if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("SingleCellMultiModal")library(MultiAssayExperiment)
library(SingleCellMultiModal)ECCITE-seq data are an evolution of the CITE-seq data (see also CITE-seq vignette for more details) by extending the CITE-seq original data types with a third one always extracted from the same cell. Indeed, in addition to the CITE-seq providing scRNA-seq and antibody-derived tags (ADT), it provides around ten Hashtagged Oligo (HTO). In particular this dataset is provided by Mimitou et al. (2019).
The user can see the available dataset by using the default options through the CITE-seq function.
CITEseq(DataType="peripheral_blood", modes="*", dry.run=TRUE, version="1.0.0")## Dataset: peripheral_blood## ah_id mode file_size rdataclass rdatadateadded rdatadateremoved
## 1 EH4613 CTCL_scADT 0.4 Mb matrix 2020-09-24 <NA>
## 2 EH4614 CTCL_scHTO 0.1 Mb matrix 2020-09-24 <NA>
## 3 EH4615 CTCL_scRNA 14.3 Mb dgCMatrix 2020-09-24 <NA>
## 4 EH4616 CTCL_TCRab 0.3 Mb data.frame 2020-09-24 <NA>
## 5 EH4617 CTCL_TCRgd 0.1 Mb data.frame 2020-09-24 <NA>
## 6 EH4618 CTRL_scADT 0.4 Mb matrix 2020-09-24 <NA>
## 7 EH4619 CTRL_scHTO 0.1 Mb matrix 2020-09-24 <NA>
## 8 EH4620 CTRL_scRNA 13.3 Mb dgCMatrix 2020-09-24 <NA>
## 9 EH4621 CTRL_TCRab 0.2 Mb data.frame 2020-09-24 <NA>
## 10 EH4622 CTRL_TCRgd 0.1 Mb data.frame 2020-09-24 <NA>Or simply by setting dry.run = FALSE it downloads the data and by default
creates the MultiAssayExperiment object.
In this example, we will use one of the two available datasets scADT_Counts:
mae <- CITEseq(DataType="peripheral_blood", modes="*", dry.run=FALSE, version="1.0.0")## Warning: 'ExperimentList' contains 'data.frame' or 'DataFrame',
## potential for errors with mixed data types
## Warning: 'ExperimentList' contains 'data.frame' or 'DataFrame',
## potential for errors with mixed data types
## Warning: 'ExperimentList' contains 'data.frame' or 'DataFrame',
## potential for errors with mixed data types
## 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: dgCMatrix with 52 rows and 13000 columns
## [2] scHTO: dgCMatrix with 7 rows and 13000 columns
## [3] scRNA: dgCMatrix with 33538 rows and 10248 columns
## Functionality:
## experiments() - obtain the ExperimentList instance
## colData() - the primary/phenotype DataFrame
## sampleMap() - the sample coordination DataFrame
## `$`, `[`, `[[` - extract colData columns, subset, or experiment
## *Format() - convert into a long or wide DataFrame
## assays() - convert ExperimentList to a SimpleList of matrices
## exportClass() - save data to flat filesExample with actual data:
experiments(mae)## ExperimentList class object of length 3:
## [1] scADT: dgCMatrix with 52 rows and 13000 columns
## [2] scHTO: dgCMatrix with 7 rows and 13000 columns
## [3] scRNA: dgCMatrix with 33538 rows and 10248 columnsAdditionally, we stored into the object metedata
Check row annotations:
