# scRNAseqApp Guide

Jianhong Ou

#### 15 April 2025

# 1 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):

Tool | platform | plot type
--- | --- | ---
alona | web-based | scatters, bar
ASAP | web-based | scatters
Asc-Seurat | shiny package | scatters, heatmap, violin, dot, trajectory
BingleSeq | shiny package | scatters, heatmap, violin, ridge
CellView | shiny package | scatters
cellxgene VIP | web-based | scatters, bar, and visualization plugin
Cerebro/cerebroApp | shiny package | scatters, 3D scatters
CHARTS | web-based | scatters, bar
ChromSCape | shiny package | scatters, heatmap
Cirrocumulus | python package |
CReSCENT | web-based | scatters, violin
Cytosplore Viewer | Cytosplore | scatters, phylogeny
Granatum | shiny package | scatters, bar, trajectory, ppi
InterCellar | shiny package | dot, ppi, circos, radar, pie
iS-CellR | shiny package | scatters, heatmap, violin, bar, dot
iSEE | shiny package | scatters, heatmap, violin, bar
loom-viewer | python package |
Loupe Cell Browser | Desktop | scatters
PIVOT | shiny package | scatters, heatmap, violin, bar, pie
SC1 | web-based | scatters, heatmap, violin, bar
SCANNER | web-based | scatters
scClustViz | shiny package | scatters, heatmap, violin, bar, dot
scExplorer | web-based | scatters, heatmap
SCope | web-based | scatters
scSVA | shiny package | scatters, 3D scatters
scVI | python package | scatters, heatmap, violin, bar
seuratv3wizard | web-based | scatters
ShinyArchRUiO | shiny package | scatters, heatmap, track
ShinyCell | shiny package | scatters, heatmap, violin, bar
SCiAp1 1 1 short name for Single Cell Interactive Application | galaxy |
singleCellTK | shiny package | scatters, heatmap, violin, dot, trajectory
SPRING | python package | scatters
UCSC Cell Browser | web-based | scatters
Vitessce | python package | scatters, heatmap
WASP | shiny package | scatters, heatmap

Tool | languages | license | starts | watching | forks | citation
--- | --- | --- | --- | --- | --- | ---
alona | python | GPL-3 | 12 | 3 | 5 | 24
ASAP | Java,R,Python | GPL-3 | 18 | 6 | 8 | 88
Asc-Seurat | R | GPL-3 | 12 | 2 | 6 | 10
BingleSeq | R | MIT | 18 | 2 | 6 | 4
CellView | R | MIT | 16 | 9 | 8 | 8
cellxgene VIP | python,R,JavaScript | MIT | 81 | 6 | 24 | 10
Cerebro/cerebroApp | R,JavaScript,C | MIT | 79 | 7 | 18 | 41
CHARTS | python | MIT | 2 | 6 | 0 | 7
ChromSCape | R | GPL-3 | 11 | 2 | 4 | 13
Cirrocumulus | JavaScript,Python | BSD-3 | 38 | 7 | 9 | 78
CReSCENT | R,Perl | GPL-3 | 8 | 1 | 4 | 11
Cytosplore Viewer | java,javascript |  |  |  |  |
Granatum | R | Apache2 | 18 | 4 | 11 | 65
InterCellar | R | MIT | 7 | 1 | 3 | 4
iS-CellR | R | GPL-3 | 21 | 6 | 6 | 15
iSEE | R | MIT | 201 | 14 | 39 | 41
loom-viewer | python,JavaScript | BSD-2 | 32 | 9 | 6 |
Loupe Cell Browser |  |  |  |  |  |
PIVOT | R |  | 27 | 6 | 15 | 27
SC1 | R |  |  |  |  | 5
SCANNER | R |  | 0 | 2 | 1 | 1
scClustViz | R | MIT | 41 | 12 | 10 | 36
scExplorer | JavaScript,Python | GPL-3 | 7 | 3 | 6 | 16
SCope | python,JavaScript | GPL-3 | 60 | 8 | 14 | 4382 2 2 the package contribute partial to the citation
scSVA | R | GPL-3 | 20 | 6 | 7 | 8
scVI | python | BSD-3 | 840 | 27 | 263 | 787
seuratv3wizard | R | GPL-3 | 29 | 8 | 13 | 283 3 3 citation is from NASQAR
ShinyArchRUiO | R | GPL-3 | 11 | 2 | 4 | 3
ShinyCell | R | GPL-3 | 70 | 9 | 23 | 28
SCiAp |  |  |  |  |  | 16
singleCellTK | R | MIT | 105 | 12 | 61 | 6
SPRING | python,matlab,JavaScript |  | 59 | 10 | 29 | 250
UCSC Cell Browser | JavaScript,Python,R | GPL-3 | 3 | 1 | 39 | 46
Vitessce | JavaScript,Python,R | MIT | 92 | 6 | 23 | 2
WASP | R,python |  | 2 | 1 | 0 | 3

