1 Installation

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("SingleCellMultiModal")

2 Load libraries

library(MultiAssayExperiment)
library(SingleCellMultiModal)

3 CITE-seq dataset

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).

3.1 Downloading datasets

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(): 2022-04-19
##    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 files

Example 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 columns

3.2 Exploring the data structure

Check 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-R5s31

Take 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 ACACGACGCTCTTCCG

3.3 scRNA-seq data

The 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                  0

3.4 scADT data

The 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               82

4 SingleCellExperiment object conversion

Because 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): scADT

5 Session Info

sessionInfo()
## R version 4.2.0 RC (2022-04-21 r82226)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 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_1.9.0  MultiAssayExperiment_1.23.0
##  [3] SummarizedExperiment_1.27.0 Biobase_2.57.0             
##  [5] GenomicRanges_1.49.0        GenomeInfoDb_1.33.0        
##  [7] IRanges_2.31.0              S4Vectors_0.35.0           
##  [9] BiocGenerics_0.43.0         MatrixGenerics_1.9.0       
## [11] matrixStats_0.62.0          BiocStyle_2.25.0           
## 
## 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_4.2.0                   bslib_0.3.1                  
##  [7] utf8_1.2.2                    R6_2.5.1                     
##  [9] HDF5Array_1.25.0              DBI_1.1.2                    
## [11] rhdf5filters_1.9.0            withr_2.5.0                  
## [13] tidyselect_1.1.2              bit_4.0.4                    
## [15] curl_4.3.2                    compiler_4.2.0               
## [17] cli_3.3.0                     formatR_1.12                 
## [19] DelayedArray_0.23.0           bookdown_0.26                
## [21] sass_0.4.1                    rappdirs_0.3.3               
## [23] stringr_1.4.0                 digest_0.6.29                
## [25] SpatialExperiment_1.7.0       R.utils_2.11.0               
## [27] rmarkdown_2.14                XVector_0.37.0               
## [29] pkgconfig_2.0.3               htmltools_0.5.2              
## [31] sparseMatrixStats_1.9.0       limma_3.53.0                 
## [33] dbplyr_2.1.1                  fastmap_1.1.0                
## [35] rlang_1.0.2                   RSQLite_2.2.12               
## [37] shiny_1.7.1                   DelayedMatrixStats_1.19.0    
## [39] jquerylib_0.1.4               generics_0.1.2               
## [41] jsonlite_1.8.0                BiocParallel_1.31.0          
## [43] R.oo_1.24.0                   dplyr_1.0.8                  
## [45] RCurl_1.98-1.6                magrittr_2.0.3               
## [47] scuttle_1.7.0                 GenomeInfoDbData_1.2.8       
## [49] Matrix_1.4-1                  Rcpp_1.0.8.3                 
## [51] Rhdf5lib_1.19.0               fansi_1.0.3                  
## [53] R.methodsS3_1.8.1             lifecycle_1.0.1              
## [55] edgeR_3.39.0                  stringi_1.7.6                
## [57] yaml_2.3.5                    zlibbioc_1.43.0              
## [59] rhdf5_2.41.0                  BiocFileCache_2.5.0          
## [61] AnnotationHub_3.5.0           grid_4.2.0                   
## [63] blob_1.2.3                    dqrng_0.3.0                  
## [65] parallel_4.2.0                promises_1.2.0.1             
## [67] ExperimentHub_2.5.0           crayon_1.5.1                 
## [69] lattice_0.20-45               beachmat_2.13.0              
## [71] Biostrings_2.65.0             KEGGREST_1.37.0              
## [73] magick_2.7.3                  locfit_1.5-9.5               
## [75] knitr_1.38                    pillar_1.7.0                 
## [77] rjson_0.2.21                  glue_1.6.2                   
## [79] BiocVersion_3.16.0            evaluate_0.15                
## [81] BiocManager_1.30.17           vctrs_0.4.1                  
## [83] png_0.1-7                     httpuv_1.6.5                 
## [85] purrr_0.3.4                   assertthat_0.2.1             
## [87] cachem_1.0.6                  xfun_0.30                    
## [89] DropletUtils_1.17.0           mime_0.12                    
## [91] xtable_1.8-4                  later_1.3.0                  
## [93] SingleCellExperiment_1.19.0   tibble_3.1.6                 
## [95] AnnotationDbi_1.59.0          memoise_2.0.1                
## [97] ellipsis_0.3.2                interactiveDisplayBase_1.35.0

References

Stoeckius, 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.