Author: Ludwig Geistlinger
Date: 2026-05-21

# G&T-seq Mouse Embryo (8-cell stage)

Ludwig Geistlinger

#### 21 May 2026

#### Package

SingleCellMultiModal 1.25.3

# 1 Installation

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

    BiocManager::install("SingleCellMultiModal")

## 1.1 Load

    library(SingleCellMultiModal)
    library(MultiAssayExperiment)

# 2 G&T-seq: parallel sequencing data of single-cell genomes and transcriptomes

G&T-seq is a combination of Picoplex amplified gDNA sequencing (genome) and SMARTSeq2 amplified cDNA sequencing (transcriptome) of the same cell. For more information, see Macaulay et al. (2015).

## 2.1 Downloading datasets

The user can see the available dataset by using the default options

    GTseq("mouse_embryo_8_cell", mode = "*", dry.run = TRUE)

    ##    ah_id           mode file_size           rdataclass rdatadateadded
    ## 1 EH5431        genomic      0 Mb     RaggedExperiment     2021-03-24
    ## 2 EH5433 transcriptomic    2.3 Mb SingleCellExperiment     2021-03-24
    ##   rdatadateremoved
    ## 1             <NA>
    ## 2             <NA>

Or by simply running:

    GTseq()

    ##    ah_id           mode file_size           rdataclass rdatadateadded
    ## 1 EH5431        genomic      0 Mb     RaggedExperiment     2021-03-24
    ## 2 EH5433 transcriptomic    2.3 Mb SingleCellExperiment     2021-03-24
    ##   rdatadateremoved
    ## 1             <NA>
    ## 2             <NA>

## 2.2 Obtaining the data

To obtain the actual datasets:

    gts <- GTseq(dry.run = FALSE)

    ## Warning: sampleMap[['assay']] coerced with as.factor()

    gts

    ## A MultiAssayExperiment object of 2 listed
    ##  experiments with user-defined names and respective classes.
    ##  Containing an ExperimentList class object of length 2:
    ##  [1] genomic: RaggedExperiment with 2366 rows and 112 columns
    ##  [2] transcriptomic: SingleCellExperiment with 24029 rows and 112 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

## 2.3 Exploring the data structure

Check available metadata for each of the 112 mouse embryo cells assayed by G&T-seq:

    colData(gts)

    ## DataFrame with 112 rows and 3 columns
    ##         Characteristics.organism. Characteristics.sex.
    ##                       <character>          <character>
    ## cell1                Mus musculus               female
    ## cell2                Mus musculus               female
    ## cell3                Mus musculus                 male
    ## cell4                Mus musculus                 male
    ## cell5                Mus musculus               female
    ## ...                           ...                  ...
    ## cell108              Mus musculus               female
    ## cell109              Mus musculus                 male
    ## cell110              Mus musculus                 male
    ## cell111              Mus musculus               female
    ## cell112              Mus musculus               female
    ##         Characteristics.cell.type.
    ##                        <character>
    ## cell1       8_cell_stage_single_..
    ## cell2       8_cell_stage_single_..
    ## cell3       8_cell_stage_single_..
    ## cell4       8_cell_stage_single_..
    ## cell5       8_cell_stage_single_..
    ## ...                            ...
    ## cell108     8_cell_stage_single_..
    ## cell109     8_cell_stage_single_..
    ## cell110     8_cell_stage_single_..
    ## cell111     8_cell_stage_single_..
    ## cell112     8_cell_stage_single_..

Take a peek at the `sampleMap`:

    sampleMap(gts)

    ## DataFrame with 224 rows and 3 columns
    ##              assay     primary     colname
    ##           <factor> <character> <character>
    ## 1   transcriptomic       cell1   ERR861694
    ## 2   transcriptomic       cell2   ERR861750
    ## 3   transcriptomic       cell3   ERR861695
    ## 4   transcriptomic       cell4   ERR861751
    ## 5   transcriptomic       cell5   ERR861696
    ## ...            ...         ...         ...
    ## 220        genomic     cell108   ERR863164
    ## 221        genomic     cell109   ERR863109
    ## 222        genomic     cell110   ERR863165
    ## 223        genomic     cell111   ERR863110
    ## 224        genomic     cell112   ERR863166

## 2.4 Copy numbers

To access the integer copy numbers as detected from scDNA-seq:

    head(assay(gts, "genomic"))[, 1:4]

    ##                          ERR863111 ERR863834 ERR863112 ERR863835
    ## chr1:23000001-25500000          NA        NA        NA        NA
    ## chr4:112000001-114500000        NA        NA        NA        NA
    ## chr4:145000001-148500000        NA        NA        NA        NA
    ## chr5:14000001-16500000          NA        NA        NA        NA
    ## chr15:66500001-69000000         NA        NA        NA        NA
    ## chrX:21500001-36000000          NA        NA        NA        NA

