Welcome to the bulkanalysis package.
The purpose of the bulkanalysis package is to streamline the downstream analysis of bulk RNA-seq data by providing a collection of commonly used analysis and visualizations in a single package. The package was developed as a wrapper around statistical and visualization methods that I have frequently encountered during my experience analyzing bulk RNA-seq data. Rather than introducing new statistical methods, bulkanalysis is designed to reduce repetitive coding and provide a consistent workflow for obtaining commonly used downstream results. This package performs techniques such as differential expression via edgeR with traditional outputs such as heatmaps, volcano plots. As well as over-representation analysis via clusterProfiler using Fishcher’s Exact Test with summarized output visuals (As of 9/17/26).
Installation Instructions:
Currently Includes:
baseline_dimreduction(): ariance-stabilized expression values are used to calculate sample distances and principal components. The most variable genes are used for PCA, and euclidean distance visualization.
edgeR_diffexp(): Performs EdgeR based pairwise (group A vs group B) differential expression analysis for each experimental state in the entered colData of the DeseqDataset input.
volcano_plot(): Contrsucts a volcano plot of the initial DEGs
heatmap_function(): Constructs a column clustered, z-scaled heatmap of DEGs
gene_id_converter_ver2(): Converts genes via getBM() Ensembl dataset API pulls. If fails to connect to API, then will use the downloaded AnnotationDbi of chosen organism to convert available geneids.
enrichgo_unpack_ver3(): Unpacks the key results of clusterProfiler::enrichgo() function. Outputs are set into the set list marked by over-represented terms to view individual DEGs for eahc term.
ora_enrichgo(): Performs over-representation test on upregulated or downregulated DEGs and plots the top 15 over-represented terms into a dotplot.
pc_var_association(): Custom implementation inspired by Principal Variance Component Analysis (PVCA) described by Li, J., Bushel, P., et. al.Chapter 12, 2010. Quantifies the association between sample-level metadata covariates and factors and principal components (PCs) derived from a bulk RNA-seq expression dataset using ANOVA or a linear regression.
run_de_pipleine(): Runs volcano_plot(), heatmap_function(), ora_enrichgo(), and enrichgo_unpack_ver3() in succession and reports the up/down regulated DEGs for each comparison in your initial list of results.
Future Goals:
Implement GSEA
Protein-Protein Interaction Network
New ORA visuals:
-Radial Upset Plot to determine which enriched term contains most DEGs
-IGraph based ORA network to determine central enriched terms Citations:
Mark D. Robinson, Davis J. McCarthy, Gordon K. Smyth, edgeR: a Bioconductor package for differential expression analysis of digital gene expression data, Bioinformatics, Volume 26, Issue 1, January 2010, Pages 139–140, https://doi.org/10.1093/bioinformatics/btp616
Wu T, Hu E, Xu S, Chen M, Guo P, Dai Z, Feng T, Zhou L, Tang W, Zhan L, Fu x, Liu S, Bo X, Yu G (2021). “clusterProfiler 4.0: A universal enrichment tool for interpreting omics data.” The Innovation, 2(3), 100141.doi:10.1016/j.xinn.2021.100141.
Love MI, Huber W, Anders S (2014).“Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.”Genome Biology, 15, 550.doi:10.1186/s13059-014-0550-8.
Li, J., Bushel, P., Chu, T., Wolfinger, R., Batch Effects and Noise in Microarray Experiment, Chapter 12, 2010