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.
- 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
install.packages("remotes")
remotes::install_github("spencertreadway/BioUtils")# 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)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
Full documentation is available at https://spencertreadway.github.io/BioUtils/ or via:
help(package = "BioUtils")- 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
MIT
Spencer Treadway