library(MultiAssayExperiment)
library(SpatialExperiment)
library(SingleCellMultiModal)The dataset consists of two data types, seq-FISH data was provided by Zhu et al. (2018), while scRNA-seq data was provided by Tasic et al. (2016).
Data have been retrievedas part of the Hackathon in the Mathematical Frameworks for Integrative Analysis of Emerging Biological DataTypes workshop.
The user can see the available dataset by using the default options
SingleCellMultiModal::seqFISH(DataType="mouse_visual_cortex",
modes="*", dry.run=TRUE, version="2.0.0")## snapshotDate(): 2020-10-27## ah_id mode file_size rdataclass rdatadateadded
## 1 EH3785 scRNA_Counts 0.2 Mb matrix 2020-09-14
## 2 EH3786 scRNA_Labels 0 Mb data.frame 2020-09-14
## 3 EH3787 seqFISH_Coordinates 0 Mb data.frame 2020-09-14
## 4 EH3788 seqFISH_Counts 0.2 Mb matrix 2020-09-14
## 5 EH3789 seqFISH_Labels 0 Mb data.frame 2020-09-14
## rdatadateremoved
## 1 <NA>
## 2 <NA>
## 3 <NA>
## 4 <NA>
## 5 <NA>Or simply by running:
mae <- SingleCellMultiModal::seqFISH(DataType="mouse_visual_cortex",
modes="*", dry.run=FALSE, version="2.0.0")## snapshotDate(): 2020-10-27## Working on: scRNA_Counts## Working on: scRNA_Labels## Working on: seqFISH_Coordinates## Working on: seqFISH_Counts## Working on: seqFISH_Labels## see ?SingleCellMultiModal and browseVignettes('SingleCellMultiModal') for documentation## loading from cache## see ?SingleCellMultiModal and browseVignettes('SingleCellMultiModal') for documentation## loading from cache## see ?SingleCellMultiModal and browseVignettes('SingleCellMultiModal') for documentation## loading from cache## see ?SingleCellMultiModal and browseVignettes('SingleCellMultiModal') for documentation## loading from cache## see ?SingleCellMultiModal and browseVignettes('SingleCellMultiModal') for documentation## loading from cache## ExperimentList contains data.frame or DataFrame,
## potential for errors with mixed data typesmae## A MultiAssayExperiment object of 2 listed
## experiments with user-defined names and respective classes.
## Containing an ExperimentList class object of length 2:
## [1] seqFISH: SpatialExperiment with 113 rows and 1597 columns
## [2] scRNAseq: SingleCellExperiment with 113 rows and 1723 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(mae)## ExperimentList class object of length 2:
## [1] seqFISH: SpatialExperiment with 113 rows and 1597 columns
## [2] scRNAseq: SingleCellExperiment with 113 rows and 1723 columnsCheck row annotations:
rownames(mae)## CharacterList of length 2
## [["seqFISH"]] abca15 abca9 acta2 adcy4 aldh3b2 ... wrn zfp182 zfp715 zfp90
## [["scRNAseq"]] abca15 abca9 acta2 adcy4 aldh3b2 ... wrn zfp182 zfp715 zfp90Take a peek at the sampleMap:
sampleMap(mae)## DataFrame with 3320 rows and 3 columns
## assay primary colname
## <factor> <character> <character>
## 1 seqFISH V2 V2
## 2 seqFISH V3 V3
## 3 seqFISH V4 V4
## 4 seqFISH V5 V5
## 5 seqFISH V6 V6
## ... ... ... ...
## 3316 scRNAseq V1719 V1719
## 3317 scRNAseq V1720 V1720
## 3318 scRNAseq V1721 V1721
## 3319 scRNAseq V1722 V1722
## 3320 scRNAseq V1723 V1723The scRNA-seq data are accessible with $scRNAseq, which returns a
SingleCellExperiment class object, with all its associated methods.
experiments(mae)$scRNAseq## class: SingleCellExperiment
## dim: 113 1723
## metadata(0):
## assays(1): counts
## rownames(113): abca15 abca9 ... zfp715 zfp90
## rowData names(1): X
## colnames(1723): V1 V2 ... V1722 V1723
## colData names(3): broad_type sample_name dissection
## reducedDimNames(0):
## altExpNames(0):Otherwhise the assay function can be used to access the scRNAseq assay
stored in the mae MultiAssayExperiment object.
head(assay(mae, "scRNAseq"))[,1:4]## V1 V2 V3 V4
## abca15 11 42 17 42
## abca9 22 46 22 46
## acta2 15 47 15 42
## adcy4 12 45 12 45
## aldh3b2 27 49 27 49
## amigo2 23 43 101 43The seq-FISH data are accessible with $seqFISH, which returns a
SpatialExperiment class object.
