library(MultiAssayExperiment)
library(SingleCellMultiModal)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 .
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(): 2020-10-27## 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:
mse <- CITEseq(
DataType="cord_blood", modes="*", dry.run=FALSE, version="1.0.0"
)
mse## 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
## `$`, `[`, `[[` - extract colData columns, subset, or experiment
## *Format() - convert into a long or wide DataFrame
## assays() - convert ExperimentList to a SimpleList of matrices
## exportClass() - save all data to filesExample with actual data:
experiments(mse)## ExperimentList class object of length 2:
## [1] scADT: matrix with 13 rows and 8617 columns
## [2] scRNAseq: matrix with 36280 rows and 8617 columnsCheck row annotations:
rownames(mse)## CharacterList of length 2
## [["scADT"]] CD3 CD4 CD8 CD45RA CD56 CD16 CD10 CD11c CD14 CD19 CD34 CCR5 CCR7
## [["scRNAseq"]] ERCC_ERCC-00104 HUMAN_A1BG ... MOUSE_n-R5s25 MOUSE_n-R5s31Take a peek at the sampleMap:
sampleMap(mse)## DataFrame with 17234 rows and 3 columns
## assay primary colname
## <factor> <character> <character>
## 1 scADT CTGTTTACACCGCTAG CTGTTTACACCGCTAG
## 2 scADT CTCTACGGTGTGGCTC CTCTACGGTGTGGCTC
## 3 scADT AGCAGCCAGGCTCATT AGCAGCCAGGCTCATT
## 4 scADT GAATAAGAGATCCCAT GAATAAGAGATCCCAT
## 5 scADT GTGCATAGTCATGCAT GTGCATAGTCATGCAT
## ... ... ... ...
## 17230 scRNAseq TTGCCGTGTAGATTAG TTGCCGTGTAGATTAG
## 17231 scRNAseq GGCGTGTAGTGTACTC GGCGTGTAGTGTACTC
## 17232 scRNAseq CGTATGCCGTCTTCTG CGTATGCCGTCTTCTG
## 17233 scRNAseq TACACGACGCTCTTCC TACACGACGCTCTTCC
## 17234 scRNAseq ACACGACGCTCTTCCG ACACGACGCTCTTCCGThe scRNA-seq data are accessible with the name scRNAseq, which returns a
matrix object.
head(experiments(mse)$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 0The scADT data are accessible with the name scADT, which returns a
matrix object.
head(experiments(mse)$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 82Because 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")sessionInfo()## R version 4.0.3 (2020-10-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.5 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.12-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.12-bioc/R/lib/libRlapack.so
##
## 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
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] SingleCellMultiModal_1.2.4 MultiAssayExperiment_1.16.0
## [3] SummarizedExperiment_1.20.0 Biobase_2.50.0
## [5] GenomicRanges_1.42.0 GenomeInfoDb_1.26.2
## [7] IRanges_2.24.1 S4Vectors_0.28.1
## [9] BiocGenerics_0.36.0 MatrixGenerics_1.2.0
## [11] matrixStats_0.57.0 BiocStyle_2.18.1
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.6 lattice_0.20-41
## [3] SingleCellExperiment_1.12.0 assertthat_0.2.1
## [5] digest_0.6.27 mime_0.9
## [7] BiocFileCache_1.14.0 R6_2.5.0
## [9] RSQLite_2.2.3 evaluate_0.14
## [11] httr_1.4.2 pillar_1.4.7
## [13] zlibbioc_1.36.0 rlang_0.4.10
## [15] curl_4.3 blob_1.2.1
## [17] Matrix_1.3-2 rmarkdown_2.6
## [19] AnnotationHub_2.22.0 stringr_1.4.0
## [21] RCurl_1.98-1.2 bit_4.0.4
## [23] shiny_1.6.0 DelayedArray_0.16.1
## [25] compiler_4.0.3 httpuv_1.5.5
## [27] xfun_0.20 pkgconfig_2.0.3
## [29] htmltools_0.5.1.1 tidyselect_1.1.0
## [31] tibble_3.0.5 GenomeInfoDbData_1.2.4
## [33] interactiveDisplayBase_1.28.0 bookdown_0.21
## [35] withr_2.4.1 crayon_1.3.4
## [37] dplyr_1.0.3 dbplyr_2.0.0
## [39] later_1.1.0.1 bitops_1.0-6
## [41] rappdirs_0.3.2 grid_4.0.3
## [43] xtable_1.8-4 lifecycle_0.2.0
## [45] DBI_1.1.1 magrittr_2.0.1
## [47] stringi_1.5.3 cachem_1.0.1
## [49] XVector_0.30.0 promises_1.1.1
## [51] SpatialExperiment_1.0.0 ellipsis_0.3.1
## [53] generics_0.1.0 vctrs_0.3.6
## [55] tools_4.0.3 bit64_4.0.5
## [57] glue_1.4.2 BiocVersion_3.12.0
## [59] purrr_0.3.4 fastmap_1.1.0
## [61] yaml_2.2.1 AnnotationDbi_1.52.0
## [63] ExperimentHub_1.16.0 BiocManager_1.30.10
## [65] memoise_2.0.0 knitr_1.31