## 2.5 RNA-seq

To access raw read counts as quantified from scRNA-seq:

    head(assay(gts, "transcriptomic"))[, 1:4]

    ##                    ERR861694 ERR861750 ERR861695 ERR861751
    ## ENSMUSG00000000001         4         7        30        32
    ## ENSMUSG00000000003         0         0         0         0
    ## ENSMUSG00000000028        11        17        79        94
    ## ENSMUSG00000000031         0         0         0         0
    ## ENSMUSG00000000037         0         0         1         0
    ## ENSMUSG00000000049         0         0         0         0

For protocol information, see Macaulay et al. (2016).

# 3 sessionInfo

    sessionInfo()

    ## R version 4.6.0 RC (2026-04-17 r89917)
    ## Platform: x86_64-pc-linux-gnu
    ## Running under: Ubuntu 24.04.4 LTS
    ##
    ## Matrix products: default
    ## BLAS:   /home/biocbuild/bbs-3.24-bioc/R/lib/libRblas.so
    ## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0  LAPACK version 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] RaggedExperiment_1.37.0     SingleCellExperiment_1.35.1
    ##  [3] SingleCellMultiModal_1.25.3 MultiAssayExperiment_1.39.0
    ##  [5] SummarizedExperiment_1.43.0 Biobase_2.73.1
    ##  [7] GenomicRanges_1.65.0        Seqinfo_1.3.0
    ##  [9] IRanges_2.47.1              S4Vectors_0.51.2
    ## [11] BiocGenerics_0.59.2         generics_0.1.4
    ## [13] MatrixGenerics_1.25.0       matrixStats_1.5.0
    ## [15] BiocStyle_2.41.0
    ##
    ## loaded via a namespace (and not attached):
    ##  [1] KEGGREST_1.53.0          rjson_0.2.23             xfun_0.57
    ##  [4] bslib_0.11.0             httr2_1.2.2              lattice_0.22-9
    ##  [7] vctrs_0.7.3              tools_4.6.0              curl_7.1.0
    ## [10] tibble_3.3.1             AnnotationDbi_1.75.0     RSQLite_3.52.0
    ## [13] blob_1.3.0               BiocBaseUtils_1.15.1     pkgconfig_2.0.3
    ## [16] Matrix_1.7-5             dbplyr_2.5.2             lifecycle_1.0.5
    ## [19] compiler_4.6.0           Biostrings_2.81.1        htmltools_0.5.9
    ## [22] sass_0.4.10              yaml_2.3.12              pillar_1.11.1
    ## [25] crayon_1.5.3             jquerylib_0.1.4          DelayedArray_0.39.2
    ## [28] cachem_1.1.0             magick_2.9.1             abind_1.4-8
    ## [31] ExperimentHub_3.3.0      AnnotationHub_4.3.0      tidyselect_1.2.1
    ## [34] digest_0.6.39            purrr_1.2.2              dplyr_1.2.1
    ## [37] bookdown_0.46            BiocVersion_3.24.0       fastmap_1.2.0
    ## [40] grid_4.6.0               cli_3.6.6                SparseArray_1.13.2
    ## [43] magrittr_2.0.5           S4Arrays_1.13.0          withr_3.0.2
    ## [46] filelock_1.0.3           rappdirs_0.3.4           bit64_4.8.2
    ## [49] rmarkdown_2.31           XVector_0.53.0           httr_1.4.8
    ## [52] bit_4.6.0                otel_0.2.0               png_0.1-9
    ## [55] SpatialExperiment_1.23.0 memoise_2.0.1            evaluate_1.0.5
    ## [58] knitr_1.51               BiocFileCache_3.3.0      rlang_1.2.0
    ## [61] Rcpp_1.1.1-1.1           glue_1.8.1               DBI_1.3.0
    ## [64] formatR_1.14             BiocManager_1.30.27      jsonlite_2.0.0
    ## [67] R6_2.6.1

# References

Macaulay, Iain C, Wilfried Haerty, Parveen Kumar, Yang I Li, Tim Xiaoming Hu, Mabel J Teng, Mubeen Goolam, et al. 2015. “G&T-seq: Parallel Sequencing of Single-Cell Genomes and Transcriptomes.” Nat. Methods 12 (6): 519–22.

Macaulay, Iain C, Mabel J Teng, Wilfried Haerty, Parveen Kumar, Chris P Ponting, and Thierry Voet. 2016. “Separation and Parallel Sequencing of the Genomes and Transcriptomes of Single Cells Using G&T-seq.” Nat. Protoc. 11 (11): 2081–2103.