IntegraTRN

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Description

The R package IntegraTRN integrates transcriptomic, small RNAomic, proteomic, and epigenomic data to construct a transcriptional regulatory network (TRN) underlying the gene expression changes in a developmental or disease (or any continuous/binary) biological context. In particular, the TRN is a network of interacting factors, in which one element can exert a regulatory effect (activating or inhibitory) on the expression of one or more elements. Given the complex nature of transcriptional regulation, the package reveals the key players of such regulation, including transcriptional factors (TFs) and small RNAs. Since gene/protein expression changes are usually the most direct molecular cause of observed phenotypic alterations, the core TRN deciphers changes between two or more conditions and infers the upstream regulatory factors that mediate such changes. The analysis pipeline provided by this package is primarily composed of a two-step process: (1) elucidating the transcriptional and regulatory changes that take place during a biological process, such as development or disease; and (2) performing correlational analysis with rigorous filtering based on biological data to identify the regulatory interactions that are responsible for the observed changes.

The package is developed under the following environment:

• R version 4.3.1 (2023-06-16 ucrt)
• Platform: x86_64-w64-mingw32/x64 (64-bit)
• Running under: Windows 11 x64 (build 22621)

Installation

To install the latest developmental version of the package:

require("devtools")
devtools::install_github("j-y26/IntegraTRN", build_vignettes = TRUE)
library("IntegraTRN")

To run the shinyApp:

runIntegraTRN()

A pre-built docker image based on Bioconductor release 3.18 is also available for one-step installation. To install the docker image, ensure that Docker Desktop has been installed on your computer. Then, run the following command in the terminal to setup and run the container:

docker run -e PASSWORD=changeit -v ${pwd}:/home/rstudio/projects -p 8787:8787 jyang26/integra_trn:v0.1.0

In a browser, navigate to localhost:8787 and login with username rstudio.

Overview

To browse the available function and and data in the package, as well as the vignette tutorial, run the following commands in the R console:

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

To get started, the functionality of the package is primarily divided into two parts: (1) exploring differential expression or accessibility of genes and proteins, and (2) constructing a small RNA - transcription factor - gene regulatory network. The following is an overview of the functions in the package, in the order of use subjected to the types of omcis data available. IntegraTRN provides functions:

Part 1 primarily consists of the following functionalities:

1. MOList for generating a MOList object that contains the omics data and sample grouping information; the singly responsible function/object for handling all types of omics data
2. diffOmics for performing differential analysis on the omics data
3. annotateSmallRNA for annotating small RNA transcripts
4. plotVolcano for visualizing differential expression results of count-based omics data (RNAseq, small RNAseq, and proteomics) in the form of volcano plots with up and down regulated genes highlighted
5. plotVolcanoSmallRNA for visualizing small RNAseq differential expression, highlighted by the type of small RNA
6. plotSmallRNAPCAs for visualizing small RNAseq principal component analysis for each type of small RNA
7. countPCA for general-purpose principal component analysis on count data
8. annotateATACPeaksMotif for annotating ATACseq peaks with motif enrichment analysis
9. plotATACAnno for visualizing the annotation of ATACseq peaks
10. plotATACCoverage for visualizing the coverage of ATACseq peaks
11. plotATACMotifHeatmap for visualizing the motif enrichment analysis as a heatmap comparing differentially enriched motifs between the two testing conditions
12. exportDE, a utility function for exporting the differential expression results to a data frame
13. as.data.frame, a utility generic function for converting the PEAKTag object to a data frame for easy manipulation
14. asGRanges, a utility generic function for converting the PEAKTag object to a GRanges object for easy manipulation
15. getRawData, a utility function for easy access of the raw data stored in the MOList object, which is an accessor function for the MOList internal slots
16. exportNormalizedCounts, a utility function for exporting the normalized counts after differential analysis
17. DETag, a utility function as a constructor for the DETag object, useful for optimizing TRN construction workflow
18. TOPTag, a utility function as a constructor for the TOPTag object, which inherits the DETag class with additional gene selection criteria and ranking, useful for optimizing TRN construction workflow
19. PEAKTag, a utility function as a constructor for the PEAKTag object, which inherits the DETag class with specific peak annotations, useful for optimizing TRN construction workflow

Part 2 primarily consists of the following functionalities:

20. matchSamplesRNAsmallRNA for matching the samples between RNAseq and small RNAseq data
21. exportMatchResult for exporting the matching results to a data frame
22. loadExtInteractions for loading external interaction data for small RNA - gene and TF - gene interactions
23. setGene2Protein for setting the gene to protein name mapping
24. setOmicCutoffs for setting the cutoffs for differential expression and accessibility used to filter the key elements in the TRN
25. constructTRN for constructing the TRN
26. plotNetwork for visualizing the TRN
27. parseVertexMetadata for parsing the vertex metadata of the TRN to retrieve the key elements in the TRN
28. exportEdgeSet, a utility function for exporting the edge set of the TRN
29. exportIgraph, a utility function for exporting an igraph object of the TRN for further plot customization
30. writeTRN, a utility function for exporting the TRN to a file in a compatible format for third-party network analysis softwares
31. TRNet, a utility function as a constructor for the TRNet object, useful for optimizing TRN construction workflow if users simply want to visualize a network without prior analysis

and finally integrating the two parts into a single workflow:

