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 Under development (unstable) (2024-10-21 r87258)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0
## 
## 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_1.29.1 SingleCellMultiModal_1.19.1
##  [3] MultiAssayExperiment_1.33.1 SummarizedExperiment_1.37.0
##  [5] Biobase_2.67.0              GenomicRanges_1.59.1       
##  [7] GenomeInfoDb_1.43.1         IRanges_2.41.1             
##  [9] S4Vectors_0.45.2            BiocGenerics_0.53.3        
## [11] generics_0.1.3              MatrixGenerics_1.19.0      
## [13] matrixStats_1.4.1           BiocStyle_2.35.0           
## 
## loaded via a namespace (and not attached):
##  [1] KEGGREST_1.47.0          rjson_0.2.23             xfun_0.49               
##  [4] bslib_0.8.0              lattice_0.22-6           vctrs_0.6.5             
##  [7] tools_4.5.0              curl_6.0.1               AnnotationDbi_1.69.0    
## [10] tibble_3.2.1             fansi_1.0.6              RSQLite_2.3.8           
## [13] blob_1.2.4               BiocBaseUtils_1.9.0      pkgconfig_2.0.3         
## [16] Matrix_1.7-1             dbplyr_2.5.0             lifecycle_1.0.4         
## [19] GenomeInfoDbData_1.2.13  compiler_4.5.0           Biostrings_2.75.1       
## [22] htmltools_0.5.8.1        sass_0.4.9               yaml_2.3.10             
## [25] pillar_1.9.0             crayon_1.5.3             jquerylib_0.1.4         
## [28] DelayedArray_0.33.2      cachem_1.1.0             magick_2.8.5            
## [31] abind_1.4-8              mime_0.12                ExperimentHub_2.15.0    
## [34] AnnotationHub_3.15.0     tidyselect_1.2.1         digest_0.6.37           
## [37] purrr_1.0.2              dplyr_1.1.4              bookdown_0.41           
## [40] BiocVersion_3.21.1       fastmap_1.2.0            grid_4.5.0              
## [43] cli_3.6.3                SparseArray_1.7.2        magrittr_2.0.3          
## [46] S4Arrays_1.7.1           utf8_1.2.4               withr_3.0.2             
## [49] filelock_1.0.3           UCSC.utils_1.3.0         rappdirs_0.3.3          
## [52] bit64_4.5.2              rmarkdown_2.29           XVector_0.47.0          
## [55] httr_1.4.7               bit_4.5.0                png_0.1-8               
## [58] SpatialExperiment_1.17.0 memoise_2.0.1            evaluate_1.0.1          
## [61] knitr_1.49               BiocFileCache_2.15.0     rlang_1.1.4             
## [64] Rcpp_1.0.13-1            glue_1.8.0               DBI_1.2.3               
## [67] formatR_1.14             BiocManager_1.30.25      jsonlite_1.8.9          
## [70] R6_2.5.1                 zlibbioc_1.53.0

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.

CITEseq Cord Blood

21 November, 2024

Package