TRNValStandVis

An R package for validation, standardization and visualization of transcription factor - target gene interactions.

Description

TRNValStandVis is an R package that visualizes, validates and standardizes Transcription Factor(TF) - Target Genes(TG) interactions and Transcription Regulatory Networks(TRN), in regards to representation, and nomenclatures as well.

It combines transcription factor - target gene interaction data from four data sets, the Molecular Signatures Database (MSigDB) by GSEA, hTFtarget by Zhang et al., the ChIP-X Enrichment Analysis Version 3 (ChEA3) by Ma’ayan lab, and TFLink by Liska et al. to create a single combined dataset, where all TF’s and TG’s are standardized to be HUGO Gene Nomenclature Committee (HGNC) compliant. The combined data was used to create a qualitative score to evaluate the quality of Transcription Factor - Target Gene (TF-TG) interactions as well a score to assess the overall quality of a specific TF. Further abilities include visualizing TF-TG regulons and networks and comparing different TF’s qualitatively. This package will allow for the validation of TF-TG’s across different sources, quality metrics in regards to level of validation for each TF-TG, as well as standardization in regards to nomenclature. The TRNValStandVis package was developed using R version 4.1.1 (2021-08-10), Platform: x86_64-apple-darwin17.0 (64-bit) and Running under: macOS Big Sur 11.6

Installation

In order to install the latest version of this package:

install.packages("devtools")
library("devtools")
devtools::install_github("Mogtaba-Alim/TRNValStandVis", build_vignettes = TRUE)
library("TRNValStandVis")

Overview

ls("package:TRNValStandVis")
data(package = "TRNValStandVis")
browseVignettes("TRNValStandVis")

TRNValStandVis consists of 8 functions.

1. get_TGs

• Take in a TF and return all its target genes. Can be adjusted based on number of datasets TF-TG appears in.

2. get_TFs

• Take in a gene and return all the transcription factors that regulate it. Can be adjusted based on number of datasets TF-TG appears in.

3. plot_TF_regulon

• Take in a gene and plot that gene’s regulon including regulators and targets.

4. plot_TFs_network

• Take in up to 3 gene names and plot the network involving all the genes regulons combined.

5. get_TF_TG_val

• Take in a TF and return the validity score for all the TG’s of that TF

6. plot_TF_TG_val

• Take in a TF and plot the validity score for all the TG’s of that TF

7. get_TF_total_val

• Takes in a TF and provides the average validty score for that TF

8. plot_TF_total_val

• Take in a collection of TF names and plots their average validity score

Below is an example overview of the visualization features available with this package:

plot_TF_regulon

This function plots a graph showing all the TF’s that regulate a given gene and all the TG’s regulated by it.

plot_TF_TG_val

This function plots the target genes against their TE_TG_Exp_qual_score for a specified Transcription Factor (TF).

plot_TF_total_val

This function takes a vector of Transcription Factor (TF) names and plots a graph of their names against their Validation Scores.

plot_TFs_network

This function plots a combined graph showing all the transcription factors (TFs) that regulate the input genes and all the target genes regulated by them.

The package will also have 2 datasets. The first dataset called TF_TG_Valid_Comb will be the combined dataset of TF-TG’s and will contain columns for the number of times an interaction has appeared in the different datasets, the validation score for each TF-TG and a normalized validation score. The validation score is represented by TF_TG_Exp_qual_score in the TF_TG_Valid_Comb dataset and is a qualitative score for each TF-TG interaction based on validation across datasets and in comparison to other TF-TG’s in the combined datasets. Ratio_to_TF_Avg is the normalized score.

Below we see an example of the datset.

The second dataset, will be of all the TF’s and their overall average validation score. This is represented by the Ratio_to_Overall_Avg which is qualitative value that represents the normalized average validation quality of a specific TF over all TF-TG interactions in the combined dataset that involved that TF.

Below we see an example of the dataset.

Contributions

The author of this package is Mogtaba Alim. All functions and processes including the processing and the creation of validity scores were done by the author.

The author wrote the get_TF_TG_val function which presents the validation score for all target genes of a specified transcription factor. The function uses the filter and arrange functions from the dplyr package to sort the data frame based on the values of the TF_TG_Exp_qual_score column and to filter the data frame for values that match the conditions we are looking for. We also used the pipe method from the magrittr package for the chaining of functions.

The author also wrote the get_TF_Total_Val function which presents the Ratio_to_Overall_Avg score for a specified transcription factor from the TF_Avg_Valid dataset. This function used no external packages.

They author also wrote the get_TFs function which presents a vector of transcription factors that regulate a specified target gene. This function used no external packages.

The author wrote the get_TGs function which presented a vector of target genes associated with a specified transcription factor name. This function used no external packages.

