if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("SingleCellMultiModal")library(MultiAssayExperiment)
library(SingleCellMultiModal)CITE-seq data are a combination of two data types extracted at the same time from the same cell. First data type is scRNA-seq data, while the second one consists of about a hundread of antibody-derived tags (ADT). In particular this dataset is provided by Stoeckius et al. (2017).
The user can see the available dataset by using the default options
CITEseq(DataType="cord_blood", modes="*", dry.run=TRUE, version="1.0.0")## Dataset: cord_blood## snapshotDate(): 2022-10-31## ah_id mode file_size rdataclass rdatadateadded rdatadateremoved
## 1 EH3795 scADT_Counts 0.2 Mb matrix 2020-09-23 <NA>
## 2 EH3796 scRNAseq_Counts 22.2 Mb matrix 2020-09-23 <NA>Or simply by setting dry.run = FALSE it downloads the data and creates the
MultiAssayExperiment object.
In this example, we will use one of the two available datasets scADT_Counts:
mae <- CITEseq(
DataType="cord_blood", modes="*", dry.run=FALSE, version="1.0.0"
)
mae## A MultiAssayExperiment object of 2 listed
## experiments with user-defined names and respective classes.
## Containing an ExperimentList class object of length 2:
## [1] scADT: matrix with 13 rows and 8617 columns
## [2] scRNAseq: matrix with 36280 rows and 8617 columns
## Functionality:
## experiments() - obtain the ExperimentList instance
## colData() - the primary/phenotype DataFrame
## sampleMap() - the sample coordination DataFrame
## `$`, `[`, `[[` - extract colData columns, subset, or experiment
## *Format() - convert into a long or wide DataFrame
## assays() - convert ExperimentList to a SimpleList of matrices
## exportClass() - save data to flat filesExample with actual data:
experiments(mae)## ExperimentList class object of length 2:
## [1] scADT: matrix with 13 rows and 8617 columns
## [2] scRNAseq: matrix with 36280 rows and 8617 columnsCheck row annotations:
rownames(mae)## CharacterList of length 2
## [["scADT"]] CD3 CD4 CD8 CD45RA CD56 CD16 CD10 CD11c CD14 CD19 CD34 CCR5 CCR7
## [["scRNAseq"]] ERCC_ERCC-00104 HUMAN_A1BG ... MOUSE_n-R5s25 MOUSE_n-R5s31Take a peek at the sampleMap:
sampleMap(mae)## DataFrame with 17234 rows and 3 columns
## assay primary colname
## <factor> <character> <character>
## 1 scADT CTGTTTACACCGCTAG CTGTTTACACCGCTAG
## 2 scADT CTCTACGGTGTGGCTC CTCTACGGTGTGGCTC
## 3 scADT AGCAGCCAGGCTCATT AGCAGCCAGGCTCATT
## 4 scADT GAATAAGAGATCCCAT GAATAAGAGATCCCAT
## 5 scADT GTGCATAGTCATGCAT GTGCATAGTCATGCAT
## ... ... ... ...
## 17230 scRNAseq TTGCCGTGTAGATTAG TTGCCGTGTAGATTAG
## 17231 scRNAseq GGCGTGTAGTGTACTC GGCGTGTAGTGTACTC
## 17232 scRNAseq CGTATGCCGTCTTCTG CGTATGCCGTCTTCTG
## 17233 scRNAseq TACACGACGCTCTTCC TACACGACGCTCTTCC
## 17234 scRNAseq ACACGACGCTCTTCCG ACACGACGCTCTTCCGThe scRNA-seq data are accessible with the name scRNAseq, which returns a
matrix object.
head(experiments(mae)$scRNAseq)[, 1:4]## CTGTTTACACCGCTAG CTCTACGGTGTGGCTC AGCAGCCAGGCTCATT
## ERCC_ERCC-00104 0 0 0
## HUMAN_A1BG 0 0 0
## HUMAN_A1BG-AS1 0 0 0
## HUMAN_A1CF 0 0 0
## HUMAN_A2M 0 0 0
## HUMAN_A2M-AS1 0 0 0
## GAATAAGAGATCCCAT
## ERCC_ERCC-00104 0
## HUMAN_A1BG 0
## HUMAN_A1BG-AS1 0
## HUMAN_A1CF 0
## HUMAN_A2M 0
## HUMAN_A2M-AS1 0The scADT data are accessible with the name scADT, which returns a
matrix object.
head(experiments(mae)$scADT)[, 1:4]## CTGTTTACACCGCTAG CTCTACGGTGTGGCTC AGCAGCCAGGCTCATT GAATAAGAGATCCCAT
## 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 82Because of already large use of some methodologies (such as
in the SingleCellExperiment vignette or CiteFuse Vignette where the
SingleCellExperiment object is used for CITE-seq data,
we provide a function for the conversion of our CITE-seq MultiAssayExperiment
object into a SingleCellExperiment object with scRNA-seq data as counts and
scADT data as altExps.
