# ADAMgui: Activity and Diversity Analysis Module Graphical User Interface

Giordano B. S. Seco, André L. Molan, Agnes A. S. Takeda, Jose L. Rybarczyk-Filho

#### 3 May 2019

#### Package

ADAMgui 1.0.0

# Contents

# 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.

## 3.1 GFAG Path Viewer Function: GFAGpathUi()

‘Paths’ are essentially group names that contains the targets (eg: Gene Ontologies and KEGG are paths while genes and proteins related to them are targets). The GFAGAnalysis output file contains information about the paths that the user utilized in the analysis and the GFAGpathUi() is a function that launches a shiny based (GUI) that can construct heatmaps for q-values present in the GFAGAnalysis output file. Below there is the layout of an usage example of the application with the GFAGAnalysis output file created with the first 10 entries of the Aedes Aegypt data used in the statistical modules.

To generate the input file ‘ResultAnalysisAedes.txt’ use the commands on R console listed below. First load the data in the package:

    library(ADAMgui)
    data("ResultAnalysisAedes")

Then select the first 10 rows:

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

And save it:

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

Now launch the app, it can be launched in local machine or your default browser:

    library(ADAMgui)

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

- 1 - Tabs
  - Data Input - The first and the only non-disabled Tab upon the application’s launching. As the name implies, it is where the user will choose the input data (GFAGAnalysis output file) and it’s reading parameters.
  - Data Selection - The elements of this Tab will be disabled initially, you need to correctly upload a file in the first tab to enable it’s elements. Here you will be able to select the data to be plotted.
  - Data Formatting - The elements of this Tab will be disabled initially, you need to correctly upload a file in the first tab and then select your data in the second Tab to enable its elements. Here you will be able to format the selected data (select column order, rename columns, etc.).
  - Plot - The elements of this Tab will be disabled initially, you need to correctly upload a file in the first tab, select your data in the second Tab and the format the selected data in the third Tab to enable its elements. Here you will be able to plot the data and download the plot in .eps format.
- 2 - Tab 1: File upload - A search buttom to upload the non-formated raw data (GFAGAnalysis output file)
- 3 - Tab 1: Read Table Parameters - Parameters for reading file uploaded in 2, checks=TRUE.
- 4 - Tab 1: Read Table Separator - Separators for reading file uploaded in 2, checks=TRUE (naturally only one may be selected).
- 5 - Tab 1: File Information - Dinamic message in the main panel that informs the user about the progress in the process of file uploading.
- 6 - Tab 1: Main Panel Tabs - Tabs in the main panel that only appear if a file was correctly uploaded. The ‘About file’ Tab provides basic information about the uploaded file such as name, size and path. The ‘Data’ Tab shows the created data.table according to the uploaded file in 1 and the selected parameters in 3 and 4. The ‘Summary’ Tab shows the output of the summary() function.

- 7 - Tab 2: Select the ID column - Select the ID (Path) column of the uploaded file.
- 8 - Tab 2: Select the P-values/Q-values columns - Select multiple columns corresponding to the p-values or q-values to be plotted.
- 9 - Tab 2: Create Dataframe - Action Buttom to create a data.frame with the selected columns in 7 and 8. Note that you can add columns from multiple files, as long as the ID column is IDENTICAL!
- 10 - Tab 2: Dataframe is ready for plotting - Action button to confirm the creation of the data.frame.

- 11 - Tab 2: Reset Dataframe - Action buttom that resets everything done by the user in Tabs 2,3 and 4.

- 12 - Tab 2: Main Panel - Shows the selected data.frame and the created data.frame.

- 13 - Tab 3: Select Columns Order - Select the desired column order of your created data.frame, this will affect the plot.
- 14 - Tab 3: Re-order dataframe - Action button that reorders the created data.frame according to the selected order in 13. Should 13 be empty the order will not be altered.
- 15 - Tab 3: Select a column to rename - Disabled until you hit 14, this allows the user to select a column to be renamed. Note that renaming columns is purely optional!.
- 16 - Tab 3: Write a name for the selected column - Disabled until you hit 14, this allows the user to input a new name for the column selected in 15. Note that renaming columns is purely optional!.
- 17 - Tab 3: Rename Button - Disabled until you hit 14, this action button renames the column selected in 14 with the name in 15. If 15 is empty, the column in 14 will not be renamed. Note that renaming columns is purely optional!.