experiments(mae)$seqFISH## class: SpatialExperiment
## dim: 113 1597
## metadata(0):
## assays(1): counts
## rownames(113): abca15 abca9 ... zfp715 zfp90
## rowData names(1): X
## colnames(1597): V2 V3 ... V1597 V1598
## colData names(6): Cell_ID cluster ... Irrelevant Prob
## reducedDimNames(0):
## altExpNames(0):
## spatialCoordinates(4): Cell_ID Irrelevant x yOtherwhise the assay function can be used to access the seqFISH assay
stored in the mae MultiAssayExperiment object.
head(assay(mae, "seqFISH"))[,1:4]## V2 V3 V4 V5
## abca15 68 49 50 39
## abca9 41 42 38 36
## acta2 25 23 16 21
## adcy4 39 54 37 18
## aldh3b2 101 47 41 52
## amigo2 93 64 93 93Spatial coordinates can be retrieved with spatialCoords function on the
SpatialExperiment object.
(sc <- spatialCoords(experiments(mae)$seqFISH))## DataFrame with 1597 rows and 4 columns
## Cell_ID Irrelevant x y
## <integer> <integer> <numeric> <numeric>
## 1 1 100 265.76 -231.14
## 2 2 100 290.48 -261.52
## 3 3 100 257.12 -133.35
## 4 4 100 753.46 -261.14
## 5 5 100 700.01 -169.05
## ... ... ... ... ...
## 1593 1593 100 1129.06 -1669.57
## 1594 1594 100 1044.02 -1872.66
## 1595 1595 100 1388.76 -1880.47
## 1596 1596 100 5172.85 -1340.96
## 1597 1597 100 5220.60 -1523.37They can also be stored by using the <- operator.
fakeCoords <- cbind(sc[,c(1:3)], sc[,3])
colnames(fakeCoords)[4] <- "y"
spatialCoords(experiments(mae)$seqFISH) <- fakeCoords
spatialCoords(experiments(mae)$seqFISH)## DataFrame with 1597 rows and 4 columns
## Cell_ID Irrelevant x y
## <integer> <integer> <numeric> <numeric>
## 1 1 100 265.76 265.76
## 2 2 100 290.48 290.48
## 3 3 100 257.12 257.12
## 4 4 100 753.46 753.46
## 5 5 100 700.01 700.01
## ... ... ... ... ...
## 1593 1593 100 1129.06 1129.06
## 1594 1594 100 1044.02 1044.02
## 1595 1595 100 1388.76 1388.76
## 1596 1596 100 5172.85 5172.85
## 1597 1597 100 5220.60 5220.60Direct access to the colnames of the spacial coordinates with
spatialCoordsNames function.
spatialCoordsNames(experiments(mae)$seqFISH)## [1] "Cell_ID" "Irrelevant" "x" "y"The provided seqFISH dataset comes out in two different versions:
The full scRNA-seq data matrix is 24057 rows x 1809 columns.
To access the v1.0.0 simply run
mae <- SingleCellMultiModal::seqFISH(DataType="mouse_visual_cortex",
modes="*", dry.run=FALSE, version="1.0.0")## snapshotDate(): 2020-10-27## Working on: scRNA_Full_Counts## Working on: scRNA_Full_Labels## Working on: seqFISH_Coordinates## Working on: seqFISH_Counts## Working on: seqFISH_Labels## see ?SingleCellMultiModal and browseVignettes('SingleCellMultiModal') for documentation## loading from cache## see ?SingleCellMultiModal and browseVignettes('SingleCellMultiModal') for documentation## loading from cache## see ?SingleCellMultiModal and browseVignettes('SingleCellMultiModal') for documentation## loading from cache## see ?SingleCellMultiModal and browseVignettes('SingleCellMultiModal') for documentation## loading from cache## see ?SingleCellMultiModal and browseVignettes('SingleCellMultiModal') for documentation## loading from cache## ExperimentList contains data.frame or DataFrame,