sce <- CITEseq(DataType="cord_blood", modes="*", dry.run=FALSE, version="1.0.0",
DataClass="SingleCellExperiment")
sce## class: SingleCellExperiment
## dim: 36280 8617
## metadata(0):
## assays(1): counts
## rownames(36280): ERCC_ERCC-00104 HUMAN_A1BG ... MOUSE_n-R5s25
## MOUSE_n-R5s31
## rowData names(0):
## colnames(8617): CTGTTTACACCGCTAG CTCTACGGTGTGGCTC ... TACACGACGCTCTTCC
## ACACGACGCTCTTCCG
## colData names(0):
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(1): scADTsessionInfo()## R version 4.2.3 (2023-03-15)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.6 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.16-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.16-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
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] SingleCellMultiModal_1.10.1 MultiAssayExperiment_1.24.0
## [3] SummarizedExperiment_1.28.0 Biobase_2.58.0
## [5] GenomicRanges_1.50.2 GenomeInfoDb_1.34.9
## [7] IRanges_2.32.0 S4Vectors_0.36.2
## [9] BiocGenerics_0.44.0 MatrixGenerics_1.10.0
## [11] matrixStats_0.63.0 BiocStyle_2.26.0
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-7 bit64_4.0.5
## [3] filelock_1.0.2 httr_1.4.5
## [5] tools_4.2.3 bslib_0.4.2
## [7] utf8_1.2.3 R6_2.5.1
## [9] HDF5Array_1.26.0 DBI_1.1.3
## [11] rhdf5filters_1.10.1 withr_2.5.0
## [13] tidyselect_1.2.0 bit_4.0.5
## [15] curl_5.0.0 compiler_4.2.3
## [17] cli_3.6.1 formatR_1.14
## [19] DelayedArray_0.24.0 bookdown_0.33
## [21] sass_0.4.5 rappdirs_0.3.3
## [23] digest_0.6.31 SpatialExperiment_1.8.1
## [25] rmarkdown_2.21 R.utils_2.12.2
## [27] XVector_0.38.0 pkgconfig_2.0.3
## [29] htmltools_0.5.5 sparseMatrixStats_1.10.0
## [31] limma_3.54.2 dbplyr_2.3.2
## [33] fastmap_1.1.1 rlang_1.1.0
## [35] RSQLite_2.3.1 shiny_1.7.4
## [37] DelayedMatrixStats_1.20.0 jquerylib_0.1.4
## [39] generics_0.1.3 jsonlite_1.8.4
## [41] BiocParallel_1.32.6 dplyr_1.1.1
## [43] R.oo_1.25.0 RCurl_1.98-1.12
## [45] magrittr_2.0.3 scuttle_1.8.4
## [47] GenomeInfoDbData_1.2.9 Matrix_1.5-4
## [49] Rcpp_1.0.10 Rhdf5lib_1.20.0
## [51] fansi_1.0.4 lifecycle_1.0.3
## [53] R.methodsS3_1.8.2 edgeR_3.40.2
## [55] yaml_2.3.7 zlibbioc_1.44.0
## [57] rhdf5_2.42.1 BiocFileCache_2.6.1
## [59] AnnotationHub_3.6.0 grid_4.2.3
## [61] blob_1.2.4 dqrng_0.3.0
## [63] parallel_4.2.3 promises_1.2.0.1
## [65] ExperimentHub_2.6.0 crayon_1.5.2
## [67] lattice_0.21-8 Biostrings_2.66.0
## [69] beachmat_2.14.2 KEGGREST_1.38.0
## [71] magick_2.7.4 locfit_1.5-9.7
## [73] knitr_1.42 pillar_1.9.0
## [75] rjson_0.2.21 codetools_0.2-19
## [77] glue_1.6.2 BiocVersion_3.16.0
## [79] evaluate_0.20 BiocManager_1.30.20
## [81] png_0.1-8 vctrs_0.6.1
## [83] httpuv_1.6.9 purrr_1.0.1
## [85] cachem_1.0.7 xfun_0.38
## [87] DropletUtils_1.18.1 mime_0.12
## [89] xtable_1.8-4 later_1.3.0
## [91] SingleCellExperiment_1.20.1 tibble_3.2.1
## [93] AnnotationDbi_1.60.2 memoise_2.0.1
## [95] ellipsis_0.3.2 interactiveDisplayBase_1.36.0Stoeckius, Marlon, Christoph Hafemeister, William Stephenson, Brian Houck-Loomis, Pratip K Chattopadhyay, Harold Swerdlow, Rahul Satija, and Peter Smibert. 2017. “Simultaneous Epitope and Transcriptome Measurement in Single Cells.” Nature Methods 14 (9): 865.