rownames(mae)## CharacterList of length 3
## [["scADT"]] B220 (CD45R) B7-H1 (PD-L1) C-kit (CD117) ... no_match total_reads
## [["scHTO"]] HTO28_5P HTO29_5P HTO30_5P HTO44_5P bad_struct no_match total_reads
## [["scRNA"]] hg19_A1BG hg19_A1BG-AS1 hg19_A1CF ... hg19_ZZEF1 hg19_hsa-mir-1253Take a peek at the sampleMap:
sampleMap(mae)## DataFrame with 36248 rows and 3 columns
## assay primary colname
## <factor> <character> <character>
## 1 scADT CTCL_AAACCTGAGCTATGCT CTCL_AAACCTGAGCTATGCT
## 2 scADT CTCL_AAACCTGCAATGGAGC CTCL_AAACCTGCAATGGAGC
## 3 scADT CTCL_AAACCTGCATACTACG CTCL_AAACCTGCATACTACG
## 4 scADT CTCL_AAACCTGCATATGGTC CTCL_AAACCTGCATATGGTC
## 5 scADT CTCL_AAACCTGCATGCATGT CTCL_AAACCTGCATGCATGT
## ... ... ... ...
## 36244 scRNA CTRL_TTTGTCAGTCACCCAG CTRL_TTTGTCAGTCACCCAG
## 36245 scRNA CTRL_TTTGTCAGTGCAGGTA CTRL_TTTGTCAGTGCAGGTA
## 36246 scRNA CTRL_TTTGTCATCACAATGC CTRL_TTTGTCATCACAATGC
## 36247 scRNA CTRL_TTTGTCATCCTAAGTG CTRL_TTTGTCATCCTAAGTG
## 36248 scRNA CTRL_TTTGTCATCGTTGACA CTRL_TTTGTCATCGTTGACAThe scRNA-seq data are accessible with the name scRNAseq, which returns a
matrix object.
head(experiments(mae)$scRNA)[, 1:4]## 6 x 4 sparse Matrix of class "dgCMatrix"
## CTCL_AAACCTGCAATGGAGC CTCL_AAACCTGCATACTACG CTCL_AAACCTGCATATGGTC
## hg19_A1BG . . .
## hg19_A1BG-AS1 . . .
## hg19_A1CF . . .
## hg19_A2M . . .
## hg19_A2M-AS1 . . .
## hg19_A2ML1 . . .
## CTCL_AAACCTGCATGCATGT
## hg19_A1BG .
## hg19_A1BG-AS1 .
## hg19_A1CF .
## hg19_A2M .
## hg19_A2M-AS1 .
## hg19_A2ML1 .The scADT data are accessible with the name scADT, which returns a
matrix object.
head(experiments(mae)$scADT)[, 1:4]## 6 x 4 sparse Matrix of class "dgCMatrix"
## CTCL_AAACCTGAGCTATGCT CTCL_AAACCTGCAATGGAGC CTCL_AAACCTGCATACTACG
## B220 (CD45R) 4 1 .
## B7-H1 (PD-L1) 2 . 3
## C-kit (CD117) 5 2 3
## CCR7 23 7 11
## CD11b 4 . 11
## CD11c 5 3 3
## CTCL_AAACCTGCATATGGTC
## B220 (CD45R) 1
## B7-H1 (PD-L1) 3
## C-kit (CD117) 5
## CCR7 18
## CD11b 5
## CD11c 3The dataset has two different conditions (CTCL and CTRL) which samples can be identified with the colData accessor.
CTCL stands for cutaneous T-cell lymphoma while CTRL for control.
For example, if we want only the CTCL samples, we can run:
(ctclMae <- mae[,colData(mae)$condition == "CTCL",])## A MultiAssayExperiment object of 3 listed
## experiments with user-defined names and respective classes.
## Containing an ExperimentList class object of length 3:
## [1] scADT: dgCMatrix with 52 rows and 6500 columns
## [2] scHTO: dgCMatrix with 7 rows and 6500 columns
## [3] scRNA: dgCMatrix with 33538 rows and 5399 columns
## Functionality:
## experiments() - obtain the ExperimentList instance
## colData() - the primary/phenotype DataFrame
## sampleMap() - the sample coordination DataFrame
## `$`, `[`, `[[` - extract colData columns, subset, or experiment
## *Format() - convert into a long or wide DataFrame
## assays() - convert ExperimentList to a SimpleList of matrices
## exportClass() - save data to flat filesAnd if you’re interested into the common samples across all the modalities
you can use the complete.cases funtion.
ctclMae[,complete.cases(ctclMae),]## A MultiAssayExperiment object of 3 listed
## experiments with user-defined names and respective classes.
## Containing an ExperimentList class object of length 3:
## [1] scADT: dgCMatrix with 52 rows and 4190 columns
## [2] scHTO: dgCMatrix with 7 rows and 4190 columns
## [3] scRNA: dgCMatrix with 33538 rows and 4190 columns
## Functionality:
## experiments() - obtain the ExperimentList instance
## colData() - the primary/phenotype DataFrame
## sampleMap() - the sample coordination DataFrame
## `$`, `[`, `[[` - 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 filesThe CRISPR perturbed scRNAs data are stored in a different spot to keep their original long format.
