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
## 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 0.1 Mb data.frame 2023-05-17 <NA>
## 4 EH8305 scADT_clrCounts 0.8 Mb matrix 2023-07-05 <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:
## Warning: 'ExperimentList' contains 'data.frame' or 'DataFrame',
## potential for errors with mixed data types## A MultiAssayExperiment object of 3 listed
## experiments with user-defined names and respective classes.
## Containing an ExperimentList class object of length 3:
## [1] scADT_clr: matrix with 13 rows and 7858 columns
## [2] scADT: matrix with 13 rows and 7858 columns
## [3] scRNAseq: matrix with 36280 rows and 7858 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:
## ExperimentList class object of length 3:
## [1] scADT_clr: matrix with 13 rows and 7858 columns
## [2] scADT: matrix with 13 rows and 7858 columns
## [3] scRNAseq: matrix with 36280 rows and 7858 columnsCheck row annotations:
## CharacterList of length 3
## [["scADT_clr"]] CD3 CD4 CD8 CD45RA CD56 CD16 CD10 CD11c CD14 CD19 CD34 CCR5 CCR7
## [["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:
## DataFrame with 23574 rows and 3 columns
## assay primary colname
## <factor> <character> <character>
## 1 scADT_clr TACAGTGTCTCGGACG TACAGTGTCTCGGACG
## 2 scADT_clr GTTTCTACATCATCCC GTTTCTACATCATCCC
## 3 scADT_clr GTACGTATCCCATTTA GTACGTATCCCATTTA
## 4 scADT_clr ATGTGTGGTCGCCATG ATGTGTGGTCGCCATG
## 5 scADT_clr AACGTTGTCAGTTAGC AACGTTGTCAGTTAGC
## ... ... ... ...
## 23570 scRNAseq AGCGTCGAGTCAAGGC AGCGTCGAGTCAAGGC
## 23571 scRNAseq GTCGGGTAGTAGCCGA GTCGGGTAGTAGCCGA
## 23572 scRNAseq GTCGGGTAGTTCGCAT GTCGGGTAGTTCGCAT
## 23573 scRNAseq TTGCCGTGTAGATTAG TTGCCGTGTAGATTAG
## 23574 scRNAseq GGCGTGTAGTGTACTC GGCGTGTAGTGTACTCThe scRNA-seq data are accessible with the name
scRNAseq, which returns a matrix object.
## TACAGTGTCTCGGACG GTTTCTACATCATCCC GTACGTATCCCATTTA
## 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
## ATGTGTGGTCGCCATG
## 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.
## TACAGTGTCTCGGACG GTTTCTACATCATCCC GTACGTATCCCATTTA ATGTGTGGTCGCCATG
## CD3 36 34 49 35
## CD4 28 21 38 29
## CD8 34 41 52 47
## CD45RA 228 228 300 303
## CD56 26 18 48 36
## CD16 44 38 51 59Because 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 types## class: SingleCellExperiment
## dim: 36280 7858
## metadata(0):
## assays(1): counts
## rownames(36280): ERCC_ERCC-00104 HUMAN_A1BG ... MOUSE_n-R5s25
## MOUSE_n-R5s31
## rowData names(0):
## colnames(7858): TACAGTGTCTCGGACG GTTTCTACATCATCCC ... TTGCCGTGTAGATTAG
## GGCGTGTAGTGTACTC
## colData names(6): adt.discard mito.discard ... celltype markers
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(1): scADT## R version 4.6.0 (2026-04-24)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.4 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [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: Etc/UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] SingleCellExperiment_1.35.1 SingleCellMultiModal_1.25.3
## [3] MultiAssayExperiment_1.39.0 SummarizedExperiment_1.43.0
## [5] Biobase_2.73.1 GenomicRanges_1.65.0
## [7] Seqinfo_1.3.0 IRanges_2.47.1
## [9] S4Vectors_0.51.2 BiocGenerics_0.59.2
## [11] generics_0.1.4 MatrixGenerics_1.25.0
## [13] matrixStats_1.5.0 BiocStyle_2.41.0
##
## loaded via a namespace (and not attached):
## [1] KEGGREST_1.53.0 rjson_0.2.23 xfun_0.57
## [4] bslib_0.11.0 httr2_1.2.2 lattice_0.22-9
## [7] vctrs_0.7.3 tools_4.6.0 curl_7.1.0
## [10] tibble_3.3.1 AnnotationDbi_1.75.0 RSQLite_3.52.0
## [13] blob_1.3.0 BiocBaseUtils_1.15.1 pkgconfig_2.0.3
## [16] Matrix_1.7-5 dbplyr_2.5.2 lifecycle_1.0.5
## [19] compiler_4.6.0 Biostrings_2.81.1 htmltools_0.5.9
## [22] sys_3.4.3 buildtools_1.0.0 sass_0.4.10
## [25] yaml_2.3.12 pillar_1.11.1 crayon_1.5.3
## [28] jquerylib_0.1.4 DelayedArray_0.39.2 cachem_1.1.0
## [31] magick_2.9.1 abind_1.4-8 ExperimentHub_3.3.0
## [34] AnnotationHub_4.3.0 tidyselect_1.2.1 digest_0.6.39
## [37] purrr_1.2.2 dplyr_1.2.1 BiocVersion_3.24.0
## [40] maketools_1.3.2 fastmap_1.2.0 grid_4.6.0
## [43] cli_3.6.6 SparseArray_1.13.2 magrittr_2.0.5
## [46] S4Arrays_1.13.0 withr_3.0.2 filelock_1.0.3
## [49] rappdirs_0.3.4 bit64_4.8.2 rmarkdown_2.31
## [52] XVector_0.53.0 httr_1.4.8 bit_4.6.0
## [55] png_0.1-9 SpatialExperiment_1.23.0 memoise_2.0.1
## [58] evaluate_1.0.5 knitr_1.51 BiocFileCache_3.3.0
## [61] rlang_1.2.0 Rcpp_1.1.1-1.1 glue_1.8.1
## [64] DBI_1.3.0 formatR_1.14 BiocManager_1.30.27
## [67] jsonlite_2.0.0 R6_2.6.1