scRNAseqApp Guide

Jianhong Ou

21 February 2024

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 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 the package contribute paritial 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 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 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 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 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")
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 Under development (unstable) (2024-01-16 r85808)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.3 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.19-bioc/R/lib/libRblas.so 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
## 
## 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] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] Seurat_5.0.1       SeuratObject_5.0.1 sp_2.1-3           scRNAseqApp_1.3.11
## [5] BiocStyle_2.31.0  
## 
## loaded via a namespace (and not attached):
##   [1] matrixStats_1.2.0           spatstat.sparse_3.0-3      
##   [3] bitops_1.0-7                lubridate_1.9.3            
##   [5] httr_1.4.7                  RColorBrewer_1.1-3         
##   [7] doParallel_1.0.17           tools_4.4.0                
##   [9] sctransform_0.4.1           backports_1.4.1            
##  [11] utf8_1.2.4                  R6_2.5.1                   
##  [13] DT_0.32                     lazyeval_0.2.2             
##  [15] uwot_0.1.16                 rhdf5filters_1.15.2        
##  [17] GetoptLong_1.0.5            withr_3.0.0                
##  [19] gridExtra_2.3               progressr_0.14.0           
##  [21] cli_3.6.2                   Biobase_2.63.0             
##  [23] spatstat.explore_3.2-6      fastDummies_1.7.3          
##  [25] sass_0.4.8                  spatstat.data_3.0-4        
##  [27] ggridges_0.5.6              pbapply_1.7-2              
##  [29] askpass_1.2.0               slingshot_2.11.0           
##  [31] Rsamtools_2.19.3            R.utils_2.12.3             
##  [33] shinyhelper_0.3.2           parallelly_1.37.0          
##  [35] limma_3.59.3                RSQLite_2.3.5              
##  [37] generics_0.1.3              shape_1.4.6                
##  [39] BiocIO_1.13.0               ica_1.0-3                  
##  [41] spatstat.random_3.2-2       dplyr_1.1.4                
##  [43] Matrix_1.6-5                fansi_1.0.6                
##  [45] S4Vectors_0.41.3            RefManageR_1.4.0           
##  [47] abind_1.4-5                 R.methodsS3_1.8.2          
##  [49] lifecycle_1.0.4             yaml_2.3.8                 
##  [51] SummarizedExperiment_1.33.3 rhdf5_2.47.4               
##  [53] SparseArray_1.3.4           Rtsne_0.17                 
##  [55] grid_4.4.0                  blob_1.2.4                 
##  [57] promises_1.2.1              crayon_1.5.2               
##  [59] miniUI_0.1.1.1              lattice_0.22-5             
##  [61] billboarder_0.4.1           cowplot_1.1.3              
##  [63] pillar_1.9.0                knitr_1.45                 
##  [65] ComplexHeatmap_2.19.0       GenomicRanges_1.55.3       
##  [67] rjson_0.2.21                future.apply_1.11.1        
##  [69] codetools_0.2-19            leiden_0.4.3.1             
##  [71] glue_1.7.0                  data.table_1.15.0          
##  [73] vctrs_0.6.5                 png_0.1-8                  
##  [75] spam_2.10-0                 gtable_0.3.4               
##  [77] assertthat_0.2.1            cachem_1.0.8               
##  [79] xfun_0.42                   princurve_2.1.6            
##  [81] S4Arrays_1.3.3              mime_0.12                  
##  [83] survival_3.5-8              SingleCellExperiment_1.25.0
##  [85] iterators_1.0.14            statmod_1.5.0              
##  [87] ellipsis_0.3.2              fitdistrplus_1.1-11        
##  [89] ROCR_1.0-11                 nlme_3.1-164               
##  [91] bit64_4.0.5                 RcppAnnoy_0.0.22           
##  [93] rprojroot_2.0.4             GenomeInfoDb_1.39.6        
##  [95] bslib_0.6.1                 irlba_2.3.5.1              
##  [97] KernSmooth_2.23-22          colorspace_2.1-0           
##  [99] BiocGenerics_0.49.1         DBI_1.2.2                  
## [101] tidyselect_1.2.0            curl_5.2.0                 
## [103] bit_4.0.5                   compiler_4.4.0             
## [105] xml2_1.3.6                  ggdendro_0.1.23            
## [107] DelayedArray_0.29.4         plotly_4.10.4              
## [109] scrypt_0.1.6                bookdown_0.37              
## [111] rtracklayer_1.63.0          scales_1.3.0               
## [113] lmtest_0.9-40               stringr_1.5.1              
## [115] digest_0.6.34               goftest_1.2-3              
## [117] spatstat.utils_3.0-4        rmarkdown_2.25             
## [119] XVector_0.43.1              htmltools_0.5.7            
## [121] pkgconfig_2.0.3             bibtex_0.5.1               
## [123] MatrixGenerics_1.15.0       learnr_0.11.5              
## [125] fastmap_1.1.1               rlang_1.1.3                
## [127] GlobalOptions_0.1.2         htmlwidgets_1.6.4          
## [129] shiny_1.8.0                 farver_2.1.1               
## [131] jquerylib_0.1.4             shinymanager_1.0.410       
## [133] zoo_1.8-12                  jsonlite_1.8.8             
## [135] BiocParallel_1.37.0         R.oo_1.26.0                
## [137] RCurl_1.98-1.14             magrittr_2.0.3             
## [139] GenomeInfoDbData_1.2.11     dotCall64_1.1-1            
## [141] patchwork_1.2.0             Rhdf5lib_1.25.1            
## [143] munsell_0.5.0               Rcpp_1.0.12                
## [145] reticulate_1.35.0           TrajectoryUtils_1.11.1     
## [147] stringi_1.8.3               zlibbioc_1.49.0            
## [149] MASS_7.3-60.2               plyr_1.8.9                 
## [151] parallel_4.4.0              listenv_0.9.1              
## [153] ggrepel_0.9.5               deldir_2.0-2               
## [155] Biostrings_2.71.2           splines_4.4.0              
## [157] tensor_1.5                  circlize_0.4.16            
## [159] igraph_2.0.2                spatstat.geom_3.2-8        
## [161] RcppHNSW_0.6.0              reshape2_1.4.4             
## [163] stats4_4.4.0                XML_3.99-0.16.1            
## [165] evaluate_0.23               BiocManager_1.30.22        
## [167] foreach_1.5.2               tweenr_2.0.2               
## [169] httpuv_1.6.14               RANN_2.6.1                 
## [171] tidyr_1.3.1                 openssl_2.1.1              
## [173] purrr_1.0.2                 polyclip_1.10-6            
## [175] future_1.33.1               clue_0.3-65                
## [177] scattermore_1.2             ggplot2_3.4.4              
## [179] ggforce_0.4.2               xtable_1.8-4               
## [181] restfulr_0.0.15             RSpectra_0.16-1            
## [183] later_1.3.2                 viridisLite_0.4.2          
## [185] tibble_3.2.1                memoise_2.0.1              
## [187] GenomicAlignments_1.39.4    IRanges_2.37.1             
## [189] cluster_2.1.6               sortable_0.5.0             
## [191] timechange_0.3.0            globals_0.16.2

References

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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).

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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).

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28. Feng, D., Whitehurst, C. E., Shan, D., Hill, J. D. & Yue, Y. G. Single cell explorer, collaboration-driven tools to leverage large-scale single cell rna-seq data. BMC genomics 20, 1–8 (2019).

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