Tool | source code
--- | ---
alona | https://github.com/oscar-franzen/adobo/
ASAP | https://github.com/DeplanckeLab/ASAP
Asc-Seurat | https://github.com/KirstLab/asc_seurat/
BingleSeq | https://github.com/dbdimitrov/BingleSeq/
CellView | https://github.com/mohanbolisetty/CellView
cellxgene VIP | https://github.com/interactivereport/cellxgene_VIP
Cerebro/cerebroApp | https://github.com/romanhaa/Cerebro
CHARTS | https://github.com/stewart-lab/CHARTS
ChromSCape | https://github.com/vallotlab/ChromSCape
Cirrocumulus | https://github.com/lilab-bcb/cirrocumulus
CReSCENT | https://github.com/pughlab/crescent
Cytosplore Viewer |
Granatum | https://github.com/lanagarmire/Granatum
InterCellar | https://github.com/martaint/InterCellar
iS-CellR | https://github.com/immcore/iS-CellR
iSEE | https://github.com/iSEE/iSEE
loom-viewer | https://github.com/linnarsson-lab/loom-viewer
Loupe Cell Browser |
PIVOT | https://github.com/kimpenn/PIVOT
SC1 |
SCANNER | https://github.com/GuoshuaiCai/scanner
scClustViz | https://github.com/BaderLab/scClustViz
scExplorer | https://github.com/d-feng/scExplorer
SCope | https://github.com/aertslab/Scope
scSVA | https://github.com/klarman-cell-observatory/scSVA
scVI | https://github.com/scverse/scvi-tools
seuratv3wizard | https://github.com/nasqar/seuratv3wizard
ShinyArchRUiO | https://github.com/EskelandLab/ShinyArchRUiO
ShinyCell | https://github.com/SGDDNB/ShinyCell
SCiAp |
singleCellTK | https://github.com/compbiomed/singleCellTK
SPRING | https://github.com/AllonKleinLab/SPRING_dev
UCSC Cell Browser | https://github.com/ucscGenomeBrowser/cellBrowser
Vitessce | https://github.com/vitessce/vitessce
WASP | https://github.com/andreashoek/wasp

Tool | demo
--- | ---
alona | https://alona.panglaodb.se/
ASAP | https://asap.epfl.ch/
Asc-Seurat |
BingleSeq |
CellView | https://mbolisetty.shinyapps.io/CellView/
cellxgene VIP | https://cellxgenevip-ms.bxgenomics.com/
Cerebro/cerebroApp |
CHARTS | https://charts.morgridge.org/
ChromSCape | https://vallotlab.shinyapps.io/ChromSCape/
Cirrocumulus |
CReSCENT | https://crescent.cloud/
Cytosplore Viewer |
Granatum | http://granatum.dcmb.med.umich.edu:8103/
InterCellar |
iS-CellR |
iSEE | https://marionilab.cruk.cam.ac.uk/iSEE_allen/
loom-viewer |
Loupe Cell Browser |
PIVOT |
SC1 | https://sc1.engr.uconn.edu/
SCANNER | https://www.thecailab.com/scanner/
scClustViz | https://innesbt.shinyapps.io/scclustvizdemoapp/
scExplorer | http://singlecellexplorer.org
SCope | https://scope.aertslab.org/
scSVA |
scVI |
seuratv3wizard | https://nasqar.abudhabi.nyu.edu/SeuratV3Wizard/
ShinyArchRUiO | https://cancell.medisin.uio.no/ShinyArchR.UiO/
ShinyCell | http://shinycell1.ddnetbio.com/
SCiAp | https://humancellatlas.usegalaxy.eu/
singleCellTK | https://sctk.bu.edu/
SPRING. | https://kleintools.hms.harvard.edu/tools/
| springViewer_1_6_dev.html?datasets/mouse_HPCs/
| basal_bone_marrow/full4 4 4 URL shown in multiple lines
UCSC Cell Browser | https://cells.ucsc.edu/
Vitessce | http://vitessce.io/
WASP |