The author wrote the plot_TF_regulon function which plots a graph showing all the transcription factors that regulate a given gene and all the target genes regulated by it. The package utilized three functions from the igraph package. Firstly the graph_from_data_frame was used to create a graph object from a data frame, then the V(graph) function was used to access the nodes of the graph object created using the igraph package. Finally the layout_with_fr function was used to create a well spaced layout for the graph, specifically using the Fruchterman-Reingold layout algorithm, which is a force-directed algorithm often used for arranging nodes in a visually pleasing manner.

The author wrote the plot_TF_TG_val function which the target genes against their TF_TG_Exp_qual_score for a specified transcription factor. This function used several functions from the ggplot2 package. Firstly the ggplot function was used to create the plot, with that the aes function is used to define each part of the data represented on the plot in terms of colors, shapes, sizes etc. Furthermore, the geom_point was used to add the points to the plot and the scale_color_viridis_d was used to set the color scale of the plot. The element_text function was used to customize the appearance of text in the plot. Finally the labs function was used to add labels to the plot and the theme was used to customize these labels.

The author wrote the plot_TF_Total_Val function which takes a vector of transcription factor names and plots a graph of their names against their validation scores represented by the Ratio_to_Overall_Avg column. The function utilized several functions from the ggplot2 package. Firstly the ggplot function was used to create the plot, with that the aes function is used to define each part of the data represented on the plot in terms of colors, shapes, sizes etc. Furthermore, the geom_point was used to add the points to the plot and the theme_minimal function was used to apply a minimalist theme to the plot. The element_text function was used to customize the appearance of text in the plot. Finally the labs function was used to add labels to the plot and the theme was used to customize these labels.

The author also wrote the plot_TFs_network function which plots a combined graph showing all the transcription factors that regulate the input genes and all the target genes regulated by them. The function uses two functions from the igraph package. Firstly the graph_from_data_frame was used to create a graph object from a data frame, then the V(graph) function was used to access the nodes of the graph object created using the igraph package.

Other packages used outside functions include the testthat package which was used to test the functions of the package. Furthermore, the knittr package was used to parse the vingettes file along with the rmarkdown package.

No other aids were used in the generation of this package.

References

• Allaire J, Xie Y, Dervieux C, McPherson J, Luraschi J, Ushey K, Atkins A, Wickham H, Cheng J, Chang W, Iannone R (2023). rmarkdown: Dynamic Documents for R. R package version 2.25, https://github.com/rstudio/rmarkdown.

• Bache S, Wickham H (2022). magrittr: A Forward-Pipe Operator for R. R package version 2.0.3, https://CRAN.R-project.org/package=magrittr.

• Chang W, Cheng J, Allaire J, Sievert C, Schloerke B, Xie Y, Allen J, McPherson J, Dipert A, Borges B (2023). shiny: Web Application Framework for R. R package version 1.8.0.9000, https://github.com/rstudio/shiny, https://shiny.posit.co/.

• Csardi G, Nepusz T (2006). “The igraph software package for complex network research.” InterJournal, Complex Systems, 1695. https://igraph.org.

• Fujiyoshi, K., Bruford, E. A., Mroz, P., Sims, C. L., O’Leary, T. J., Lo, A. W. I., … Ogino, S. (2021). Standardizing gene product nomenclature—a call to action. Proceedings of the National Academy of Sciences of the United States of America, 118(3). doi:10.1073/pnas.2025207118

• H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016. https://ggplot2.tidyverse.org

• Lachmann, A., Xu, H., Krishnan, J., Berger, S. I., Mazloom, A. R., & Ma’ayan, A. (2010). ChEA: transcription factor regulation inferred from integrating genome-wide ChIP-X experiments. Bioinformatics (Oxford, England), 26(19), 2438–2444. doi:10.1093/bioinformatics/btq466

• Liberzon, A., Birger, C., Thorvaldsdóttir, H., Ghandi, M., Mesirov, J. P., & Tamayo, P. (2015). The molecular signatures database hallmark gene set collection. Cell Systems, 1(6), 417–425. doi:10.1016/j.cels.2015.12.004

• Liska, O., Bohár, B., Hidas, A., Korcsmáros, T., Papp, B., Fazekas, D., & Ari, E. (2022). TFLink: an integrated gateway to access transcription factor–target gene interactions for multiple species. Database: The Journal of Biological Databases and Curation, 2022, baac083. doi:10.1093/database/baac083

• R Core Team (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/

• Wickham H (2011). “testthat: Get Started with Testing.” The R Journal, 3, 5-10. https://journal.r-project.org/archive/2011-1/RJournal_2011-1_Wickham.pdf.

• Wickham H, François R, Henry L, Müller K, Vaughan D (2023). dplyr: A Grammar of Data Manipulation. R package version 1.1.4, https://CRAN.R-project.org/package=dplyr.

