtils_1.19.0 # # [79]AnnotationDbi_1.61.0 edgeR_3.41.0 ## [81] singlecelexperiment_1 .21.0 GenomeInfoDbData_1.2.9 ## [83] HDF5Array_1.27.0 cli_3.4.1 ## [85] rappdirs_0.3.3 fansi_1.0.3 ## [87] dplyr_1.0.10 r.d astss3_1 .8.2 ## [89] sass_0.4.2 digest_0.6.30 ## [91] dqrng_0.3.0 rjson_0.2.21 ## [93] memoise_2.0.1 htmltools_0.5.3 ## [95] R.oo_1.25.0 lifecycle_1.0.3 ## [97] httr_1.4.4 mime_0.12 ## [99] bit64_4.0.5