A deep learning-based classification and prediction system for deciphering the characteristics of clinical protome raw mass spectrometry data
MSpectraAI is a free, user-friendly and comprehensive software for mining and classifying raw LC-MS2-based proteomics or metabolomics data of different samples using deep learning models. Users can also built your own deep neural network model in this software. It is developed with R and an example is shown here: https://www.omicsolution.org/wukong/MSpectraAI/.
- Run this tool locally. As we know, the raw data from mass spectrometer are usually very large. You can analyze your data on our web server, but the analysis speed will be slower. Therefore, we recomand you run this tool locally on a high configuration computer. The entire analysis time will be much shorter.
- Be familiar with the basic usage of R language. This web tool is developed with R, therefore, if you know some basic knowledge about R, it will help you understand this tool better. However, you need not worry if you know nothing about R, and you can learn to use our tool expertly as well after reading our manual.
- Install R. You can download R from here: https://www.r-project.org/.
- Install RStudio (Recommendatory but not necessary). You can download RStudio from here: https://www.rstudio.com/.
- Install Anaconda (For Windows users). You can download Anaconda from here: https://www.anaconda.com/download/.
We recommend the R version >= 3.5.0. Particularly, if you use unix-like systems, you may need install some dependent packages in advance, for example, on CentOS 7:
sudo yum -y install libxml2-devel igraph-devel libxslt-devel netcdf-devel libcurl-devel openssl-devel cairo-devel
pip install virtualenv#Packages
needpackages<-c("BiocManager","devtools","shiny","shinyjs","shinyBS","ggplot2","ggjoy","openxlsx","gdata","DT","gtools","ggsci","mzR",
"plyr","tidyr","abind","data.table","parallel","ggrastr","ggthemes","viridis","glue","ComplexHeatmap",
"reshape","impute","circlize","ROCR","keras")
#Check and install function
CheckInstallFunc <- function(x){
for(i in x){
# require returns TRUE invisibly if it was able to load package
if(!require(i, character.only = TRUE)){
# If package was not able to be loaded then re-install
install.packages(i, dependencies = TRUE)
if(!require(i, character.only = TRUE)) BiocManager::install(i, dependencies = TRUE)
if(i=="ggrastr"){
devtools::install_github('VPetukhov/ggrastr')
}
}
}
}
#Start to check and install
CheckInstallFunc(needpackages)
#R interface to Keras: https://keras.rstudio.com/
library(keras)
install_keras()You can download our tool from this github and unzip the file, then run:
#Find the file path and run
library(shiny)
runApp(".../MSpectraAI")Then you can start your own analysis:
The usage about this tool can be found here: https://github.com/wangshisheng/MSpectraAI/blob/master/SupportingNotes.pdf.
> sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 7 x64 (build 7601) Service Pack 1
Matrix products: default
locale:
[1] LC_COLLATE=Chinese (Simplified)_People's Republic of China.936 LC_CTYPE=Chinese (Simplified)_People's Republic of China.936
[3] LC_MONETARY=Chinese (Simplified)_People's Republic of China.936 LC_NUMERIC=C
[5] LC_TIME=Chinese (Simplified)_People's Republic of China.936
attached base packages:
[1] grid parallel stats graphics grDevices utils datasets methods base
other attached packages:
[1] keras_2.1.6 ROCR_1.0-7 gplots_3.0.1 circlize_0.4.4 ComplexHeatmap_1.18.1
[6] glue_1.3.0 viridis_0.5.1 viridisLite_0.3.0 ggthemes_4.0.0 ggrastr_0.1.5
[11] data.table_1.11.8 abind_1.4-5 tidyr_0.8.2 plyr_1.8.4 impute_1.53.0
[16] mzR_2.13.6 Rcpp_0.12.19 ggsci_2.8 gtools_3.5.0 DT_0.4
[21] gdata_2.18.0 openxlsx_4.0.17 ggjoy_0.4.1 ggridges_0.5.0 ggplot2_3.1.0
[26] shinyBS_0.61 shinyjs_1.0 shiny_1.2.0
loaded via a namespace (and not attached):
[1] ProtGenerics_1.11.0 bitops_1.0-6 RColorBrewer_1.1-2 tools_3.5.0 R6_2.2.2 KernSmooth_2.23-15
[7] lazyeval_0.2.1 BiocGenerics_0.26.0 colorspace_1.3-2 GetoptLong_0.1.7 withr_2.1.2 tidyselect_0.2.5
[13] gridExtra_2.3 compiler_3.5.0 Biobase_2.39.2 Cairo_1.5-9 labeling_0.3 caTools_1.17.1.1
[19] scales_1.0.0 tfruns_1.3 stringr_1.3.1 digest_0.6.18 base64enc_0.1-3 pkgconfig_2.0.1
[25] htmltools_0.3.6 htmlwidgets_1.3 rlang_0.3.0.1 GlobalOptions_0.1.0 rstudioapi_0.7 shape_1.4.4
[31] bindr_0.1.1 jsonlite_1.5 tensorflow_1.8 crosstalk_1.0.0 dplyr_0.7.7 magrittr_1.5
[37] Matrix_1.2-14 munsell_0.5.0 reticulate_1.9 stringi_1.1.7 whisker_0.3-2 yaml_2.1.19
[43] promises_1.0.1 crayon_1.3.4 lattice_0.20-35 zeallot_0.1.0 pillar_1.2.1 rjson_0.2.19
[49] codetools_0.2-15 httpuv_1.4.4.1 gtable_0.2.0 purrr_0.2.4.9000 reshape_0.8.7 assertthat_0.2.0
[55] mime_0.5Wang, S., Zhu, H., Zhou, H. et al. MSpectraAI: a powerful platform for deciphering proteome profiling of multi-tumor mass spectrometry data by using deep neural networks. BMC Bioinformatics 21, 439 (2020). https://doi.org/10.1186/s12859-020-03783-0.
You could push an issue on this github. And optionally, please feel free to sent me an e-mail if you have any question or find a bug about this tool. Thank you^_^ Email: wssdandan2009@outlook.com