- 18 - Tab 3: Select ID/Path column position - Disabled until you hit 14, here you must select what is the current position of your ID/Path column in your currently formated data.frame. The default value is 1, should your ID/Path column be in another position, then change the value accordingly!

- 19 - Tab 3: Dataframe is ready for plotting - Disabled until you hit 14, this action button informs the application that the formating process is finished. Upon hitting the plotting phase will start and the elements in Tab 4 will be enabled. .
- 20 - Tab 3: Main Panel - The main Panel will show the created data.frame in Tab 2 initially, then it will also show the formated data.frame upon any changes done by the user in Tab 3. The main panel also displays a dinamic message that informs the user about progress done in this Tab. .

- 21 - Tab 4: Plot Height - Select the desired height for the plot, it is a reactive input, meaning that the plot will be updated automatically as this value changes. .
- 22 - Tab 4: Plot Width - Select the desired width for the plot, it is a reactive input, meaning that the plot will be updated automatically as this value changes. .
- 23 - Tab 4: Select a font family for plot elements - Select the desired font family for the plot’s elements. Not reactive, you need to click in 29 to re-plot and see the changes. .
- 24 - Tab 4: Select a size for plot elements - Select the desired size for the font of the plot’s elements. Not reactive, you need to click in 29 to re-plot and see the changes. .
- 25 - Tab 4: Select a size for plot legend - Select the desired size for the font of the plot’s legend. Not reactive, you need to click in 29 to re-plot and see the changes. .
- 26 - Tab 4: Select a font face for the axis text - Select the desired font face/style (default=‘plain’,‘bold’,‘italic’,‘bold.italic’) for the font of the plot’s legend. Not reactive, you need to click in 29 to re-plot and see the changes.
- 27 - Tab 4: Select a font face for the legend - Select the desired font face/style (default=‘plain’,‘bold’,‘italic’,‘bold.italic’) for the font of the plot’s legend. Not reactive, you need to click in 29 to re-plot and see the changes.
- 28 - Tab 4: Plot Button - Action button to display the plot with the selected parameters.
- 29 - Tab 4: Download Button - Download buttom, downloads the displayed plot in .eps format. Disabled as long as there is no plot displayed.
- 30 - Tab 4: Main Panel - Displays the plot.

## 3.2 GFAG Target Viewer Function: GFAGtargetUi()

This application was made to further analyse the GFAGs in the GFAGAnalysis output file. These paths can have many targets (genes, proteins) and this application constructs a plot that show the differential expression of the targets in a selected Path. The user needs to input 4 files in order to use this application: the GFAGAnalysis output file, an expression file, a Path-to-Target relationship file and a differential expression file.

Below there is the layout of an usage example of the application with the GFAGAnalysis output file of the Aedes Aegypt data used in the statistical modules. Load the required data first in order to generate the input data:

    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

Now create the files:

    # 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)

The app can be launched in local machine or your default browser:

    library(ADAM)
    library(ADAMgui)

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

- 1 - Tabs
  - File Upload & Case Selection - The first and the only non-disabled Tab upon the application’s launching. As the name implies, it is where the user will choose the input data (GFAGAnalysis output file) it’s reading parameters.
  - GFAG Data Filter - The elements of this Tab will be disabled initially, you need to correctly upload a file in the first tab to enable it’s elements. Here you will be able to filter and select the Paths whose targets will be plotted.
  - Plot - The elements of this Tab will be disabled initially, you need to correctly upload a file in the first tab and select a GFAG in the second Tab to enable it’s elements. Here you will be able to select plotting parameters, construct the plot and download it in .eps format.
- 2 - Tab 1: GFAG Data Initially this the only enabled element. Is it where the user will choose the GFAGAnalysis output file and consequentially the case as well.
- 3 - Tab 1: Target Expression Data . This element is initially disabled, the user needs to correctly upload a GFAGAnalysis output file first to enable it. Here the user will choose the expression file that he used to generate the GFAG output file that was selected.
- 4 - Tab 1: Path-to-Target Data . This element is initially disabled, the user needs to correctly upload a GFAGAnalysis output file and a expression file first. Here the user will choose the Path-to-Target relationship file that can be generated by the statistical modules.
- 5 - Tab 1:Target Differential Expression Data . This element is initially disabled, the user needs to correctly upload a GFAGAnalysis output file, a expression file anda Path-to-Target file first. Here the user will choose the differential expression file of interest. Note that this package does not supports differential expression analysis. The user needs to make the analysis by other means and input the results in the correct format. Because of this, once a file is uploaded a menu containing reading parameters and column selection elements will be displayed in this field. The columns to be selected are respectively: the column the contains the target’s ID, the column that contains the logFC (log Fold Change) values and a the column that contains the p-values or q-values. No control can implemented here, so if the user input the wrong columns in the fields, the resulting plot will be wrong. Should this happen the user needs to reset the application by clicking on 6.
- 6 - Tab 1: Reset Button . This action button can reset the whole application so the user can make a new analysis.
- 7 - Tab 1: Main Panel Tabs .
  - 7 - Tab 1: Main Panel Tab 1-Summary Displays information about the uploaded files such as the number of entries in each file, the number of unique elements in each file, the number of targets that are present in your expression file that are contemplated in the Path-to-Target file, the number of Paths in your GFAGAnalysis output file that are contemplated in the Path-to-Target.
  - 7 - Tab 1: Main Panel Tab 2-Tables Displays data.tables of the uploaded files.
- 8 - Tab 1: Main Panel . Different displays depending on the selected Tab in 7.

