SingleCellMultiModal 1.18.0
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("SingleCellMultiModal")library(MultiAssayExperiment)
library(SingleCellMultiModal)
library(SingleCellExperiment)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
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 typesmae## 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
## `$`, `[`, `[[` - extract colData columns, subset, or experiment
## *Format() - convert into a long or wide DataFrame
## assays() - convert ExperimentList to a SimpleList of matrices
## exportClass() - save data to flat filesExample with actual data:
experiments(mae)## ExperimentList class object of length 3:
## [1] scADT: matrix with 13 rows and 7858 columns
## [2] scADT_clr: matrix with 13 rows and 7858 columns
## [3] scRNAseq: matrix with 36280 rows and 7858 columnsCheck row annotations:
rownames(mae)## CharacterList of length 3
## [["scADT"]] CD3 CD4 CD8 CD45RA CD56 CD16 CD10 CD11c CD14 CD19 CD34 CCR5 CCR7
## [["scADT_clr"]] CD3 CD4 CD8 CD45RA CD56 CD16 CD10 CD11c CD14 CD19 CD34 CCR5 CCR7
## [["scRNAseq"]] ERCC_ERCC-00104 HUMAN_A1BG ... MOUSE_n-R5s25 MOUSE_n-R5s31Take a peek at the sampleMap:
sampleMap(mae)## DataFrame with 23574 rows and 3 columns
## assay primary colname
## <factor> <character> <character>
## 1 scADT TACAGTGTCTCGGACG TACAGTGTCTCGGACG
## 2 scADT GTTTCTACATCATCCC GTTTCTACATCATCCC
## 3 scADT GTACGTATCCCATTTA GTACGTATCCCATTTA
## 4 scADT ATGTGTGGTCGCCATG ATGTGTGGTCGCCATG
## 5 scADT AACGTTGTCAGTTAGC AACGTTGTCAGTTAGC
## ... ... ... ...
## 23570 scRNAseq AGCGTCGAGTCAAGGC AGCGTCGAGTCAAGGC
## 23571 scRNAseq GTCGGGTAGTAGCCGA GTCGGGTAGTAGCCGA
## 23572 scRNAseq GTCGGGTAGTTCGCAT GTCGGGTAGTTCGCAT
## 23573 scRNAseq TTGCCGTGTAGATTAG TTGCCGTGTAGATTAG
## 23574 scRNAseq GGCGTGTAGTGTACTC GGCGTGTAGTGTACTCThe scRNA-seq data are accessible with the name scRNAseq, which returns a
matrix object.
head(experiments(mae)$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 0The 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 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 typessce## 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): scADTsessionInfo()## 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: /home/biocbuild/bbs-3.20-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_1.28.0 SingleCellMultiModal_1.18.0
## [3] MultiAssayExperiment_1.32.0 SummarizedExperiment_1.36.0
## [5] Biobase_2.66.0 GenomicRanges_1.58.0
## [7] GenomeInfoDb_1.42.0 IRanges_2.40.0
## [9] S4Vectors_0.44.0 BiocGenerics_0.52.0
## [11] MatrixGenerics_1.18.0 matrixStats_1.4.1
## [13] BiocStyle_2.34.0
##
## loaded via a namespace (and not attached):
## [1] KEGGREST_1.46.0 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 generics_0.1.3 curl_5.2.3
## [10] AnnotationDbi_1.68.0 tibble_3.2.1 fansi_1.0.6
## [13] RSQLite_2.3.7 blob_1.2.4 BiocBaseUtils_1.8.0
## [16] pkgconfig_2.0.3 Matrix_1.7-1 dbplyr_2.5.0
## [19] lifecycle_1.0.4 GenomeInfoDbData_1.2.13 compiler_4.4.1
## [22] Biostrings_2.74.0 htmltools_0.5.8.1 sass_0.4.9
## [25] yaml_2.3.10 pillar_1.9.0 crayon_1.5.3
## [28] jquerylib_0.1.4 DelayedArray_0.32.0 cachem_1.1.0
## [31] magick_2.8.5 abind_1.4-8 mime_0.12
## [34] ExperimentHub_2.14.0 AnnotationHub_3.14.0 tidyselect_1.2.1
## [37] digest_0.6.37 purrr_1.0.2 dplyr_1.1.4
## [40] bookdown_0.41 BiocVersion_3.20.0 fastmap_1.2.0
## [43] grid_4.4.1 cli_3.6.3 SparseArray_1.6.0
## [46] magrittr_2.0.3 S4Arrays_1.6.0 utf8_1.2.4
## [49] withr_3.0.2 rappdirs_0.3.3 filelock_1.0.3
## [52] UCSC.utils_1.2.0 bit64_4.5.2 rmarkdown_2.28
## [55] XVector_0.46.0 httr_1.4.7 bit_4.5.0
## [58] png_0.1-8 SpatialExperiment_1.16.0 memoise_2.0.1
## [61] evaluate_1.0.1 knitr_1.48 BiocFileCache_2.14.0
## [64] rlang_1.1.4 Rcpp_1.0.13 glue_1.8.0
## [67] DBI_1.2.3 formatR_1.14 BiocManager_1.30.25
## [70] jsonlite_1.8.9 R6_2.5.1 zlibbioc_1.52.0Stoeckius, 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.