CITEseq Cord Blood

Installation

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

BiocManager::install("SingleCellMultiModal")

Load libraries

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

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).

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

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

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

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

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

Session Info

sessionInfo()
## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 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=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: Etc/UTC
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] SingleCellExperiment_1.28.0 SingleCellMultiModal_1.19.0
##  [3] MultiAssayExperiment_1.33.0 SummarizedExperiment_1.36.0
##  [5] Biobase_2.67.0              GenomicRanges_1.59.0       
##  [7] GenomeInfoDb_1.43.0         IRanges_2.41.0             
##  [9] S4Vectors_0.44.0            BiocGenerics_0.53.1        
## [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_0.48               
##  [4] bslib_0.8.0              lattice_0.22-6           vctrs_0.6.5             
##  [7] tools_4.4.1              curl_5.2.3               AnnotationDbi_1.69.0    
## [10] tibble_3.2.1             fansi_1.0.6              RSQLite_2.3.7           
## [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_4.4.1           Biostrings_2.75.0       
## [22] htmltools_0.5.8.1        sys_3.4.3                buildtools_1.0.0        
## [25] sass_0.4.9               yaml_2.3.10              pillar_1.9.0            
## [28] crayon_1.5.3             jquerylib_0.1.4          DelayedArray_0.33.1     
## [31] cachem_1.1.0             magick_2.8.5             abind_1.4-8             
## [34] mime_0.12                ExperimentHub_2.15.0     AnnotationHub_3.15.0    
## [37] tidyselect_1.2.1         digest_0.6.37            purrr_1.0.2             
## [40] dplyr_1.1.4              BiocVersion_3.21.1       maketools_1.3.1         
## [43] fastmap_1.2.0            grid_4.4.1               cli_3.6.3               
## [46] SparseArray_1.6.0        magrittr_2.0.3           S4Arrays_1.6.0          
## [49] utf8_1.2.4               withr_3.0.2              rappdirs_0.3.3          
## [52] filelock_1.0.3           UCSC.utils_1.2.0         bit64_4.5.2             
## [55] rmarkdown_2.28           XVector_0.46.0           httr_1.4.7              
## [58] bit_4.5.0                png_0.1-8                SpatialExperiment_1.16.0
## [61] memoise_2.0.1            evaluate_1.0.1           knitr_1.48              
## [64] BiocFileCache_2.15.0     rlang_1.1.4              Rcpp_1.0.13             
## [67] glue_1.8.0               DBI_1.2.3                formatR_1.14            
## [70] BiocManager_1.30.25      jsonlite_1.8.9           R6_2.5.1                
## [73] zlibbioc_1.52.0

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.