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BioUtils

BioUtils is an end-to-end R toolkit for analyzing gene expression data from GEO datasets. It provides a unified workflow for differential expression, statistical testing, visualization, and biological interpretation.

Features

• Load and preprocess GEO datasets
• Differential expression analysis using limma
• Visualization (PCA, volcano plots, gene-level plots)
• Statistical testing (adaptive t-test, effect size, bootstrapped CI)
• Gene set enrichment analysis (GSEA)
• Machine learning (LASSO biomarker selection)
• Gene co-expression analysis

Installation

install.packages("remotes")
remotes::install_github("spencertreadway/BioUtils")

Example Workflow

# Load data
eset <- load.geo.soft("GDS507.soft", log.transform = TRUE)
geo <- extract.expression(eset)

# PCA visualization
pca.plot(geo$expression, geo$phenotype, color.by = "disease.state")

# Differential expression
de.results <- run.limma.de(geo)

# Volcano plot
volcano.plot(de.results, fc.threshold = 0.3)

# Select top genes
top.genes <- head(rownames(de.results[order(de.results$adj.P.Val), ]), 5)
probe.ids <- find.probe.by.gene(geo$gene, top.genes)

# Single gene analysis
expr <- get.gene.expression(geo$expression, probe.ids[1])
df <- build.analysis.df(expr, geo$phenotype, geo$gene)
gene.analysis.plot(df)

# LASSO model
phenotype.binary <- ifelse(geo$phenotype$disease.state == "disease", 1, 0)
lasso.fit <- fit.lasso(geo$expression, phenotype.binary)

Interpretation

BioUtils integrates multiple layers of analysis:

• PCA reveals global structure in the data
• Differential expression (limma) identifies significant genes
• Effect size & CI quantify biological impact
• LASSO selects predictive biomarkers
• GSEA links results to biological pathways

Documentation

Full documentation is available at https://spencertreadway.github.io/BioUtils/ or via:

help(package = "BioUtils")

Notes

• Probe-to-gene mapping depends on GEO platform annotations
• Fold-change thresholds are user-defined and dataset-dependent
• Statistical significance does not always imply biological relevance

License

MIT

Author

Spencer Treadway

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.