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(): 2021-10-18## 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 4.1.1 (2021-08-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.3 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.14-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.14-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_1.6.0 MultiAssayExperiment_1.20.0
## [3] SummarizedExperiment_1.24.0 Biobase_2.54.0
## [5] GenomicRanges_1.46.0 GenomeInfoDb_1.30.0
## [7] IRanges_2.28.0 S4Vectors_0.32.0
## [9] BiocGenerics_0.40.0 MatrixGenerics_1.6.0
## [11] matrixStats_0.61.0 BiocStyle_2.22.0
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-7 bit64_4.0.5
## [3] filelock_1.0.2 httr_1.4.2
## [5] tools_4.1.1 bslib_0.3.1
## [7] utf8_1.2.2 R6_2.5.1
## [9] HDF5Array_1.22.0 DBI_1.1.1
## [11] rhdf5filters_1.6.0 withr_2.4.2
## [13] tidyselect_1.1.1 bit_4.0.4
## [15] curl_4.3.2 compiler_4.1.1
## [17] formatR_1.11 DelayedArray_0.20.0
## [19] bookdown_0.24 sass_0.4.0
## [21] rappdirs_0.3.3 stringr_1.4.0
## [23] digest_0.6.28 SpatialExperiment_1.4.0
## [25] rmarkdown_2.11 R.utils_2.11.0
## [27] XVector_0.34.0 pkgconfig_2.0.3
## [29] htmltools_0.5.2 sparseMatrixStats_1.6.0
## [31] limma_3.50.0 dbplyr_2.1.1
## [33] fastmap_1.1.0 rlang_0.4.12
## [35] RSQLite_2.2.8 shiny_1.7.1
## [37] DelayedMatrixStats_1.16.0 jquerylib_0.1.4
## [39] generics_0.1.1 jsonlite_1.7.2
## [41] BiocParallel_1.28.0 R.oo_1.24.0
## [43] dplyr_1.0.7 RCurl_1.98-1.5
## [45] magrittr_2.0.1 scuttle_1.4.0
## [47] GenomeInfoDbData_1.2.7 Matrix_1.3-4
## [49] Rcpp_1.0.7 Rhdf5lib_1.16.0
## [51] fansi_0.5.0 lifecycle_1.0.1
## [53] R.methodsS3_1.8.1 edgeR_3.36.0
## [55] stringi_1.7.5 yaml_2.2.1
## [57] zlibbioc_1.40.0 rhdf5_2.38.0
## [59] BiocFileCache_2.2.0 AnnotationHub_3.2.0
## [61] grid_4.1.1 blob_1.2.2
## [63] dqrng_0.3.0 parallel_4.1.1
## [65] promises_1.2.0.1 ExperimentHub_2.2.0
## [67] crayon_1.4.1 lattice_0.20-45
## [69] beachmat_2.10.0 Biostrings_2.62.0
## [71] KEGGREST_1.34.0 magick_2.7.3
## [73] locfit_1.5-9.4 knitr_1.36
## [75] pillar_1.6.4 rjson_0.2.20
## [77] glue_1.4.2 BiocVersion_3.14.0
## [79] evaluate_0.14 BiocManager_1.30.16
## [81] vctrs_0.3.8 png_0.1-7
## [83] httpuv_1.6.3 purrr_0.3.4
## [85] assertthat_0.2.1 cachem_1.0.6
## [87] xfun_0.27 DropletUtils_1.14.0
## [89] mime_0.12 xtable_1.8-4
## [91] later_1.3.0 SingleCellExperiment_1.16.0
## [93] tibble_3.1.5 AnnotationDbi_1.56.1
## [95] memoise_2.0.0 ellipsis_0.3.2
## [97] interactiveDisplayBase_1.32.0Stoeckius, 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.