Tool | visualization tutorial
--- | ---
alona | https://alona.panglaodb.se/faq.html
ASAP | https://asap.epfl.ch/home/tutorial?t=fca
Asc-Seurat | https://asc-seurat.readthedocs.io/en/latest/index.html
BingleSeq | https://github.com/dbdimitrov/BingleSeq/blob/master/README.md
CellView |
cellxgene VIP | https://interactivereport.github.io/cellxgene_VIP/tutorial/docs
Cerebro/cerebroApp | https://romanhaa.github.io/cerebroApp/
CHARTS | https://github.com/stewart-lab/CHARTS/blob/master/README.md
ChromSCape | https://vallotlab.github.io/ChromSCape/articles/vignette.html
Cirrocumulus | https://cirrocumulus.readthedocs.io/en/latest/
CReSCENT | https://pughlab.github.io/crescent-frontend/
Cytosplore Viewer | https://viewer.cytosplore.org/
Granatum | https://github.com/lanagarmire/Granatum/blob/master/doc/
| Granatum_manual_0.92.pdf5 5 5 URL shown in multiple lines
InterCellar |
iS-CellR |
iSEE |
loom-viewer | https://github.com/linnarsson-lab/loom-viewer
Loupe Cell Browser |
PIVOT | https://rawgit.com/qinzhu/PIVOT/master/inst/app/www/manual_file.html
SC1 |
SCANNER |
scClustViz |
scExplorer | http://singlecellexplorer.org/tutorial.html
SCope | https://github.com/aertslab/SCope/blob/master/README.md
scSVA | https://github.com/klarman-cell-observatory/scSVA/blob/
| master/docs/index.md6 6 6 URL shown in multiple lines
scVI | https://docs.scvi-tools.org/en/stable/user_guide/index.html
seuratv3wizard | https://github.com/nasqar/seuratv3wizard/blob/master/README.md
ShinyArchRUiO |
ShinyCell | https://github.com/SGDDNB/ShinyCell/blob/master/README.md
SCiAp |
singleCellTK |
SPRING | https://kleintools.hms.harvard.edu/tools/spring.html
UCSC Cell Browser | https://cellbrowser.readthedocs.io/en/master/interface.html
Vitessce | http://vitessce.io/docs/
WASP | https://github.com/andreashoek/wasp/blob/main/README.md

# 2 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.

# 3 Quick start

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

## 3.1 Installation

First, install scRNAseqApp and other packages required to run the examples. Please note that the example dataset used here is from a small subset of PBMC34. Additional package are also required for enhancement functions such trajectory analysis or cell communication analysis.

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

## 3.2 Load library

    library(scRNAseqApp)

## 3.3 Initial the database

    publish_folder=tempdir()
    scInit(app_path=publish_folder)

## 3.4 Start shiny app

    scRNAseqApp(app_path = publish_folder)

# 4 Create a new data

There are two ways to create a new data.

- from the administrator mode
- use R session

## 4.1 by administrator

Log in to admin by `Switch User` tab and click `administrator button` in the right-bottom corner of screen. Click `UploadData` and upload a Seurat object.

## 4.2 via R session

There are two steps to create a new data via R session. First, create the config file with description of the data and second create the data from a Seurat object.

    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"

# 5 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.

# 6 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.

# 7 SessionInfo

    sessionInfo()

