1 Overview

ADAMgui is an Graphical User Interface for the ADAM package which is GSEA R package created to group a set of genes from comparative samples (control versus experiment) belonging to different species according to their respective functions (Gene Ontology and KEGG pathways as default) and show their significance by calculating p-values referring to gene diversity and activity (Castro et al. (2009)). Each group of genes is called GFAG (Group of Functionally Associated Genes).

The ADAM package was constituted by an statistical class, all genes present in the expression data are grouped by their respective functions according to the domains described by AnalysisDomain argument. The relationship between genes and functions are made based on the species annotation package. If there is no annotation package, a three column file (gene, function and function description) must be provided. For each GFAG, gene diversity and activity in each sample are calculated. As the package always compare two samples (control versus experiment), relative gene diversity and activity for each GFAG are calculated. Using bootstrap method, for each GFAG, according to relative gene diversity and activity, two p-values are calculated. The p-values are then corrected, according to the correction method defined by PCorrectionMethod argument, generating a q-value (Molan (2018)). The significative GFAGs will be those whoose q-value stay under the cutoff set by PCorrection argument. Optionally, it’s possible to run Wilcoxon test and/or Fisher’s exact test (Fontoura and Mombach (2016)). These tests also provide a corrected p-value, and siginificative groups can be seen through them.

The ADAMgui package provides a graphical class so the outputs of the statistical functions in the ADAM package can be studied through different plots. It’s possible, for example, to choose a specific GFAG and observe the gene expression behavior within it. Features as differential expression and fold change can be easily seen.

2 Package Download and Installation

Download and installation of the package can be done with Bioconductor plataform via BiocManager package, as the code chunk shows:

if (!requireNamespace("BiocManager", quietly = TRUE)){
    install.packages("BiocManager") 
    BiocManager::install("ADAMgui")}

Download and installation of the development version of the package can be done from the package’s GitHub repositories as follows:

BiocManager::install('jrybarczyk/ADAMgui')

After download and installation of the desired version of the ADAMgui package, it can be loaded:

library(ADAMgui)

3 Graphical User Interface (GUI)

The ADAMgui package contains 2 graphical modules for visualization and analysis of the GFAGAnalysis output file. These modules are shiny-generated (GUI)s that allows the user to create and download plots in .eps format. The functions that launches those applications are: GFAGpathUi() and GFAGtargetUi(). The sections below contains more information about both applications.

library(ADAMgui)
data("ResultAnalysisAedes")

library(ADAMgui)
data("ResultAnalysisAedes")
dt<-ResultAnalysisAedes[1:10,]

write.table(dt,'ResultAnalysisAedes.txt',sep='\t',quote = F,
    row.names = F,col.names = T)

library(ADAMgui)

 GFAGpathUi(TRUE)  #Run the app in your default browser.
 GFAGpathUi(FALSE) #Run the app in R (your local machine).

library(ADAM)
library(ADAMgui)

data("ResultAnalysisAedes") # GFAG Output data

data("ExpressionAedes") # target expression data

data("GeneFunctionAedes") # Path-to-Target relationship data

data("DiffAedes") # target differential expression

# save the GFAG output file
write.table(dt,'ResultAnalysisAedes.txt',sep='\t',quote = F,
    row.names = F,col.names = T)

# save the target expression file
write.table(dt,'ExpressionAedes.txt',sep='\t',quote = F,
    row.names = F,col.names = T)

# save the Path-to-Target relationship file
write.table(dt,'GeneFunctionAedes.txt',sep='\t',quote = F,
    row.names = F,col.names = T)

# save the target differential expression file
write.table(dt,'DiffAedes.txt',sep='\t',quote = F,
    row.names = F,col.names = T)

library(ADAM)
library(ADAMgui)

GFAGtargetUi(TRUE)  #Run the app in your default browser.
GFAGtargetUi(FALSE) #Run the app in R (your local machine).

4 Session Info

## R version 3.6.0 (2019-04-26)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server 2012 R2 x64 (build 9600)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=C                          
## [2] LC_CTYPE=English_United States.1252   
## [3] LC_MONETARY=English_United States.1252
## [4] LC_NUMERIC=C                          
## [5] LC_TIME=English_United States.1252    
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] ADAMgui_1.0.0    ADAM_1.0.0       BiocStyle_2.12.0
## 
## loaded via a namespace (and not attached):
##  [1] Biobase_2.44.0              httr_1.4.0                 
##  [3] bit64_0.9-7                 shiny_1.3.2                
##  [5] assertthat_0.2.1            BiocManager_1.30.4         
##  [7] stats4_3.6.0                blob_1.1.1                 
##  [9] GenomeInfoDbData_1.2.1      yaml_2.2.0                 
## [11] ggrepel_0.8.0               pillar_1.3.1               
## [13] RSQLite_2.1.1               lattice_0.20-38            
## [15] glue_1.3.1                  digest_0.6.18              
## [17] promises_1.0.1              GenomicRanges_1.36.0       
## [19] RColorBrewer_1.1-2          XVector_0.24.0             
## [21] ggsignif_0.5.0              colorspace_1.4-1           
## [23] httpuv_1.5.1                htmltools_0.3.6            
## [25] Matrix_1.2-17               plyr_1.8.4                 
## [27] pkgconfig_2.0.2             bookdown_0.9               
## [29] zlibbioc_1.30.0             xtable_1.8-4               
## [31] purrr_0.3.2                 GO.db_3.8.2                
## [33] scales_1.0.0                later_0.8.0                
## [35] BiocParallel_1.18.0         tibble_2.1.1               
## [37] KEGGREST_1.24.0             IRanges_2.18.0             
## [39] ggplot2_3.1.1               ggpubr_0.2                 
## [41] DT_0.5                      shinyjs_1.0                
## [43] SummarizedExperiment_1.14.0 pbapply_1.4-0              
## [45] BiocGenerics_0.30.0         lazyeval_0.2.2             
## [47] mime_0.6                    magrittr_1.5               
## [49] crayon_1.3.4                memoise_1.1.0              
## [51] evaluate_0.13               data.table_1.12.2          
## [53] tools_3.6.0                 matrixStats_0.54.0         
## [55] stringr_1.4.0               S4Vectors_0.22.0           
## [57] munsell_0.5.0               DelayedArray_0.10.0        
## [59] AnnotationDbi_1.46.0        Biostrings_2.52.0          
## [61] colorRamps_2.3              compiler_3.6.0             
## [63] GenomeInfoDb_1.20.0         rlang_0.3.4                
## [65] grid_3.6.0                  RCurl_1.95-4.12            
## [67] htmlwidgets_1.3             bitops_1.0-6               
## [69] rmarkdown_1.12              testthat_2.1.1             
## [71] gtable_0.3.0                DBI_1.0.0                  
## [73] reshape2_1.4.3              R6_2.4.0                   
## [75] gridExtra_2.3               knitr_1.22                 
## [77] dplyr_0.8.0.1               varhandle_2.0.3            
## [79] bit_1.1-14                  stringi_1.4.3              
## [81] parallel_3.6.0              Rcpp_1.0.1                 
## [83] png_0.1-7                   tidyselect_0.2.5           
## [85] xfun_0.6