OpDEA is a R shiny application for:
- presenting results of our benchmarking of proteomics data differential expression analysis workflow;
- guiding users to select optimal workflows for analyzing their proteomics data;
- For analyzing the user's proteomics data;
- providing links for downloading datasets used for benchmarking and for downloading our results.
We prepared a freely-accessible webserver for helping users to use OpDEA without installation of the package, see http://www.ai4pro.tech:3838/. The webserver requires uploading expression data, so we recommend using our standalone toolkit (available at: https://doi.org/10.5281/zenodo.10958381, no R package needs to be installed and no need to upload data, just decompress it and use it) instead. If you still hope to try our R package, please see following installation instructions.
You should install the following packages with the same versionor higher:
R base: R-4.2.0; R packages: shiny 1.8.0; shinydashboard 0.7.2; threejs 0.3.3; DT 0.31; ggplot2 3.4.4; reshape2 1.4.4; ggpubr 0.6.0; ggsci 3.0.0; readxl 1.4.3; ggalluvial 0.12.5; golem 0.4.1; iq 1.9.12; dplyr 1.1.4; aggregation 1.0.1; stringr 1.5.1; tidyverse 2.0.0; matrixStats 1.2.0; readr 2.1.4; rrcovNA 0.5.0; BiocManager 1.30.22; NormalyzerDE 1.16.0; limma 3.54.2; ROTS 1.26.0; MSnbase 2.24.2; edgeR 3.40.2; proDA 1.12.0; DEqMS 1.16.0; plgem 1.70.0; DEP 1.20.0; MSstats 4.6.5; samr 3.0.0; mice 3.16.0; missForest 1.5; SeqKnn 1.0.1; GMSimpute 0.0.1.0 (see https://github.com/wangshisheng/NAguideR)
The whole R session of my R environment are as follows:
R version 4.2.0 (2022-04-22 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 22631)
Matrix products: default
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] shinydashboard_0.7.2 shiny_1.8.0
loaded via a namespace (and not attached):
[1] circlize_0.4.15 shape_1.4.6 tidyselect_1.2.0 bslib_0.6.1
[5] purrr_1.0.2 colorspace_2.1-0 vctrs_0.6.5 generics_0.1.3
[9] htmltools_0.5.7 yaml_2.3.8 base64enc_0.1-3 utf8_1.2.4
[13] rlang_1.1.2 jquerylib_0.1.4 later_1.3.2 pillar_1.9.0
[17] glue_1.6.2 withr_2.5.2 lifecycle_1.0.4 munsell_0.5.0
[21] gtable_0.3.4 ragg_1.2.7 fontawesome_0.5.2 ggalluvial_0.12.5
[25] htmlwidgets_1.6.4 GlobalOptions_0.1.2 memoise_2.0.1 labeling_0.4.3
[29] fastmap_1.1.1 golem_0.4.1 httpuv_1.6.13 fansi_1.0.6
[33] Rcpp_1.0.11 xtable_1.8-4 promises_1.2.1 scales_1.3.0
[37] DT_0.31 cachem_1.0.8 jsonlite_1.8.8 config_0.3.2
[41] farver_2.1.1 mime_0.12 systemfonts_1.0.5 textshaping_0.3.7
[45] ggplot2_3.4.4 digest_0.6.33 dplyr_1.1.4 grid_4.2.0
[49] OpDEA_0.0.0.9000 cli_3.6.2 tools_4.2.0 magrittr_2.0.3
[53] sass_0.4.8 tibble_3.2.1 tidyr_1.3.0 pkgconfig_2.0.3
[57] ellipsis_0.3.2 rsconnect_1.2.0 attempt_0.3.1 rstudioapi_0.15.0
[61] R6_2.5.1 compiler_4.2.0
It can be installed via two ways:
-
Install the package "devtools" if you have not installed it before
if(!requireNamespace("devtools")){ install.packages("devtools") }
Then, the package can be installed from github via the following code:
library(devtools)
install_github('PennHui2016/OpDEA')
2.Or via downloading "OpDEA_0.0.0.9000.tar.gz" from this site, then installed with the following command:
install.packages(pkgs = '~/OpDEA_0.0.0.9000.tar.gz', repos = NULL, type = "source")
At last, the shiny app can be launched via:
OpDEA::run_app()
If success, the page showing the introduction of our OpDEA will be presented. It can be used according to the contents in the help page.
The python and R source codes for benchmarking proteomics data differential expression analysis workflows are located in the folder "codes_DEA_benchmarking". Please read the "README" file inside the "codes_DEA_benchmarking" folder to find how to reproduce our benchmarking results.
The R codes for regenerating our figures in our paper can be found in the folder "code_generate_figures". Please read the "README" file inside the "code_generate_figures" folder to find how to regenerate our figures.
Hui Peng, He Wang, Weijia Kong, Jinyan Li*, Wilson Wen Bin Goh*. (2024). Optimizing Differential Expression Analysis for Proteomics Data via High-Performing Rules and Ensemble Inference.
Any problems or requesting source codes for reproducing results in our paper please contact
Hui Peng: hui.peng@ntu.edu.sg; Wilson Wen Bin Goh: wilsongoh@ntu.edu.sg