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## 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>
## 3 EH8228 coldata_scRNAseq 0 Mb data.frame 2023-05-17 <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"
)## Warning: 'ExperimentList' contains 'data.frame' or 'DataFrame',
## potential for errors with mixed data typesmae## 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")## Warning: 'ExperimentList' contains 'data.frame' or 'DataFrame',
## potential for errors with mixed data typessce## 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(2): sampleID cd
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(1): scADTsessionInfo()## R version 4.3.0 RC (2023-04-18 r84287)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.2 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.18-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_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] SingleCellMultiModal_1.13.8 MultiAssayExperiment_1.27.0
## [3] SummarizedExperiment_1.31.1 Biobase_2.61.0
## [5] GenomicRanges_1.53.1 GenomeInfoDb_1.37.1
## [7] IRanges_2.35.1 S4Vectors_0.39.1
## [9] BiocGenerics_0.47.0 MatrixGenerics_1.13.0
## [11] matrixStats_0.63.0 BiocStyle_2.29.0
##
## loaded via a namespace (and not attached):
## [1] DBI_1.1.3 bitops_1.0-7
## [3] formatR_1.14 rlang_1.1.1
## [5] magrittr_2.0.3 compiler_4.3.0
## [7] RSQLite_2.3.1 DelayedMatrixStats_1.23.0
## [9] png_0.1-8 vctrs_0.6.2
## [11] pkgconfig_2.0.3 SpatialExperiment_1.11.0
## [13] crayon_1.5.2 fastmap_1.1.1
## [15] magick_2.7.4 dbplyr_2.3.2
## [17] XVector_0.41.1 ellipsis_0.3.2
## [19] scuttle_1.11.0 utf8_1.2.3
## [21] promises_1.2.0.1 rmarkdown_2.21
## [23] purrr_1.0.1 bit_4.0.5
## [25] xfun_0.39 zlibbioc_1.47.0
## [27] cachem_1.0.8 beachmat_2.17.2
## [29] jsonlite_1.8.4 blob_1.2.4
## [31] later_1.3.1 rhdf5filters_1.13.2
## [33] DelayedArray_0.27.4 Rhdf5lib_1.23.0
## [35] BiocParallel_1.35.2 interactiveDisplayBase_1.39.0
## [37] parallel_4.3.0 R6_2.5.1
## [39] bslib_0.4.2 limma_3.57.1
## [41] jquerylib_0.1.4 Rcpp_1.0.10
## [43] bookdown_0.34 knitr_1.42
## [45] R.utils_2.12.2 BiocBaseUtils_1.3.0
## [47] httpuv_1.6.11 Matrix_1.5-4.1
## [49] tidyselect_1.2.0 yaml_2.3.7
## [51] codetools_0.2-19 curl_5.0.0
## [53] lattice_0.21-8 tibble_3.2.1
## [55] withr_2.5.0 shiny_1.7.4
## [57] KEGGREST_1.41.0 evaluate_0.21
## [59] BiocFileCache_2.9.0 ExperimentHub_2.9.0
## [61] Biostrings_2.69.1 pillar_1.9.0
## [63] BiocManager_1.30.20 filelock_1.0.2
## [65] generics_0.1.3 RCurl_1.98-1.12
## [67] BiocVersion_3.18.0 sparseMatrixStats_1.13.0
## [69] xtable_1.8-4 glue_1.6.2
## [71] tools_4.3.0 AnnotationHub_3.9.1
## [73] locfit_1.5-9.7 rhdf5_2.45.0
## [75] grid_4.3.0 DropletUtils_1.21.0
## [77] AnnotationDbi_1.63.1 edgeR_3.43.2
## [79] SingleCellExperiment_1.23.0 GenomeInfoDbData_1.2.10
## [81] HDF5Array_1.29.3 cli_3.6.1
## [83] rappdirs_0.3.3 fansi_1.0.4
## [85] S4Arrays_1.1.4 dplyr_1.1.2
## [87] R.methodsS3_1.8.2 sass_0.4.6
## [89] digest_0.6.31 SparseArray_1.1.6
## [91] dqrng_0.3.0 rjson_0.2.21
## [93] memoise_2.0.1 htmltools_0.5.5
## [95] R.oo_1.25.0 lifecycle_1.0.3
## [97] httr_1.4.6 mime_0.12
## [99] bit64_4.0.5Stoeckius, 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.