• Xie Y (2023). knitr: A General-Purpose Package for Dynamic Report Generation in R. R package version 1.45, https://yihui.org/knitr/.

• Zhang, Q., Liu, W., Zhang, H.-M., Xie, G.-Y., Miao, Y.-R., Xia, M., & Guo, A.-Y. (2020). HTFtarget: A comprehensive database for regulations of human transcription factors and their targets. Genomics, Proteomics & Bioinformatics, 18(2), 120–128. doi:10.1016/j.gpb.2019.09.006

Acknowledgements

This package was developed as part of an assessment for 2023 BCB410H: Applied Bioinformat- ics course at the University of Toronto, Toronto, CANADA. TRNValStandVis welcomes issues, enhancement requests, and other contributions. To submit an issue, use the GitHub issues.

Package Structure

.
├── DESCRIPTION
├── LICENSE
├── LICENSE.md
├── Meta
│   └── vignette.rds
├── NAMESPACE
├── R
│   ├── data.R
│   ├── get_TF_TG_val.R
│   ├── get_TF_Total_Val.R
│   ├── get_TFs.R
│   ├── get_TGs.R
│   ├── plot_TF_TG_val.R
│   ├── plot_TF_Total_Val.R
│   ├── plot_TF_regulon.R
│   ├── plot_TFs_network.R
│   └── runTRNValStandVis.R
├── README.Rmd
├── README.md
├── TRNValStandVis.Rproj
├── data
│   ├── TF_Avg_Valid.rda
│   ├── TF_TG_Valid_Comb.rda
├── doc
│   ├── TRNValStandVisVignettes.R
│   ├── TRNValStandVisVignettes.Rmd
│   └── TRNValStandVisVignettes.html
├── inst
│   ├── CITATION
│   ├── extdata
│   │   ├── TF_Avg_Valid_Example.png
│   │   ├── TF_TG_Valid_Comb_Example.png
│   │   ├── get_TF_TG_val_Example_1.png
│   │   ├── get_TF_TG_val_Example_2.png
│   │   ├── get_TF_TG_val_Example_3.png
│   │   ├── get_TFs_Example_1.png
│   │   ├── get_TFs_Example_2.png
│   │   ├── get_TFs_Example_3.png
│   │   ├── get_TGs_Example_1.png
│   │   ├── get_TGs_Example_2.png
│   │   ├── get_TGs_Example_3.png
│   │   ├── plot_TF_TG_val_Example.png
│   │   ├── plot_TF_TG_val_Example_1.png
│   │   ├── plot_TF_TG_val_Example_2.png
│   │   ├── plot_TF_TG_val_Example_3.png
│   │   ├── plot_TF_regulon_Example.png
│   │   ├── plot_TF_regulon_Example_1.png
│   │   ├── plot_TF_regulon_Example_2.png
│   │   ├── plot_TF_regulon_Example_3.png
│   │   ├── plot_TF_total_val_Example.png
│   │   ├── plot_TF_total_val_Example_1.png
│   │   ├── plot_TFs_network_Example.png
│   │   ├── plot_TFs_network_Example_1.png
│   │   ├── plot_TFs_network_Example_2.png
│   │   └── plot_TFs_network_Example_3.png
│   └── shiny-scripts
│       ├── app.R
│       └── www
│           ├── TF_Avg_Valid_Example.png
│           ├── TF_TG_Valid_Comb_Example.png
│           ├── get_TF_TG_val_Example_1.png
│           ├── plot_TF_TG_val_Example.png
│           ├── plot_TF_regulon_Example.png
│           ├── plot_TF_total_val_Example.png
│           └── plot_TFs_network_Example.png
├── man
│   ├── TF_Avg_Valid.Rd
│   ├── TF_TG_Valid_Comb.Rd
│   ├── get_TF_TG_val.Rd
│   ├── get_TF_Total_Val.Rd
│   ├── get_TFs.Rd
│   ├── get_TGs.Rd
│   ├── plot_TF_TG_val.Rd
│   ├── plot_TF_regulon.Rd
│   ├── plot_TF_total_val.Rd
│   ├── plot_TFs_network.Rd
│   └── runTRNValStandVis.Rd
├── tests
│   ├── testthat
│   │   ├── _snaps
│   │   ├── test-get_TF_TG_val.R
│   │   ├── test-get_TF_Total_Val.R
│   │   ├── test-get_TFs.R
│   │   ├── test-get_TGs.R
│   │   ├── test-plot_TF_TG_val.R
│   │   ├── test-plot_TF_Total_Val.R
│   │   ├── test-plot_TF_regulon.R
│   │   └── test-plot_TFs_network.R
│   └── testthat.R
└── vignettes
    └── TRNValStandVisVignettes.Rmd