chevreulShiny 1.1.2
chevreulShinyR is an open-source statistical environment which can be easily modified
to enhance its functionality via packages. chevreulShiny is a R
package available via the Bioconductor repository
for packages. R can be installed on any operating system from
CRAN after which you can install
chevreulShiny by using the following commands in your R session:
if (!requireNamespace("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
BiocManager::install("chevreulShiny")
The chevreulShiny package is designed for single-cell RNA sequencing
data. The functions included within this package are derived from other
packages that have implemented the infrastructure needed for RNA-seq data
processing and analysis. Packages that have been instrumental in the
development of chevreulShiny include,
Biocpkg("SummarizedExperiment") and Biocpkg("scater").
R and Bioconductor have a steep learning curve so it is critical to
learn where to ask for help. The
Bioconductor support site is the main
resource for getting help: remember to use the chevreulShiny tag and check
the older posts.
chevreulShinyThe chevreulShiny package contains a shiny app for easy
visualization and analysis of scRNA data.
chevreulShiny uses SingleCellExperiment (SCE) object type
(from SingleCellExperiment)
to store expression and other metadata from single-cell experiments.
This package features functions capable of:
library("chevreulShiny")
# Load the data
data("small_example_dataset")chevreulShiny includes a shiny app for exploratory scRNA data analysis and visualization which can be accessed via
minimalChevreulApp(small_example_dataset)
Note: the SCE object must be pre-processed and integrated (if required) prior to running the shiny app.
This package includes a set of Shiny apps for interactive exploration of single cell RNA sequencing (scRNA-seq) datasets processed as SingleCellExperiments
A demo with a developing human retina scRNA-seq dataset from Shayler et al. is available here
chevreulShiny includes tools for:
chevreulShiny depends on a minimum R version 4.4. The current
chevreulShiny loads two Bioconductor dependencies
These enable standardized processing, plotting of SingleCellExperiments, respectively.
When installing an R package like chevreulShiny with many dependencies, conflicts with existing installations can arise. This is a common issue in R package management. Here are some strategies to address this problem:
Consider renv for dependency management. This tool creates isolated environments for each project, ensuring that package versions don’t conflict across different projects.
Use the conflicted Package The conflicted package provides an alternative conflict resolution strategy. It makes every conflict an error, forcing you to choose which function to use
When installing R packages on slow internet connections, several issues can arise, particularly with larger packages or when using functions like remotes::install_github(). Here are some strategies to address bandwidth-related problems:
Set a longer timeout for downloads:
options(timeout = 9999999)
Specify the download method:
options(download.file.method = "libcurl")
Recommended minimum hardware requirements for running chevreulShiny are as follows:
It’s important to note that these requirements can vary based on the size and complexity of your dataset. As the number of cells increases, so do the hardware requirements. For instance: A dataset with around 8,000 cells can be analyzed with 8 GB of RAM. For larger datasets or more complex analyses, 64-128 GB of RAM can be beneficial.
To learn more about the usage of Bioconductor tools for single-cell RNA-seq analysis. Consult the book Orchestrating Single-Cell Analysis with Bioconductor. The book walks through common workflows for the analysis of single-cell RNA-seq data (scRNA-seq). This book will show you how to make use of cutting-edge Bioconductor tools to process, analyze, visualize, and explore scRNA-seq data