## potential for errors with mixed data typesmae## A MultiAssayExperiment object of 2 listed
## experiments with user-defined names and respective classes.
## Containing an ExperimentList class object of length 2:
## [1] seqFISH: SpatialExperiment with 113 rows and 1597 columns
## [2] scRNAseq: SingleCellExperiment with 24057 rows and 1809 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 filessessionInfo()## 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] SpatialExperiment_1.0.0 rhdf5_2.34.0
## [3] scater_1.18.3 ggplot2_3.3.3
## [5] scran_1.18.3 SingleCellExperiment_1.12.0
## [7] SingleCellMultiModal_1.2.4 MultiAssayExperiment_1.16.0
## [9] SummarizedExperiment_1.20.0 Biobase_2.50.0
## [11] GenomicRanges_1.42.0 GenomeInfoDb_1.26.2
## [13] IRanges_2.24.1 S4Vectors_0.28.1
## [15] BiocGenerics_0.36.0 MatrixGenerics_1.2.0
## [17] matrixStats_0.57.0 BiocStyle_2.18.1
##
## loaded via a namespace (and not attached):
## [1] ggbeeswarm_0.6.0 colorspace_2.0-0
## [3] HCAMatrixBrowser_1.0.1 ellipsis_0.3.1
## [5] scuttle_1.0.4 bluster_1.0.0
## [7] futile.logger_1.4.3 XVector_0.30.0
## [9] BiocNeighbors_1.8.2 farver_2.0.3
## [11] bit64_4.0.5 RSpectra_0.16-0
## [13] interactiveDisplayBase_1.28.0 AnnotationDbi_1.52.0
## [15] codetools_0.2-18 sparseMatrixStats_1.2.0
## [17] cachem_1.0.1 knitr_1.31
## [19] jsonlite_1.7.2 dbplyr_2.0.0
## [21] uwot_0.1.10 HDF5Array_1.18.0
## [23] shiny_1.6.0 BiocManager_1.30.10
## [25] compiler_4.0.3 httr_1.4.2
## [27] dqrng_0.2.1 assertthat_0.2.1
## [29] Matrix_1.3-2 fastmap_1.1.0
## [31] limma_3.46.0 later_1.1.0.1
## [33] BiocSingular_1.6.0 formatR_1.7
## [35] htmltools_0.5.1.1 tools_4.0.3
## [37] rsvd_1.0.3 igraph_1.2.6
## [39] gtable_0.3.0 glue_1.4.2
## [41] GenomeInfoDbData_1.2.4 dplyr_1.0.3
## [43] rappdirs_0.3.2 Rcpp_1.0.6
## [45] rapiclient_0.1.3 rhdf5filters_1.2.0
## [47] vctrs_0.3.6 ExperimentHub_1.16.0
## [49] DelayedMatrixStats_1.12.2 AnVIL_1.2.0
## [51] xfun_0.20 stringr_1.4.0
## [53] beachmat_2.6.4 mime_0.9
## [55] lifecycle_0.2.0 irlba_2.3.3
## [57] statmod_1.4.35 AnnotationHub_2.22.0
## [59] edgeR_3.32.1 zlibbioc_1.36.0
## [61] scales_1.1.1 promises_1.1.1
## [63] lambda.r_1.2.4 yaml_2.2.1
## [65] curl_4.3 memoise_2.0.0
## [67] gridExtra_2.3 UpSetR_1.4.0
## [69] stringi_1.5.3 RSQLite_2.2.3
## [71] highr_0.8 BiocVersion_3.12.0
## [73] BiocParallel_1.24.1 rlang_0.4.10
## [75] pkgconfig_2.0.3 bitops_1.0-6
## [77] evaluate_0.14 lattice_0.20-41
## [79] Rhdf5lib_1.12.1 purrr_0.3.4
## [81] labeling_0.4.2 cowplot_1.1.1
## [83] bit_4.0.4 tidyselect_1.1.0
## [85] RcppAnnoy_0.0.18 plyr_1.8.6
## [87] magrittr_2.0.1 bookdown_0.21
## [89] R6_2.5.0 magick_2.6.0
## [91] generics_0.1.0 DelayedArray_0.16.1
## [93] DBI_1.1.1 pillar_1.4.7
## [95] withr_2.4.1 RCurl_1.98-1.2
## [97] tibble_3.0.5 crayon_1.3.4
## [99] futile.options_1.0.1 BiocFileCache_1.14.0
## [101] rmarkdown_2.6 viridis_0.5.1
## [103] locfit_1.5-9.4 grid_4.0.3
## [105] blob_1.2.1 digest_0.6.27
## [107] xtable_1.8-4 httpuv_1.5.5
## [109] munsell_0.5.0 beeswarm_0.2.3
## [111] viridisLite_0.3.0 vipor_0.4.5Tasic, Bosiljka, Vilas Menon, Thuc Nghi Nguyen, Tae Kyung Kim, Tim Jarsky, Zizhen Yao, Boaz Levi, et al. 2016. “Adult Mouse Cortical Cell Taxonomy Revealed by Single Cell Transcriptomics.” Nature Neuroscience 19 (2): 335.
Zhu, Qian, Sheel Shah, Ruben Dries, Long Cai, and Guo-Cheng Yuan. 2018. “Identification of Spatially Associated Subpopulations by Combining scRNAseq and Sequential Fluorescence in Situ Hybridization Data.” Nature Biotechnology 36 (12): 1183.