Introduction

Single-cell RNA sequencing (scRNA-seq) is a powerful technique to study gene expression, cellular heterogeneity, and cell states within samples in single-cell level. The development of scRNA-seq shed light to address the knowledge gap about the cell types, cell interactions, and key genes involved in biological process and their dynamics.

To increase the re-usability and reproducibility of scientific findings, more and more publishers require raw data and detailed descriptions of how the data were analyzed. However, difficulties arise due to the highly concise descriptions of analysis and the differences of the computing environments. Furthermore, to precisely meet the publishing requirement, the communication of the bioinformatician with researchers is a time-consuming step. Multiple interactive visualization tools were developed to provide the researchers access to the details of the data. Those tools include, but not limited to, alona¹, ASAP², Asc-Seurat³, BingleSeq⁴, CellView⁵, cellxgene VIP⁶, Cerebro⁷, CHARTS⁸, ChromSCape⁹, Cirrocumulus¹⁰, CReSCENT¹¹, Cytosplore Viewer¹², Granatum¹³, InterCellar¹⁴, iS-CellR¹⁵, iSEE¹⁶, loom-viewer, Loupe Cell Browser, PIVOT¹⁷, SC1¹⁸, SCANNER¹⁹, scClustViz²⁰, SCope²¹, scSVA²², scVI²³, SeuratV3Wizard²⁴/NASQAR, ShinyArchRUiO²⁵, ShinyCell²⁶, singleCellTK²⁷, Single Cell Explorer (scExplorer)²⁸, Single Cell Interactive Application (SCiAp)²⁹ and UCSC Cell Browser³⁰, SPRING³¹, WASP³², and Vitessce³³.

The basic information of the tools are list in the following tables (the table was created at 12/06/2022):

Motivation

Based on ShinyCell, The scRNAseqApp package is developed with multiple highly interactive visualizations of how cells and subsets of cells cluster behavior. The end users can discover the expression of genes in multiple interactive manners with highly customized filter conditions by selecting metadata supplied with the publications and download the ready-to-use results for republishing.

Quick start

Here is an example using scRNAseqApp with a subset of scRNA-seq data.

library(BiocManager)
BiocManager::install("scRNAseqApp")

library(scRNAseqApp)

publish_folder=tempdir()
scInit(app_path=publish_folder)

scRNAseqApp(app_path = publish_folder)

Create a new data

There are two ways to create a new data.

• from the administrator mode
• use R session

library(Seurat)
appconf <- createAppConfig(
            title="pbmc_small_test",
            destinationFolder = "pbmc_small_test",
            species = "Homo sapiens",
            doi="10.1038/nbt.3192",
            datatype = "scRNAseq",
            abstract = 'Put the description of the data here.')
createDataSet(
    appconf,
    pbmc_small,
    datafolder=file.path(publish_folder, "data"))

## An object of class Seurat 
## 230 features across 80 samples within 1 assay 
## Active assay: RNA (230 features, 20 variable features)
##  3 layers present: counts, data, scale.data
##  2 dimensional reductions calculated: pca, tsne

dir(file.path(publish_folder, 'data'))

## [1] "pbmc_small"      "pbmc_small_test"

Add downloadable file

If you have files intended for user download, kindly save them in the www/download folder. The application will then generate a list, differentiating between files and user folder names, for convenient access to download options. The file named as readme in text format in each folder will be used as descriptions for the files.

Distribute to a shiny server

There are two steps to distribute to a shiny server. First, install the package in the server as root user. Second, in a R session run scInit() after load the scRNAseqApp library. If you initialed the app offline, copy the app folder to the shiny server.

Note: the following files need to be writable for shiny: www/database.sqlite, www/counter.tsv and the app www folder should also be writable because the user manager is depend on SQLite, and the SQLite needs to be able to create a journal file in the same directory as the DB, before any modifications can take place. The journal is used to support transaction rollback.

SessionInfo

sessionInfo()

