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GEAnaly

Analyze gene expression matrix data within a well-constructed pipeline, integrating differential enpression analysis, enrichment analysis and gene correlation analysis into a single command

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Description

GEAnaly is an R package that implements a pipeline, which integrates gene differential expression analysis, gene enrichment analysis and gene correlation analysis together for the analysis of gene expression matrix data and visualization of the results of each analysis in the pipeline. The whole analysis pipeline can be executed in a single command efficiently.

The pipeline of the package is basically: Gene expression matrix data → Gene differential expression analysis → Select genes that are differentially expressed → Enrichment analysis for those differentially expressed genes, correlation analysis would also be applied on Gene expression matrix data. Apart from that, for each analysis, visualizations of results are generated.

The biological data analyzed by GEAnaly would be the gene expression data (usually generated from RNA-sequencing experiments), which are the expression matrix of genes in different samples, and the table that records the information about the experimental samples (like sample names and if they are controlled sample or treated sample). In the expression matrix file, the names of rows are gene names, and the names of columns are sample names. The expression matrix file records the estimated count of sequencing reads of each gene (un-normalized counts).

Currently, performing a complete bioinformatics pipeline to analyze gene expression matrix data requires programming skills and experience on processing data. However, it is hard for biologists to learn programming in a short period of time. Therefore, GEAnaly aims to make the analysis of gene expression data as easy as possible without adjusting file formats between different analysis. Also, this package provides a very simplified data analysis experience for biologists with no programming experiences, eliminating the manual construction of the workflow. In addition, users can use very simple commands to visualize the analysis results, once again simplifying the analysis process, and improving analysis efficiency. The GEAnaly package was developed using R version 4.3.1 (2023-06-16), Platform: aarch64-apple-darwin20 (64-bit) and Running under: macOS Sonoma 14.1.

Installation

To install the latest version of the package:

install.packages("devtools")
library("devtools")
devtools::install_github("ReaganGen/GEAnaly", build_vignettes = TRUE)
library("GEAnaly")

To run the Shiny app:

GEAnaly::runGEAnalyShiny()

Overview

# To list all available functions and datasets in the package, use command:
ls("package:GEAnaly")

# To list all example datasets available in the package:
data(package = "GEAnaly") 

# To see the tutorial of GEAnaly
browseVignettes("GEAnaly")

GEAnaly contains 12 functions:

  1. runGEAnaly: Execute the differential expression analysis and enrichment analysis and their visualizations in a single call of a function.

  2. runGEAnalyCor: Execute the gene correlation analysis and its visualization in a single call of a function.

  3. diffExpressionAnalysis: Perform differential expression analysis on gene expression matrix data.

  4. corrAnalysis: Perform gene expression correlation analysis on gene expression matrix data.

  5. extractSignificantGene: Filter out the genes that have significantly different expression levels in different samples based on the threshold provided and the results of differential expression analysis.

  6. labelGenes: Label genes with “UP”, “DOWN” and “NOCHANGE” according to the pValue and foldChange thresholds provided.

  7. enrichAnalysis: Perform genes’ functional enrichment analysis, which is the procedure of identifying functions that are over- or under-represented among a set of genes.

  8. visDeAnaly: Generate a volcano plot for the differential expression analysis result.

  9. visCorrelationAnaly: Generate a heatmap plot for the gene correlation analysis result.

  10. visEnrichAnaly: Generate a Manhattan plot for the enrichment analysis result.

  11. visEnrichAnalyLollipop: Generate a Lollipop plot for the enrichment analysis result (show the top 10 biological pathways with top 10 highest gene ratio values).

  12. runGEAnalyShiny: Launches the Shiny app for GEAnaly. The purpose of this app is providing a shiny app page with user-friendly UI that assists users without programming experience to perform the analyses easily and efficiently.

The package also contains 8 datasets.