They can be accessed with the metadata accessors which, in this case returns a named list of data.frames.
sgRNAs <- metadata(mae)
names(sgRNAs)## [1] "CTCL_TCRab" "CTCL_TCRgd" "CTRL_TCRab" "CTRL_TCRgd"There are four different sgRNAs datasets, one per each condition and family receptors combination.
TCR stands for T-Cell Receptor, while a,b,g,d stand for alpha, beta, gamma and delta respectively.
To look into the TCRab, simply run:
head(sgRNAs$CTCL_TCRab)## barcode is_cell contig_id high_confidence
## 1 AAACCTGCAATGGAGC-1 True AAACCTGCAATGGAGC-1_contig_1 True
## 10 AAACCTGGTCATACTG-1 True AAACCTGGTCATACTG-1_contig_2 True
## 100 AAAGTAGGTAAATACG-1 True AAAGTAGGTAAATACG-1_contig_1 True
## 1000 ACGGGCTTCGGCGCAT-1 True ACGGGCTTCGGCGCAT-1_contig_2 True
## 1001 ACGGGTCAGGACTGGT-1 True ACGGGTCAGGACTGGT-1_contig_1 True
## 1002 ACGGGTCAGGACTGGT-1 True ACGGGTCAGGACTGGT-1_contig_2 True
## length chain v_gene d_gene j_gene c_gene full_length productive
## 1 609 TRB TRBV12-4 TRBD1 TRBJ2-7 TRBC2 False None
## 10 552 TRB TRBV5-5 TRBD1 TRBJ2-1 TRBC2 True True
## 100 556 TRA TRAV12-1 None TRAJ40 TRAC True True
## 1000 560 TRB TRBV20-1 None TRBJ2-1 TRBC2 True True
## 1001 669 TRB TRBV5-1 None TRBJ2-5 TRBC2 True True
## 1002 720 TRA TRAV8-1 None TRAJ22 TRAC True True
## cdr3 cdr3_nt reads
## 1 CASSLGAVGEQYF TGTGCCAGCAGTCTCGGGGCCGTCGGGGAGCAGTACTTC 4173
## 10 CASSLLRVYEQFF TGTGCCAGCAGCTTACTCAGGGTTTATGAGCAGTTCTTC 5561
## 100 CVVNMLIGPGTYKYIF TGTGTGGTGAACATGCTCATCGGCCCAGGAACCTACAAATACATCTTT 1725
## 1000 CSARFLRGGYNEQFF TGCAGTGCTAGGTTCCTCCGGGGTGGCTACAATGAGCAGTTCTTC 8428
## 1001 CASSPPGETQYF TGCGCCAGCAGTCCCCCGGGAGAGACCCAGTACTTC 27854
## 1002 CAVNGAGSARQLTF TGTGCCGTGAATGGAGCTGGTTCTGCAAGGCAACTGACCTTT 6497
## umis raw_clonotype_id raw_consensus_id
## 1 2 clonotype126 None
## 10 3 clonotype31 clonotype31_consensus_2
## 100 1 clonotype3 clonotype3_consensus_2
## 1000 6 clonotype2 clonotype2_consensus_2
## 1001 17 clonotype289 clonotype289_consensus_2
## 1002 4 clonotype289 clonotype289_consensus_1Because of already large use of some methodologies (such as
in the [SingleCellExperiment vignette][1] or [CiteFuse Vignette][2] 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="peripheral_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
