1 Installation

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

BiocManager::install("SingleCellMultiModal")

2 Load libraries

library(MultiAssayExperiment)
library(SingleCellMultiModal)
library(SingleCellExperiment)

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
##    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:

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 types
mae
## A MultiAssayExperiment object of 3 listed
##  experiments with user-defined names and respective classes.
##  Containing an ExperimentList class object of length 3:
##  [1] scADT: matrix with 13 rows and 7858 columns
##  [2] scADT_clr: 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 files

Example with actual data:

experiments(mae)
## ExperimentList class object of length 3:
##  [1] scADT: matrix with 13 rows and 7858 columns
##  [2] scADT_clr: matrix with 13 rows and 7858 columns
##  [3] scRNAseq: matrix with 36280 rows and 7858 columns

3.2 Exploring the data structure

Check row annotations:

rownames(mae)
## CharacterList of length 3
## [["scADT"]] CD3 CD4 CD8 CD45RA CD56 CD16 CD10 CD11c CD14 CD19 CD34 CCR5 CCR7
## [["scADT_clr"]] 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 23574 rows and 3 columns
##          assay          primary          colname
##       <factor>      <character>      <character>
## 1        scADT TACAGTGTCTCGGACG TACAGTGTCTCGGACG
## 2        scADT GTTTCTACATCATCCC GTTTCTACATCATCCC
## 3        scADT GTACGTATCCCATTTA GTACGTATCCCATTTA
## 4        scADT ATGTGTGGTCGCCATG ATGTGTGGTCGCCATG
## 5        scADT AACGTTGTCAGTTAGC AACGTTGTCAGTTAGC
## ...        ...              ...              ...
## 23570 scRNAseq AGCGTCGAGTCAAGGC AGCGTCGAGTCAAGGC
## 23571 scRNAseq GTCGGGTAGTAGCCGA GTCGGGTAGTAGCCGA
## 23572 scRNAseq GTCGGGTAGTTCGCAT GTCGGGTAGTTCGCAT
## 23573 scRNAseq TTGCCGTGTAGATTAG TTGCCGTGTAGATTAG
## 23574 scRNAseq GGCGTGTAGTGTACTC GGCGTGTAGTGTACTC

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]
##                 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                  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]
##        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               59

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")
## Warning: 'ExperimentList' contains 'data.frame' or 'DataFrame',
##   potential for errors with mixed data types
sce
## 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

5 Session Info

sessionInfo()
## R version 4.4.0 beta (2024-04-15 r86425)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.19-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] SingleCellExperiment_1.26.0 SingleCellMultiModal_1.16.0
##  [3] MultiAssayExperiment_1.30.0 SummarizedExperiment_1.34.0
##  [5] Biobase_2.64.0              GenomicRanges_1.56.0       
##  [7] GenomeInfoDb_1.40.0         IRanges_2.38.0             
##  [9] S4Vectors_0.42.0            BiocGenerics_0.50.0        
## [11] MatrixGenerics_1.16.0       matrixStats_1.3.0          
## [13] BiocStyle_2.32.0           
## 
## loaded via a namespace (and not attached):
##  [1] KEGGREST_1.44.0          rjson_0.2.21             xfun_0.43               
##  [4] bslib_0.7.0              lattice_0.22-6           vctrs_0.6.5             
##  [7] tools_4.4.0              generics_0.1.3           curl_5.2.1              
## [10] AnnotationDbi_1.66.0     tibble_3.2.1             fansi_1.0.6             
## [13] RSQLite_2.3.6            blob_1.2.4               BiocBaseUtils_1.6.0     
## [16] pkgconfig_2.0.3          Matrix_1.7-0             dbplyr_2.5.0            
## [19] lifecycle_1.0.4          GenomeInfoDbData_1.2.12  compiler_4.4.0          
## [22] Biostrings_2.72.0        htmltools_0.5.8.1        sass_0.4.9              
## [25] yaml_2.3.8               pillar_1.9.0             crayon_1.5.2            
## [28] jquerylib_0.1.4          DelayedArray_0.30.0      cachem_1.0.8            
## [31] magick_2.8.3             abind_1.4-5              mime_0.12               
## [34] ExperimentHub_2.12.0     AnnotationHub_3.12.0     tidyselect_1.2.1        
## [37] digest_0.6.35            purrr_1.0.2              dplyr_1.1.4             
## [40] bookdown_0.39            BiocVersion_3.19.1       fastmap_1.1.1           
## [43] grid_4.4.0               cli_3.6.2                SparseArray_1.4.0       
## [46] magrittr_2.0.3           S4Arrays_1.4.0           utf8_1.2.4              
## [49] withr_3.0.0              rappdirs_0.3.3           filelock_1.0.3          
## [52] UCSC.utils_1.0.0         bit64_4.0.5              rmarkdown_2.26          
## [55] XVector_0.44.0           httr_1.4.7               bit_4.0.5               
## [58] png_0.1-8                SpatialExperiment_1.14.0 memoise_2.0.1           
## [61] evaluate_0.23            knitr_1.46               BiocFileCache_2.12.0    
## [64] rlang_1.1.3              Rcpp_1.0.12              glue_1.7.0              
## [67] DBI_1.2.2                formatR_1.14             BiocManager_1.30.22     
## [70] jsonlite_1.8.8           R6_2.5.1                 zlibbioc_1.50.0

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.