The muscData package contains a set of publicly
available single-cell RNA sequencing (scRNA-seq) datasets with complex
experimental designs, i.e., datasets that contain multiple samples
(e.g., individuals) measured across multiple experimental conditions
(e.g., treatments), formatted into SingleCellExperiment
(SCE) Bioconductor objects. Data objects are hosted through
Bioconductor’s ExperimentHub web resource.
The table below gives an overview of currently available datasets,
including a unique identifier (ID) that can be used to load the data
(see next section), a brief description, the original data source, and a
reference. Dataset descriptions may also be viewed from within R via
?ID (e.g., ?Kang18_8vs8).
| ID | Description | Availability | Reference |
|---|---|---|---|
Kang18_8vs8 |
10x droplet-based scRNA-seq PBMC data from 8 Lupus patients before and after 6h-treatment with INF-beta (16 samples in total) | Gene Expression Ombnibus (GEO) accession GSE96583 | Kang et al. (2018) |
Crowell19_4vs4 |
Single-nuclei RNA-seq data of 8 CD-1 male mice, split into 2 groups with 4 animals each: vehicle and peripherally lipopolysaccharaide (LPS) treated mice | Figshare DOI:10.6084/m9.figshare.8976473.v1 | Crowell et al. (2019) |
All datasets available within muscData may be loaded
either via named functions that directly reffer to the object names, or
by using the ExperimentHub interface. Both methods are
demonstrated below.
The datasets listed above may be loaded into R by their ID. All
provided SCEs contain unfiltered raw counts in their assay
slot, and any available gene and cell metadata in the
rowData and colData slots, respectively.
## class: SingleCellExperiment
## dim: 35635 29065
## metadata(0):
## assays(1): counts
## rownames(35635): MIR1302-10 FAM138A ... MT-ND6 MT-CYB
## rowData names(2): ENSEMBL SYMBOL
## colnames(29065): AAACATACAATGCC-1 AAACATACATTTCC-1 ... TTTGCATGGTTTGG-1
## TTTGCATGTCTTAC-1
## colData names(5): ind stim cluster cell multiplets
## reducedDimNames(1): TSNE
## mainExpName: NULL
## altExpNames(0):ExperimentHubBesides using an accession function as demonstrated above, we can
browse ExperimentHub records (using query) or package
specific records (using listResources), and then load the
data of interest. The key differences between these approaches is that
query will search all of ExperimentHub, while
listResources facilitate data discovery within the
specified package (here, muscData).
queryWe first initialize a Hub instance to search for and load available
data with the ExperimentHub function, and store the
complete list of >2000 records in a variable eh. Using
query, we then identify any records made available by
muscData, as well as their accession IDs (EH1234). Finally,
we can load the data into R via eh[[id]].
## ExperimentHub with 2 records
## # snapshotDate(): 2026-10-07
## # $dataprovider: GEO, F. Hoffmann-La Roche Ltd.
## # $species: Mus musculus, Homo sapiens
## # $rdataclass: SingleCellExperiment
## # additional mcols(): taxonomyid, genome, description,
## # coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
## # rdatapath, sourceurl, sourcetype
## # retrieve records with, e.g., 'object[["EH2259"]]'
##
## title
## EH2259 | Kang18_8vs8
## EH3297 | Crowell19_4vs4list/loadResourcesAlternatively, available records may be viewed via
listResources. To then load a specific dataset or subset
thereof using loadResources, we require a character vector
of metadata search terms to filter by.
Available metadata can accessed from the ExperimentHub records found
by query via mcols(), or viewed using the
accessors shown above with option metadata = TRUE. In the
example below, we use "PMBC" and "INF-beta" to
select the Kang18_8vs8 dataset. However, note that any
metadata keyword(s) that uniquely identify the data of interest could be
used (e.g., "Lupus" or "GSE96583").
## [1] "Kang18_8vs8" "Crowell19_4vs4"The scater (McCarthy et al. 2017) package provides an easy-to-use set of visualization tools for scRNA-seq data.
For interactive visualization, we recommend the iSEE
(interactive SummerizedExperiment Explorer) package (Rue-Albrecht et al. 2018), which provides a
Shiny-based graphical user interface for exploration of single-cell data
in SummarizedExperiment format (installation instructions
and user guides are available here).
When available, a great tool for interactive exploration and comparison of dimension-reduced embeddings is sleepwalk (Ovchinnikova and Anders 2019).
## R version 4.6.1 (2026-06-24)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 26.04.1 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.32.so; LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [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: Etc/UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] muscData_1.27.1 SingleCellExperiment_1.35.2
## [3] SummarizedExperiment_1.43.0 Biobase_2.73.2
## [5] GenomicRanges_1.65.4 Seqinfo_1.3.2
## [7] IRanges_2.47.5 S4Vectors_0.51.10
## [9] MatrixGenerics_1.25.0 matrixStats_1.5.0
## [11] ExperimentHub_3.3.2 AnnotationHub_4.3.2
## [13] BiocFileCache_3.3.0 dbplyr_2.6.0
## [15] BiocGenerics_0.59.12 generics_0.1.4
## [17] BiocStyle_2.41.0
##
## loaded via a namespace (and not attached):
## [1] KEGGREST_1.53.6 xfun_0.61 bslib_0.12.0
## [4] httr2_1.3.0 lattice_0.23-1 vctrs_0.7.3
## [7] tools_4.6.1 curl_8.0.0 tibble_3.3.1
## [10] AnnotationDbi_1.75.2 RSQLite_3.53.3 blob_1.3.0
## [13] pkgconfig_2.0.3 BiocBaseUtils_1.15.1 Matrix_1.7-6
## [16] lifecycle_1.0.5 compiler_4.6.1 Biostrings_2.81.9
## [19] htmltools_0.5.9 sys_3.4.3 buildtools_1.0.0
## [22] sass_0.4.10 yaml_2.3.12 pillar_1.11.1
## [25] crayon_1.5.3 jquerylib_0.1.4 DelayedArray_0.39.8
## [28] cachem_1.1.0 abind_1.4-8 tidyselect_1.2.1
## [31] digest_0.6.39 purrr_1.2.2 dplyr_1.2.1
## [34] BiocVersion_3.24.0 maketools_1.3.2 grid_4.6.1
## [37] fastmap_1.2.0 SparseArray_1.13.4 cli_3.6.6
## [40] magrittr_2.0.5 S4Arrays_1.13.2 withr_3.0.3
## [43] filelock_1.0.3 rappdirs_0.3.4 bit64_4.8.6
## [46] rmarkdown_2.32 XVector_0.53.0 httr_1.4.9
## [49] bit_4.6.0 otel_0.2.0 png_0.1-9
## [52] memoise_2.0.1 evaluate_1.0.5 knitr_1.52
## [55] rlang_1.3.0 glue_1.8.1 DBI_1.3.0
## [58] BiocManager_1.30.27 jsonlite_2.0.0 R6_2.6.1