R session information.
#> R version 4.5.0 (2025-04-11 ucrt)
#> Platform: x86_64-w64-mingw32/x64
#> Running under: Windows Server 2022 x64 (build 20348)
#>
#> Matrix products: default
#> LAPACK version 3.12.1
#>
#> locale:
#> [1] LC_COLLATE=C LC_CTYPE=English_United States.utf8 LC_MONETARY=English_United States.utf8
#> [4] LC_NUMERIC=C LC_TIME=English_United States.utf8
#>
#> time zone: America/New_York
#> tzcode source: internal
#>
#> attached base packages:
#> [1] stats4 stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] chevreulShiny_1.1.2 chevreulPlot_1.1.2 chevreulProcess_1.1.2 scater_1.37.0
#> [5] ggplot2_3.5.2 scuttle_1.19.0 shinydashboard_0.7.3 shiny_1.10.0
#> [9] SingleCellExperiment_1.31.0 SummarizedExperiment_1.39.0 Biobase_2.69.0 GenomicRanges_1.61.0
#> [13] GenomeInfoDb_1.45.4 IRanges_2.43.0 S4Vectors_0.47.0 BiocGenerics_0.55.0
#> [17] generics_0.1.4 MatrixGenerics_1.21.0 matrixStats_1.5.0 BiocStyle_2.37.0
#>
#> loaded via a namespace (and not attached):
#> [1] later_1.4.2 batchelor_1.25.0 BiocIO_1.19.0 ggplotify_0.1.2
#> [5] bitops_1.0-9 tibble_3.2.1 polyclip_1.10-7 XML_3.99-0.18
#> [9] lifecycle_1.0.4 edgeR_4.7.2 doParallel_1.0.17 globals_0.18.0
#> [13] MASS_7.3-65 lattice_0.22-7 ensembldb_2.33.0 alabaster.base_1.9.2
#> [17] magrittr_2.0.3 limma_3.65.1 plotly_4.10.4 sass_0.4.10
#> [21] rmarkdown_2.29 jquerylib_0.1.4 yaml_2.3.10 shinyBS_0.61.1
#> [25] metapod_1.17.0 httpuv_1.6.16 EnhancedVolcano_1.27.0 DBI_1.2.3
#> [29] RColorBrewer_1.1-3 ResidualMatrix_1.19.0 abind_1.4-8 purrr_1.0.4
#> [33] ggraph_2.2.1 AnnotationFilter_1.33.0 RCurl_1.98-1.17 yulab.utils_0.2.0
#> [37] rappdirs_0.3.3 tweenr_2.0.3 circlize_0.4.16 ggrepel_0.9.6
#> [41] irlba_2.3.5.1 listenv_0.9.1 megadepth_1.19.0 cmdfun_1.0.2
#> [45] parallelly_1.45.0 dqrng_0.4.1 DelayedMatrixStats_1.31.0 codetools_0.2-20
#> [49] DelayedArray_0.35.1 ggforce_0.4.2 DT_0.33 tidyselect_1.2.1
#> [53] shape_1.4.6.1 UCSC.utils_1.5.0 farver_2.1.2 rhandsontable_0.3.8
#> [57] wiggleplotr_1.33.0 ScaledMatrix_1.17.0 viridis_0.6.5 shinyWidgets_0.9.0
#> [61] GenomicAlignments_1.45.0 jsonlite_2.0.0 GetoptLong_1.0.5 BiocNeighbors_2.3.1
#> [65] waiter_0.2.5 tidygraph_1.3.1 iterators_1.0.14 foreach_1.5.2
#> [69] tools_4.5.0 Rcpp_1.0.14 glue_1.8.0 gridExtra_2.3
#> [73] SparseArray_1.9.0 xfun_0.52 dplyr_1.1.4 withr_3.0.2
#> [77] BiocManager_1.30.25 fastmap_1.2.0 clustree_0.5.1 rhdf5filters_1.21.0
#> [81] bluster_1.19.0 shinyjs_2.1.0 digest_0.6.37 rsvd_1.0.5
#> [85] gridGraphics_0.5-1 R6_2.6.1 mime_0.13 colorspace_2.1-1
#> [89] dichromat_2.0-0.1 RSQLite_2.4.0 tidyr_1.3.1 data.table_1.17.4
#> [93] rtracklayer_1.69.0 graphlayouts_1.2.2 httr_1.4.7 htmlwidgets_1.6.4
#> [97] S4Arrays_1.9.1 pkgconfig_2.0.3 gtable_0.3.6 blob_1.2.4
#> [101] ComplexHeatmap_2.25.0 XVector_0.49.0 htmltools_0.5.8.1 shinyhelper_0.3.2
#> [105] bookdown_0.43 ProtGenerics_1.41.0 clue_0.3-66 scales_1.4.0
#> [109] png_0.1-8 scran_1.37.0 rstudioapi_0.17.1 knitr_1.50
#> [113] tzdb_0.5.0 rjson_0.2.23 curl_6.2.3 rhdf5_2.53.1
#> [117] cachem_1.1.0 GlobalOptions_0.1.2 stringr_1.5.1 miniUI_0.1.2
#> [121] parallel_4.5.0 vipor_0.4.7 AnnotationDbi_1.71.0 restfulr_0.0.15
#> [125] alabaster.schemas_1.9.0 pillar_1.10.2 grid_4.5.0 vctrs_0.6.5
#> [129] promises_1.3.3 shinyFiles_0.9.3 BiocSingular_1.25.0 EnsDb.Hsapiens.v86_2.99.0
#> [133] beachmat_2.25.1 xtable_1.8-4 cluster_2.1.8.1 beeswarm_0.4.0
#> [137] evaluate_1.0.3 readr_2.1.5 GenomicFeatures_1.61.3 cli_3.6.5
#> [141] locfit_1.5-9.12 compiler_4.5.0 Rsamtools_2.25.0 rlang_1.1.6
#> [145] crayon_1.5.3 DataEditR_0.1.5 forcats_1.0.0 fs_1.6.6
#> [149] ggbeeswarm_0.7.2 stringi_1.8.7 viridisLite_0.4.2 BiocParallel_1.43.3
#> [153] Biostrings_2.77.1 lazyeval_0.2.2 Matrix_1.7-3 hms_1.1.3
#> [157] patchwork_1.3.0 future_1.58.0 sparseMatrixStats_1.21.0 bit64_4.6.0-1
#> [161] Rhdf5lib_1.31.0 KEGGREST_1.49.0 statmod_1.5.0 igraph_2.1.4
#> [165] memoise_2.0.1 bslib_0.9.0 bit_4.6.0