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
- Download and unzip the files: https://nextcloud.virginie-mournetas.ovh/index.php/s/b2GSogTGT6Z8pM5
- Run the app:
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 18.04.6 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_1.4.2
## [4] jsonlite_1.8.0 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_0.5.2 shinyFiles_0.8.0
## [13] fs_1.5.2 DBI_1.1.0 DT_0.13
## [16] stringr_1.4.0 shinyjs_2.1.0 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_1.7.1
##
## loaded via a namespace (and not attached):
## [1] Rtsne_0.15 colorspace_1.4-1 deldir_0.2-3
## [4] ellipsis_0.3.2 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_1.0.3 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] Matrix_1.2-18 fastmap_1.1.0 lazyeval_0.2.2
## [25] cli_3.3.0 later_1.3.0 tools_3.6.3
## [28] rsvd_1.0.3 igraph_1.2.5 gtable_0.3.0
## [31] glue_1.6.2 RANN_2.6.1 reshape2_1.4.4
## [34] dplyr_1.0.2 Rcpp_1.0.8 spatstat_1.64-1
## [37] jquerylib_0.1.4 vctrs_0.4.1 nlme_3.1-149
## [40] crosstalk_1.1.0.1 lmtest_0.9-38 xfun_0.12
## [43] globals_0.12.5 mime_0.12 miniUI_0.1.1.1
## [46] lifecycle_1.0.1 irlba_2.3.3 goftest_1.2-2
## [49] future_1.17.0 MASS_7.3-53 zoo_1.8-8
## [52] promises_1.2.0.1 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 sass_0.4.0
## [61] rpart_4.1-15 stringi_1.4.6 rlang_1.0.2
## [64] pkgconfig_2.0.3 matrixStats_0.57.0 evaluate_0.14
## [67] fontawesome_0.2.2 lattice_0.20-41 ROCR_1.0-11
## [70] purrr_0.3.4 tensor_1.5 patchwork_1.1.0
## [73] htmlwidgets_1.5.4 cowplot_1.1.0 tidyselect_1.1.2
## [76] RcppAnnoy_0.0.17 plyr_1.8.6 magrittr_2.0.3
## [79] R6_2.5.1 generics_0.0.2 pillar_1.7.0
## [82] withr_2.5.0 mgcv_1.8-33 fitdistrplus_1.1-1
## [85] survival_3.2-7 scatterplot3d_0.3-41 abind_1.4-5
## [88] tibble_3.1.7 future.apply_1.6.0 crayon_1.5.1
## [91] KernSmooth_2.23-17 utf8_1.2.2 grid_3.6.3
## [94] digest_0.6.29 flashClust_1.01-2 xtable_1.8-4
## [97] tidyr_1.0.2 httpuv_1.6.5 munsell_0.5.0
## [100] viridisLite_0.3.0 bslib_0.3.1
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
- 2019
-
bioRxiv . DOI:10.1101/720920
V. Mournetas , E. Massouridès, J.-B. Dupont, E. Kornobis, H. Polvèche, M. Jarrige, M. Gosselin, S. D. Garbis, D. C. Górecki, C. Pinset
Other
- 2018
-
Cah. Myol. . doi: 0.1051/myolog/201817016
V. Mournetas , E. Massouridès, E. Kornobis, C. Pinset
You like this app
Share it & Tell me
By email
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