# use the following command to list all example datasets available in the package:
data(package = "GEAnaly") 

# Use ? to check details of the data.
?diffExpressionResult

Refer to package vignettes for more details. An overview of the package is illustrated below:

Contributions

The author of the package is Gen Li. The author defined all functions used in the package, including runGEAnaly, runGEAnalyShiny,runGEAnalyCor, diffExpressionAnalysis, corrAnalysis, extractSignificantGene, labelGenes, enrichAnalysis, visDeAnaly, visCorrelationAnaly, visEnrichAnaly and visEnrichAnalyLollipop. The main contribution of the author was integrate multiple analyses into a single function and develope the shiny app. Apart from that, the author also developed various visualization functions for different visualization choices.

The author defined runGEAnaly to integrate differential expression analysis and enrichment analysis and their visualizations into a quick single call of a function. The runGEAnalyShiny used runApp function from package shiny to lauch the shiny app. Also, the author defined runGEAnalyCor to integrate correlation analysis and its visualization into a single call of the function. Apart from that, diffExpressionAnalysis used functions DESeqDataSetFromMatrix, DESeq and results from the package DESeq2 as the core for the differential expression analysis. The function extractSignificantGene applied the filter function from package dplyr to select genes with significant differences in expression levels in different samples. The function labelGenes used mutate function from package dplyr to filter and label genes. Furthermore, the function enrichAnalysis used the gost function from the package gprofiler2 to implement the enrichment analysis. In addition, the function for correlation analysis, which is corrAnalysis, used the cor function from the package stats to calculate the correlation coefficient between pairs of genes. The functions for visualizations (visDeAnaly, visCorrelationAnaly, visEnrichAnaly, visEnrichAnalyLollipop), all used package ggplot2 to create comprehensible figures. Specifically, visCorrelationAnaly made use of package pheatmap and grDevices to plot the visualization for the correlation analysis. Also, function visEnrichAnaly employed gprofiler2::gostplot() to assist in plotting of the Manhattan plot for the enrichment analysis, and used htmlwidgets to save the interactive version of the plot. Generative AI tool was never used during the development.

References

  • 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, https://CRAN.R-project.org/package=shiny.

  • Geistlinger, L., Csaba, G., Zimmer, R. (2016). “Bioconductor’s EnrichmentBrowser: seamless navigation through combined results of set- & network-based enrichment analysis.” BMC Bioinformatics, 17, 45. doi:10.1186/s12859-016-0884-1.

  • Kolberg, L., Raudvere, U., Kuzmin, I., Vilo, J., Peterson, H. (2020). “gprofiler2-an R package for gene list functional enrichment analysis and namespace conversion toolset g:Profiler.” F1000Research, 9 (ELIXIR)(709). R package version 0.2.2.

  • Kolde, R. (2019). pheatmap: Pretty Heatmaps. R package version 1.0.12, https://CRAN.R-project.org/package=pheatmap

  • Love, M. I., Huber, W., Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15(12):550. https://doi.org/10.1186/s13059-014-0550-8

  • Overmyer, K. A., Shishkova, E., Miller, I. J., Balnis, J., Bernstein, M. N., Peters-Clarke, T. M., Meyer, J. G., Quan, Q., Muehlbauer, L. K., Trujillo, E. A., He, Y., Chopra, A., Chieng, H. C., Tiwari, A., Judson, M. A., Paulson, B., Brademan, D. R., Zhu, Y., Serrano, L. R., … Jaitovich, A. (2021). Large-scale multi-omic analysis of COVID-19 severity. Cell Systems, 12(1). https://doi.org/10.1016/j.cels.2020.10.003

  • Posit team (2023). RStudio: Integrated Development Environment for R. Posit Software, PBC, Boston, MA. URL http://www.posit.co/.

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

  • Vaidyanathan, R., Xie, Y., Allaire, J., Cheng, J., Sievert, C., Russell, K. (2023). htmlwidgets: HTML Widgets for R. R package version 1.6.2, https://CRAN.R-project.org/package=htmlwidgets

  • Wickham, H., François, R., Henry, L., Müller, K., Vaughan, D. (2023). dplyr: A Grammar of Data Manipulation. R package version 1.1.3, https://CRAN.R-project.org/package=dplyr

  • Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. https://CRAN.R-project.org/package=ggplot2

  • Yu, G. (2023). ggplotify: Convert Plot to ‘grob’ or ‘ggplot’ Object. R package version 0.1.2, https://CRAN.R-project.org/package=ggplotify

Acknowledgements

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