- 9 - Tab 2: Select q-value cut-off for ativity - Input a desired q-value cutoff for the ativity.
- 10 - Tab 2: Select q-value cut-off for diversity - Input a desired q-value cutoff for the diversity.
- 11 - Tab 2: Filter Buttons - Action buttons for filtering the uploaded GFAG output file so the user can select a Path. There are 4 buttons: ‘Filter Ativity’, ‘Filter Diversity’, ‘Filter Ativity and Diversity’ and ‘No Filter’, respectively.
- 12 - Tab 2: Select a Path of interest - Select a path of interest and then click on the ‘Update GFAG/Path’ action button to update the value.
- 13 - Tab 2: Start Plot - Click in the action button once a Path has been selected and updated in 12 to start the plotting phase and enable the elements in Tab 3
- 14 - Tab 2: Main Panel - Displays a data.table of the filtered GFAGAnalysis output file.

- 15 - Tab 3: Q-value interval selection - Slider input to select a desired q-value interval between 0 and 1. Used for filtering q-values to plot.

- 16 - Tab 3: LogFC interval selection - Slider input to select a desired logFC interval between -10 and 10. Used for filtering logFCs to plot.

- 17 - Tab 3: Plot Height - Select the desired plot height.

- 18 - Tab 3: Plot Width - Select the desired plot height.

- 19 - Tab 3: Select font family for plot elements - Select the desired font family for the plot’s elements.

- 20 - Tab 3: Select font size for plot elements - Select the desired font size for the plot’s elements.

- 21 - Tab 3: Select font size for plot title - Select the desired font size for the plot’s title.

- 22 - Tab 3: Select font size for axis text - Select the desired font size for the axis text.

- 23 - Tab 3: Select font size for axis title - Select the desired font size for the axis title.

- 24 - Tab 3: Select font face for axis text - Select the desired font face/style for the axis text.

- 25 - Tab 3: Select font face for axis title - Select the desired font face/style for the axis title.

- 26 - Tab 3: Select font face for plot title - Select the desired font face/style for the plot title.

- 27 - Tab 3: Plot Buttons - Buttons to create a plot with the selected data and parameters and display it on the main panel. There are 4 tipes of plots: ‘Plot with filtered LogFC values’, ‘Plot with filtered q.values’, ‘Plot with filtered q.values and LogFC values’ and ‘Plot with no filter’.

- 28 - Tab 3: Download Plot - Download buttom for downloading the displayed plot in .eps format. Disabled as long as there is no plot displayed on the main panel.

- 29 - Tab 3: Main Panel - Display the plot.

# 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

# References

Castro, Mauro AA, José L Rybarczyk Filho, Rodrigo JS Dalmolin, Marialva Sinigaglia, José CF Moreira, José CM Mombach, and Rita MC de Almeida. 2009. “ViaComplex: Software for Landscape Analysis of Gene Expression Networks in Genomic Context.” Bioinformatics 25 (11). Oxford University Press:1468–9.

Fontoura, C.A.R.S., and J.C.M Mombach. 2016. “PATHChange: A Tool for Identification of Differentially Expressed Pathways Using Multi-Statistic Comparison.” https://cran.r-project.org/web/packages/PATHChange/PATHChange.pdf.

Molan, A. L. 2018. “Construction of a Tool for Multispecies Genic Functional Enrichment Analysis Among Comparative Samples.” Master’s thesis, Institute of Biosciences of Botucatu – Univ. Estadual Paulista. http://hdl.handle.net/11449/157105.