32. runIntegraTRN for running a shinyApp that integrates the two parts into a user-friendly single workflow

For detailed information on the analysis pipeline, please refer to the package vignette:

• Integrating multi-omics for constructing transcriptional regulatory networks

For workflow optimization information, please refer to the package vignette:

• Optimizing workflows for TRN construction

The package also provides several datasets:

• An RNAseq count matrix: RNAseq_heart

• A small RNAseq count matrix covering miRNA, tRNA, piRNA, snoRNA, snRNA, and circRNA: smallRNAseq_heart

• A proteomics count matrix: protein_heart

• Sample information for all above 3 omics data: RNAseq_heart_samples, smallRNAseq_heart_samples, and protein_heart_samples

• Two ATACseq peak files as raw data located in the extdata folder: peak1.bed and peak2.bed

• An example miRNA-gene interaction dataset: miR2Genes

• An example TF-gene interaction dataset: tf2Genes

• An lightweight example miRNA-gene interaction dataset with queried using a miRNA centric approach: mir2geneMultiMiR

• An example protein-gene name conversion information: proteinGeneIDConvert

• Position weight matrix (PWM) for vertebrate DNA binding motifs curated by the JASPAR database 2022 release: jasparVertebratePWM

• Small RNA type annotation for human small RNA transcripts: sncRNAAnnotation

• An example MOList object containing all types of omics data, but with a very light weight (100 genes only): expMOList

Please refer to the package vignette Integrating multi-omics for constructing transcriptional regulatory networks for more details on these datasets and the illustration of the analysis pipeline.

The package has also defined a set of key data structures, namely the S4 classes MOList, DETag, TOPTag, PEAKTag, and TRNet. Please refer to the package vignette Optimizing workflows for TRN construction for more details on these data structures and how to use them effectively to extend the functionality of the package.

An overview of the analysis pipeline is shown below:

Overview of the package analysis pipeline (figure created from BioRender.com)

Contributions

The author of the package is Jielin Yang. The author defined all data structures used in this package, including the S4 classes MOList, DETag, TOPTag, PEAKTag, and TRNet. The author wrote the MOList function to construct the key data structure and performs validations on the input omics data. The function diffOmics performs differential expression on RNAseq, small RNAseq, and proteomics data using a negative binomial model, which internally normalizes and performs differential analysis using the DESeq2 or edgeR package. ATACseq peaks are handled as genomic coordinates using the GenomicRanges package. The author wrote the annotateSmallRNA function to annotate small RNA transcripts. The three plotting functions, plotVolcanoRNA, plotVolcanoSmallRNA, and plotSmallRNAPCAs, are supported by the ggplot2 package, with PCA analysis supported by DESeq2 on normalized expression. The ChIPseeker package is used to annotate ATACseq peaks with motif enrichment analysis and performs plotting on ATACseq peaks. The matchSamplesRNAsmallRNA function performs optimal matching based on mahalanobis distance between the sample information of the RNAseq and small RNAseq data. The calculation of the mahalanobis distance and selection of optimal pairs are supported by the MatchIt package. The author wrote the utility functions exportMatchResult and loadExtInteractions to export the matching results and load external interaction data. The author also wrote the setOmicCutoffs function as an easy way for the users to decide on the inclusion criteria for the key elements in the TRN. The author wrote the constructTRN function, with a logic defined to integrate the different omics data depending on their availability, as well as whether predicted inference is used. The inference of predicted small RNA - gene interactions is supported by the author’s discretion to generate a single coherent normalized expression matrix for both RNAseq and small RNAseq data that allows co-expression estimation. The inference of small RNA - gene interactions is performed by the GENIE3 package, which internally uses a three-based algorithm to infer the interactions. The author designed the method for predicted inference of small RNA - gene interactions based on two separate omic dataset. The igraph package is used to visualize the TRN, with interactive support provided by the networkD3 package. Most data frame processing used internally in the functions is supported by the dplyr package. Generative AI tool was used to generate some unit test example data based on the author’s description. Generative AI results were incorporated into the tests at the author’s discretion. The packages shiny, shinyBS, and DT were used to implement with UI for the shinyApp. The author designed the shinyApp UI and logic and optimized the workflow for the shinyApp. In brief, with support of the above packages for separate functionalities, the author pioneered the analysis pipeline that integrates the different omics data and the logic for TRN inference and visualization with different levels of data integration. The author also pre-compile the package for the Docker image.