    ## R version 4.5.0 RC (2025-04-04 r88126)
    ## Platform: aarch64-apple-darwin20
    ## Running under: macOS Ventura 13.7.1
    ##
    ## Matrix products: default
    ## BLAS:   /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRblas.0.dylib
    ## LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.1
    ##
    ## locale:
    ## [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
    ##
    ## time zone: America/New_York
    ## tzcode source: internal
    ##
    ## attached base packages:
    ## [1] stats     graphics  grDevices utils     datasets  methods   base
    ##
    ## other attached packages:
    ## [1] Seurat_5.2.1       SeuratObject_5.0.2 sp_2.2-0           scRNAseqApp_1.8.0
    ## [5] BiocStyle_2.36.0
    ##
    ## loaded via a namespace (and not attached):
    ##   [1] fs_1.6.6                    matrixStats_1.5.0
    ##   [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_4.5.0                 sctransform_0.4.1
    ##  [11] backports_1.5.0             R6_2.6.1
    ##  [13] DT_0.33                     lazyeval_0.2.2
    ##  [15] uwot_0.2.3                  rhdf5filters_1.20.0
    ##  [17] GetoptLong_1.0.5            withr_3.0.2
    ##  [19] gridExtra_2.3               progressr_0.15.1
    ##  [21] cli_3.6.4                   Biobase_2.68.0
    ##  [23] spatstat.explore_3.4-2      fastDummies_1.7.5
    ##  [25] sass_0.4.10                 spatstat.data_3.1-6
    ##  [27] ggridges_0.5.6              pbapply_1.7-2
    ##  [29] askpass_1.2.1               slingshot_2.16.0
    ##  [31] Rsamtools_2.24.0            R.utils_2.13.0
    ##  [33] shinyhelper_0.3.2           parallelly_1.43.0
    ##  [35] limma_3.64.0                RSQLite_2.3.9
    ##  [37] generics_0.1.3              shape_1.4.6.1
    ##  [39] BiocIO_1.18.0               ica_1.0-3
    ##  [41] spatstat.random_3.3-3       dplyr_1.1.4
    ##  [43] Matrix_1.7-3                S4Vectors_0.46.0
    ##  [45] RefManageR_1.4.0            abind_1.4-8
    ##  [47] R.methodsS3_1.8.2           lifecycle_1.0.4
    ##  [49] yaml_2.3.10                 SummarizedExperiment_1.38.0
    ##  [51] rhdf5_2.52.0                SparseArray_1.8.0
    ##  [53] Rtsne_0.17                  grid_4.5.0
    ##  [55] blob_1.2.4                  promises_1.3.2
    ##  [57] crayon_1.5.3                miniUI_0.1.1.1
    ##  [59] lattice_0.22-7              billboarder_0.5.0
    ##  [61] cowplot_1.1.3               pillar_1.10.2
    ##  [63] knitr_1.50                  ComplexHeatmap_2.24.0
    ##  [65] GenomicRanges_1.60.0        rjson_0.2.23
    ##  [67] future.apply_1.11.3         codetools_0.2-20
    ##  [69] glue_1.8.0                  spatstat.univar_3.1-2
    ##  [71] data.table_1.17.0           vctrs_0.6.5
    ##  [73] png_0.1-8                   spam_2.11-1
    ##  [75] gtable_0.3.6                assertthat_0.2.1
    ##  [77] cachem_1.1.0                xfun_0.52
    ##  [79] princurve_2.1.6             S4Arrays_1.8.0
    ##  [81] mime_0.13                   survival_3.8-3
    ##  [83] SingleCellExperiment_1.30.0 iterators_1.0.14
    ##  [85] statmod_1.5.0               fitdistrplus_1.2-2
    ##  [87] ROCR_1.0-11                 nlme_3.1-168
    ##  [89] bit64_4.6.0-1               RcppAnnoy_0.0.22
    ##  [91] rprojroot_2.0.4             GenomeInfoDb_1.44.0
    ##  [93] bslib_0.9.0                 irlba_2.3.5.1
    ##  [95] KernSmooth_2.23-26          colorspace_2.1-1
    ##  [97] BiocGenerics_0.54.0         DBI_1.2.3
    ##  [99] tidyselect_1.2.1            bit_4.6.0
    ## [101] compiler_4.5.0              curl_6.2.2
    ## [103] xml2_1.3.8                  ggdendro_0.2.0
    ## [105] DelayedArray_0.34.0         plotly_4.10.4
    ## [107] scrypt_0.1.6                colourpicker_1.3.0
    ## [109] bookdown_0.43               rtracklayer_1.68.0
    ## [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_3.1-3
    ## [117] rmarkdown_2.29              XVector_0.48.0
    ## [119] htmltools_0.5.8.1           pkgconfig_2.0.3
    ## [121] bibtex_0.5.1                MatrixGenerics_1.20.0
    ## [123] learnr_0.11.5               fastmap_1.2.0
    ## [125] rlang_1.1.6                 GlobalOptions_0.1.2
    ## [127] htmlwidgets_1.6.4           UCSC.utils_1.4.0
    ## [129] shiny_1.10.0                farver_2.1.2
    ## [131] jquerylib_0.1.4             shinymanager_1.0.410
    ## [133] zoo_1.8-14                  jsonlite_2.0.0
    ## [135] BiocParallel_1.42.0         R.oo_1.27.0
    ## [137] RCurl_1.98-1.17             magrittr_2.0.3
    ## [139] GenomeInfoDbData_1.2.14     dotCall64_1.2
    ## [141] patchwork_1.3.0             Rhdf5lib_1.30.0
    ## [143] munsell_0.5.1               Rcpp_1.0.14
    ## [145] reticulate_1.42.0           TrajectoryUtils_1.16.0
    ## [147] stringi_1.8.7               MASS_7.3-65
    ## [149] plyr_1.8.9                  parallel_4.5.0
    ## [151] listenv_0.9.1               ggrepel_0.9.6
    ## [153] deldir_2.0-4                Biostrings_2.76.0
    ## [155] splines_4.5.0               tensor_1.5
    ## [157] circlize_0.4.16             igraph_2.1.4
    ## [159] spatstat.geom_3.3-6         RcppHNSW_0.6.0
    ## [161] reshape2_1.4.4              stats4_4.5.0
    ## [163] XML_3.99-0.18               evaluate_1.0.3
    ## [165] BiocManager_1.30.25         foreach_1.5.2
    ## [167] tweenr_2.0.3                httpuv_1.6.15
    ## [169] RANN_2.6.2                  tidyr_1.3.1
    ## [171] openssl_2.3.2               purrr_1.0.4
    ## [173] polyclip_1.10-7             future_1.40.0
    ## [175] clue_0.3-66                 scattermore_1.2
    ## [177] ggplot2_3.5.2               ggforce_0.4.2
    ## [179] xtable_1.8-4                restfulr_0.0.15
    ## [181] RSpectra_0.16-2             later_1.4.2
    ## [183] viridisLite_0.4.2           tibble_3.2.1
    ## [185] memoise_2.0.1               GenomicAlignments_1.44.0
    ## [187] IRanges_2.42.0              cluster_2.1.8.1
    ## [189] sortable_0.5.0              timechange_0.3.0
    ## [191] globals_0.16.3