## R version 4.4.2 (2024-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 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-r0.3.26.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=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: Etc/UTC
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] Seurat_5.1.0       SeuratObject_5.0.2 sp_2.1-4           scRNAseqApp_1.7.6 
## [5] BiocStyle_2.35.0  
## 
## loaded via a namespace (and not attached):
##   [1] fs_1.6.5                    matrixStats_1.4.1          
##   [3] spatstat.sparse_3.1-0       bitops_1.0-9               
##   [5] lubridate_1.9.4             httr_1.4.7                 
##   [7] RColorBrewer_1.1-3          doParallel_1.0.17          
##   [9] tools_4.4.2                 sctransform_0.4.1          
##  [11] backports_1.5.0             R6_2.5.1                   
##  [13] DT_0.33                     lazyeval_0.2.2             
##  [15] uwot_0.2.2                  rhdf5filters_1.19.0        
##  [17] GetoptLong_1.0.5            withr_3.0.2                
##  [19] gridExtra_2.3               progressr_0.15.1           
##  [21] cli_3.6.3                   Biobase_2.67.0             
##  [23] spatstat.explore_3.3-3      fastDummies_1.7.4          
##  [25] sass_0.4.9                  spatstat.data_3.1-4        
##  [27] ggridges_0.5.6              pbapply_1.7-2              
##  [29] askpass_1.2.1               slingshot_2.15.0           
##  [31] Rsamtools_2.23.1            R.utils_2.12.3             
##  [33] shinyhelper_0.3.2           parallelly_1.41.0          
##  [35] RSQLite_2.3.9               generics_0.1.3             
##  [37] shape_1.4.6.1               BiocIO_1.17.1              
##  [39] ica_1.0-3                   spatstat.random_3.3-2      
##  [41] dplyr_1.1.4                 Matrix_1.7-1               
##  [43] S4Vectors_0.45.2            RefManageR_1.4.0           
##  [45] abind_1.4-8                 R.methodsS3_1.8.2          
##  [47] lifecycle_1.0.4             yaml_2.3.10                
##  [49] SummarizedExperiment_1.37.0 rhdf5_2.51.1               
##  [51] SparseArray_1.7.2           Rtsne_0.17                 
##  [53] grid_4.4.2                  blob_1.2.4                 
##  [55] promises_1.3.2              crayon_1.5.3               
##  [57] miniUI_0.1.1.1              lattice_0.22-6             
##  [59] billboarder_0.5.0           cowplot_1.1.3              
##  [61] sys_3.4.3                   maketools_1.3.1            
##  [63] pillar_1.10.0               knitr_1.49                 
##  [65] ComplexHeatmap_2.23.0       GenomicRanges_1.59.1       
##  [67] rjson_0.2.23                future.apply_1.11.3        
##  [69] codetools_0.2-20            leiden_0.4.3.1             
##  [71] glue_1.8.0                  spatstat.univar_3.1-1      
##  [73] data.table_1.16.4           vctrs_0.6.5                
##  [75] png_0.1-8                   spam_2.11-0                
##  [77] gtable_0.3.6                assertthat_0.2.1           
##  [79] cachem_1.1.0                xfun_0.49                  
##  [81] princurve_2.1.6             S4Arrays_1.7.1             
##  [83] mime_0.12                   survival_3.8-3             
##  [85] SingleCellExperiment_1.29.1 iterators_1.0.14           
##  [87] fitdistrplus_1.2-1          ROCR_1.0-11                
##  [89] nlme_3.1-166                bit64_4.5.2                
##  [91] RcppAnnoy_0.0.22            rprojroot_2.0.4            
##  [93] GenomeInfoDb_1.43.2         bslib_0.8.0                
##  [95] irlba_2.3.5.1               KernSmooth_2.23-24         
##  [97] colorspace_2.1-1            BiocGenerics_0.53.3        
##  [99] DBI_1.2.3                   tidyselect_1.2.1           
## [101] bit_4.5.0.1                 compiler_4.4.2             
## [103] curl_6.0.1                  xml2_1.3.6                 
## [105] ggdendro_0.2.0              DelayedArray_0.33.3        
## [107] plotly_4.10.4               scrypt_0.1.6               
## [109] colourpicker_1.3.0          rtracklayer_1.67.0         
## [111] scales_1.3.0                lmtest_0.9-40              
## [113] stringr_1.5.1               digest_0.6.37              
## [115] goftest_1.2-3               spatstat.utils_3.1-1       
## [117] rmarkdown_2.29              XVector_0.47.1             
## [119] htmltools_0.5.8.1           pkgconfig_2.0.3            
## [121] bibtex_0.5.1                MatrixGenerics_1.19.0      
## [123] learnr_0.11.5               fastmap_1.2.0              
## [125] rlang_1.1.4                 GlobalOptions_0.1.2        
## [127] htmlwidgets_1.6.4           UCSC.utils_1.3.0           
## [129] shiny_1.10.0                farver_2.1.2               
## [131] jquerylib_0.1.4             shinymanager_1.0.410       
## [133] zoo_1.8-12                  jsonlite_1.8.9             
## [135] BiocParallel_1.41.0         R.oo_1.27.0                
## [137] RCurl_1.98-1.16             magrittr_2.0.3             
## [139] GenomeInfoDbData_1.2.13     dotCall64_1.2              
## [141] patchwork_1.3.0             Rhdf5lib_1.29.0            
## [143] munsell_0.5.1               Rcpp_1.0.13-1              
## [145] TrajectoryUtils_1.15.0      reticulate_1.40.0          
## [147] stringi_1.8.4               zlibbioc_1.52.0            
## [149] MASS_7.3-61                 plyr_1.8.9                 
## [151] parallel_4.4.2              listenv_0.9.1              
## [153] ggrepel_0.9.6               deldir_2.0-4               
## [155] Biostrings_2.75.3           splines_4.4.2              
## [157] tensor_1.5                  circlize_0.4.16            
## [159] igraph_2.1.2                spatstat.geom_3.3-4        
## [161] RcppHNSW_0.6.0              buildtools_1.0.0           
## [163] reshape2_1.4.4              stats4_4.4.2               
## [165] XML_3.99-0.17               evaluate_1.0.1             
## [167] BiocManager_1.30.25         foreach_1.5.2              
## [169] tweenr_2.0.3                httpuv_1.6.15              
## [171] RANN_2.6.2                  tidyr_1.3.1                
## [173] openssl_2.3.0               purrr_1.0.2                
## [175] polyclip_1.10-7             future_1.34.0              
## [177] clue_0.3-66                 scattermore_1.2            
## [179] ggplot2_3.5.1               ggforce_0.4.2              
## [181] xtable_1.8-4                restfulr_0.0.15            
## [183] RSpectra_0.16-2             later_1.4.1                
## [185] viridisLite_0.4.2           tibble_3.2.1               
## [187] memoise_2.0.1               GenomicAlignments_1.43.0   
## [189] IRanges_2.41.2              cluster_2.1.8              
## [191] sortable_0.5.0              timechange_0.3.0           
## [193] globals_0.16.3