## Warning: 'ExperimentList' contains 'data.frame' or 'DataFrame',
## potential for errors with mixed data types
## Warning: 'ExperimentList' contains 'data.frame' or 'DataFrame',
## potential for errors with mixed data types
## Warning: 'ExperimentList' contains 'data.frame' or 'DataFrame',
## potential for errors with mixed data typessce## class: SingleCellExperiment
## dim: 33538 8482
## metadata(0):
## assays(1): counts
## rownames(33538): hg19_A1BG hg19_A1BG-AS1 ... hg19_ZZEF1
## hg19_hsa-mir-1253
## rowData names(0):
## colnames(8482): CTCL_AAACCTGCAATGGAGC CTCL_AAACCTGCATACTACG ...
## CTRL_TTTGTCATCCTAAGTG CTRL_TTTGTCATCGTTGACA
## colData names(0):
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(2): scADT scHTOsessionInfo()## R version 4.3.1 (2023-06-16)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.3 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.17-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] SingleCellMultiModal_1.12.3 MultiAssayExperiment_1.26.0
## [3] SummarizedExperiment_1.30.2 Biobase_2.60.0
## [5] GenomicRanges_1.52.0 GenomeInfoDb_1.36.2
## [7] IRanges_2.34.1 S4Vectors_0.38.1
## [9] BiocGenerics_0.46.0 MatrixGenerics_1.12.3
## [11] matrixStats_1.0.0 BiocStyle_2.28.0
##
## loaded via a namespace (and not attached):
## [1] DBI_1.1.3 bitops_1.0-7
## [3] formatR_1.14 rlang_1.1.1
## [5] magrittr_2.0.3 compiler_4.3.1
## [7] RSQLite_2.3.1 DelayedMatrixStats_1.22.6
## [9] png_0.1-8 vctrs_0.6.3
## [11] pkgconfig_2.0.3 SpatialExperiment_1.10.0
## [13] crayon_1.5.2 fastmap_1.1.1
## [15] magick_2.7.5 dbplyr_2.3.3
## [17] XVector_0.40.0 ellipsis_0.3.2
## [19] scuttle_1.10.2 utf8_1.2.3
## [21] promises_1.2.1 rmarkdown_2.24
## [23] purrr_1.0.2 bit_4.0.5
## [25] xfun_0.40 zlibbioc_1.46.0
## [27] cachem_1.0.8 beachmat_2.16.0
## [29] jsonlite_1.8.7 blob_1.2.4
## [31] later_1.3.1 rhdf5filters_1.12.1
## [33] DelayedArray_0.26.7 Rhdf5lib_1.22.0
## [35] BiocParallel_1.34.2 interactiveDisplayBase_1.38.0
## [37] parallel_4.3.1 R6_2.5.1
## [39] bslib_0.5.1 limma_3.56.2
## [41] jquerylib_0.1.4 Rcpp_1.0.11
## [43] bookdown_0.35 knitr_1.43
## [45] R.utils_2.12.2 BiocBaseUtils_1.2.0
## [47] httpuv_1.6.11 Matrix_1.6-1
## [49] tidyselect_1.2.0 abind_1.4-5
## [51] yaml_2.3.7 codetools_0.2-19
## [53] curl_5.0.2 lattice_0.21-8
## [55] tibble_3.2.1 withr_2.5.0
## [57] shiny_1.7.5 KEGGREST_1.40.0
## [59] evaluate_0.21 BiocFileCache_2.8.0
## [61] ExperimentHub_2.8.1 Biostrings_2.68.1
## [63] pillar_1.9.0 BiocManager_1.30.22
## [65] filelock_1.0.2 generics_0.1.3
## [67] RCurl_1.98-1.12 BiocVersion_3.17.1
## [69] sparseMatrixStats_1.12.2 xtable_1.8-4
## [71] glue_1.6.2 tools_4.3.1
## [73] AnnotationHub_3.8.0 locfit_1.5-9.8
## [75] rhdf5_2.44.0 grid_4.3.1
## [77] DropletUtils_1.20.0 AnnotationDbi_1.62.2
## [79] edgeR_3.42.4 SingleCellExperiment_1.22.0
## [81] GenomeInfoDbData_1.2.10 HDF5Array_1.28.1
## [83] cli_3.6.1 rappdirs_0.3.3
## [85] fansi_1.0.4 S4Arrays_1.0.6
## [87] dplyr_1.1.3 R.methodsS3_1.8.2
## [89] sass_0.4.7 digest_0.6.33
## [91] dqrng_0.3.1 rjson_0.2.21
## [93] memoise_2.0.1 htmltools_0.5.6
## [95] R.oo_1.25.0 lifecycle_1.0.3
## [97] httr_1.4.7 mime_0.12
## [99] bit64_4.0.5https://www.bioconductor.org/packages/release/bioc/vignettes/SingleCellExperiment/inst/doc/intro.html#5_adding_alternative_feature_sets http://www.bioconductor.org/packages/release/bioc/vignettes/CiteFuse/inst/doc/CiteFuse.html
Mimitou, Eleni P, Anthony Cheng, Antonino Montalbano, Stephanie Hao, Marlon Stoeckius, Mateusz Legut, Timothy Roush, et al. 2019. “Multiplexed Detection of Proteins, Transcriptomes, Clonotypes and Crispr Perturbations in Single Cells.” Nature Methods 16 (5): 409–12.