How does this shiny application work?


Find a brief data overview under the 'Data presentation' tab


Find the actual data under the 'Expression data' tab

Please be patient

The web server is limited, therefore some graphics can take a little while to appear on your screen.

You have R installed on your computer and you want this app to be quicker

Go to the 'Download the app' tab for instructions.

You use data or code from this app in your research

Please cite the paper on the 'References' tab.

You like this app

Share it
Use the 'Contact' tab to tell me

A brief overview of the differentiation protocol

The list of available samples

How to run this app from your own computer?



First method: run from GitHub - no need to download anything

require(shiny)
runGitHub("Muscle_DMD_Omics", "VirginieMournetas")



Second method: run from local folder

require(shiny)
setwd("/home/Downloads") #set the directory where is the app folder on your computer
runApp("Muscle-DMD-Omics.app")



If you have any issue, please contact shiny@virginie-mournetas.fr



sessionInfo()
## R version 3.6.3 (2020-02-29)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.5 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1
## 
## 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       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] knitr_1.28               Seurat_3.2.2             httr_1.4.1              
##  [4] jsonlite_1.6.1           data.table_1.12.8        FactoMineR_2.3          
##  [7] factoextra_1.0.7         scales_1.1.0             plotly_4.9.2.1          
## [10] ggplot2_3.3.0            htmltools_0.4.0          shinyFiles_0.8.0        
## [13] fs_1.4.1                 DBI_1.1.0                DT_0.13                 
## [16] stringr_1.4.0            shinyjs_1.1              shinyBS_0.61            
## [19] bsplus_0.1.1             shinyWidgets_0.5.1       shinydashboardPlus_0.7.0
## [22] shinydashboard_0.7.1     sctransform_0.3.1        shiny_1.4.0.2           
## 
## loaded via a namespace (and not attached):
##  [1] Rtsne_0.15            colorspace_1.4-1      deldir_0.2-3         
##  [4] ellipsis_0.3.0        ggridges_0.5.2        markdown_1.1         
##  [7] spatstat.data_1.5-2   leiden_0.3.5          listenv_0.8.0        
## [10] ggrepel_0.8.2         fansi_0.4.1           lubridate_1.7.8      
## [13] codetools_0.2-16      splines_3.6.3         leaps_3.1            
## [16] polyclip_1.10-0       ica_1.0-2             cluster_2.1.0        
## [19] png_0.1-7             uwot_0.1.9            compiler_3.6.3       
## [22] assertthat_0.2.1      Matrix_1.2-18         fastmap_1.0.1        
## [25] lazyeval_0.2.2        cli_2.0.2             later_1.0.0          
## [28] tools_3.6.3           rsvd_1.0.3            igraph_1.2.5         
## [31] gtable_0.3.0          glue_1.4.0            RANN_2.6.1           
## [34] reshape2_1.4.4        dplyr_1.0.2           Rcpp_1.0.5           
## [37] spatstat_1.64-1       vctrs_0.3.5           nlme_3.1-149         
## [40] crosstalk_1.1.0.1     lmtest_0.9-38         xfun_0.12            
## [43] globals_0.12.5        mime_0.9              miniUI_0.1.1.1       
## [46] lifecycle_0.2.0       irlba_2.3.3           goftest_1.2-2        
## [49] future_1.17.0         MASS_7.3-53           zoo_1.8-8            
## [52] promises_1.1.0        spatstat.utils_1.17-0 parallel_3.6.3       
## [55] RColorBrewer_1.1-2    yaml_2.2.1            reticulate_1.18      
## [58] pbapply_1.4-3         gridExtra_2.3         rpart_4.1-15         
## [61] stringi_1.4.6         rlang_0.4.9           pkgconfig_2.0.3      
## [64] matrixStats_0.57.0    evaluate_0.14         lattice_0.20-41      
## [67] ROCR_1.0-11           purrr_0.3.3           tensor_1.5           
## [70] patchwork_1.1.0       htmlwidgets_1.5.1     cowplot_1.1.0        
## [73] tidyselect_1.1.0      RcppAnnoy_0.0.17      plyr_1.8.6           
## [76] magrittr_1.5          R6_2.4.1              generics_0.0.2       
## [79] pillar_1.4.3          withr_2.1.2           mgcv_1.8-33          
## [82] fitdistrplus_1.1-1    survival_3.2-7        scatterplot3d_0.3-41 
## [85] abind_1.4-5           tibble_3.0.0          future.apply_1.6.0   
## [88] crayon_1.3.4          KernSmooth_2.23-17    grid_3.6.3           
## [91] digest_0.6.25         flashClust_1.01-2     xtable_1.8-4         
## [94] tidyr_1.0.2           httpuv_1.5.2          munsell_0.5.0        
## [97] viridisLite_0.3.0



You use data or code from this app in your research (licensed under GNU General Public License v3.0) - Please cite the following paper:


Myogenesis modelled by human pluripotent stem cells: a multi‐omic study of Duchenne myopathy early onset, Mournetas et al., JCSM, 2021

Related publications:


Preprints

1

Other

1

You like this app

Share it & Tell me

By email

shiny@virginie-mournetas.fr

Through social media


Website

Have a look at my website virginie-mournetas.fr

Last update: 15/02/21 by Virginie Mournetas
Licensed under GNU General Public License v3.0