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2.
Gardeux, V., David, F. P., Shajkofci, A., Schwalie, P. C. & Deplancke, B. ASAP: A web-based platform for the analysis and interactive visualization of single-cell RNA-seq data. Bioinformatics 33, 3123–3125 (2017).
3.
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4.
Dimitrov, D. & Gu, Q. BingleSeq: A user-friendly r package for bulk and single-cell RNA-seq data analysis. PeerJ 8, e10469 (2020).
5.
Bolisetty, M. T., Stitzel, M. L. & Robson, P. CellView: Interactive exploration of high dimensional single cell RNA-seq data. bioRxiv 123810 (2017).
6.
Li, K. et al. Cellxgene VIP unleashes full power of interactive visualization and integrative analysis of scRNA-seq, spatial transcriptomics, and multiome data. bioRxiv 2020–08 (2022).
7.
Hillje, R., Pelicci, P. G. & Luzi, L. Cerebro: Interactive visualization of scRNA-seq data. Bioinformatics 36, 2311–2313 (2020).
8.
Bernstein, M. N. et al. CHARTS: A web application for characterizing and comparing tumor subpopulations in publicly available single-cell RNA-seq data sets. BMC bioinformatics 22, 1–9 (2021).
9.
Prompsy, P. et al. Interactive analysis of single-cell epigenomic landscapes with ChromSCape. Nature communications 11, 1–9 (2020).
10.
Li, B. et al. Cumulus provides cloud-based data analysis for large-scale single-cell and single-nucleus RNA-seq. Nature methods 17, 793–798 (2020).
11.
Mohanraj, S. et al. Crescent: Cancer single cell expression toolkit. Nucleic Acids Research 48, W372–W379 (2020).
12.
Tasic, B. et al. Shared and distinct transcriptomic cell types across neocortical areas. Nature 563, 72–78 (2018).
13.
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