References

Aharon-Yariv, Adar, Yaxu Wang, Abdalla Ahmed, and Paul Delgado-Olguı́n. 2023. “Integrated Small RNA, mRNA and Protein Omics Reveal a miRNA Network Orchestrating Metabolic Maturation of the Developing Human Heart.” BMC Genomics 24 (1): 1–18.

Bailey, Eric. 2022. shinyBS: Twitter Bootstrap Components for Shiny. https://CRAN.R-project.org/package=shinyBS.

Chang, Le, Guangyan Zhou, Othman Soufan, and Jianguo Xia. 2020. “miRNet 2.0: Network-Based Visual Analytics for miRNA Functional Analysis and Systems Biology.” Nucleic Acids Research 48 (W1): W244–51.

Chang, Winston, Joe Cheng, JJ Allaire, Carson Sievert, Barret Schloerke, Yihui Xie, Jeff Allen, Jonathan McPherson, Alan Dipert, and Barbara Borges. 2023. Shiny: Web Application Framework for r. https://CRAN.R-project.org/package=shiny.

Csardi, Gabor, Tamas Nepusz, et al. 2006. “The Igraph Software Package for Complex Network Research.” InterJournal, Complex Systems 1695 (5): 1–9.

Huynh-Thu, Vân Anh, Alexandre Irrthum, Louis Wehenkel, and Pierre Geurts. 2010. “Inferring Regulatory Networks from Expression Data Using Tree-Based Methods.” PloS One 5 (9): e12776.

Lawrence, Michael, Wolfgang Huber, Hervé Pagès, Patrick Aboyoun, Marc Carlson, Robert Gentleman, Martin T Morgan, and Vincent J Carey. 2013. “Software for Computing and Annotating Genomic Ranges.” PLoS Computational Biology 9 (8): e1003118.

Love, Michael I, Wolfgang Huber, and Simon Anders. 2014. “Moderated Estimation of Fold Change and Dispersion for RNA-Seq Data with DESeq2.” Genome Biology 15 (12): 1–21.

Machlab, Dania, Lukas Burger, Charlotte Soneson, Filippo M Rijli, Dirk Schübeler, and Michael B Stadler. 2022. “monaLisa: An r/Bioconductor Package for Identifying Regulatory Motifs.” Bioinformatics 38 (9): 2624–25.

Müller, Kirill, and Lorenz Walthert. 2023. Styler: Non-Invasive Pretty Printing of r Code. https://CRAN.R-project.org/package=styler.

R Core Team. 2023. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.

Robinson, Mark D, Davis J McCarthy, and Gordon K Smyth. 2010. “edgeR: A Bioconductor Package for Differential Expression Analysis of Digital Gene Expression Data.” Bioinformatics 26 (1): 139–40.

Stuart, Elizabeth A, Gary King, Kosuke Imai, and Daniel Ho. 2011. “MatchIt: Nonparametric Preprocessing for Parametric Causal Inference.” Journal of Statistical Software.

Villanueva, Randle Aaron M, and Zhuo Job Chen. 2019. “Ggplot2: Elegant Graphics for Data Analysis.” Taylor & Francis.

Wickham, Hadley, Romain François, Lionel Henry, Kirill Müller, and Davis Vaughan. 2023. Dplyr: A Grammar of Data Manipulation. https://CRAN.R-project.org/package=dplyr.

Xie, Yihui, Joe Cheng, and Xianying Tan. 2023. DT: A Wrapper of the JavaScript Library ’DataTables’. https://CRAN.R-project.org/package=DT.

Yu, Guangchuang, Li-Gen Wang, and Qing-Yu He. 2015. “ChIPseeker: An r/Bioconductor Package for ChIP Peak Annotation, Comparison and Visualization.” Bioinformatics 31 (14): 2382–83.

Acknowledgements

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

Author’s note

Bioconductor version

A recent version of Bioconductor release is recommended. The author used Bioconductor 3.18 for the development of this package.

To set Bioconductor version:

BiocManager::install(version = "3.18") # or another recent version

Building the package locally

Users are encouraged to clone the repository locally to build the package. However, when running R CMD check using devtools::check(), one warning is expected:

Requires (indirectly) orphaned package: 'plotrix'

This is due to the plotrix package being orphaned, which is imported by one of the package dependency, ChIPseeker.

According to CRAN, which is updated on Nov. 10, 2023, the plotrix package is orphaned. The the maintainer of the plotrix package has passed away, and the package is current searching for a new maintainer. Since ChIPseeker presents a core functionality of IntegraTRN and that the plotrix package has been stable for an extended period, the recent change on the status of plotrix should not affect the functionality of IntegraTRN. Hence, the warning can be safely ignored.

Any suggestions or comments on this issue are welcome.