| name | bioconductor-zenith |
| description | Zenith performs gene set analysis on the result of differential expression using linear (mixed) modeling with dream by considering the correlation between gene expression traits. This package implements the camera method from the limma package proposed by Wu and Smyth (2012). Zenith is a simple extension of camera to be compatible with linear mixed models implemented in variancePartition::dream(). |
Zenith
Workflows
Standard Workflow
Perform gene set enrichment analysis on differential expression results from a linear mixed model fit with dream and visualize the results.
library(zenith)
library(edgeR)
library(variancePartition)
library(tweeDEseqCountData)
library(kableExtra)
data(pickrell)
geneCounts = exprs(pickrell.eset)
df_metadata = pData(pickrell.eset)
dsgn = model.matrix(~ gender, df_metadata)
keep = filterByExpr(geneCounts, dsgn, min.count=5)
dge = DGEList(counts = geneCounts[keep,])
dge = calcNormFactors(dge)
vobj = voomWithDreamWeights(dge, ~ gender, df_metadata)
fit = dream(vobj, ~ gender, df_metadata)
fit = eBayes(fit)
msdb.gs = get_MSigDB("H", to="ENSEMBL")
res.gsa = zenith_gsa(fit, msdb.gs, 'gendermale', progressbar=FALSE)
plotZenithResults(res.gsa)
Input: Raw RNA-seq counts and sample metadata. Output: A data frame of gene set enrichment statistics and a heatmap visualization of the top results.
When to Use
- To perform gene set enrichment analysis on differential expression results generated from linear (mixed) models using
variancePartition::dream().
- To account for correlation between gene expression traits by extending the
camera method from limma to linear mixed models.
- To easily fetch and cache gene sets from MSigDB (using
get_MSigDB) or Gene Ontology (using get_GeneOntology).
When NOT to Use
- For simple fixed-effect designs without random effects or sample correlation; standard
limma::camera is sufficient.
- If you are not using
variancePartition::dream for differential expression; zenith_gsa is specifically designed to take the output of dream().
Data Requirements
- A fitted model object from
variancePartition::dream() that has been processed with limma::eBayes().
- A
GeneSetCollection object containing gene sets (e.g., loaded via get_MSigDB or get_GeneOntology).
- Gene identifiers in the gene sets must match the rownames of the fitted model (e.g., both using ENSEMBL or SYMBOL).
Key Parameters
- to ("ENSEMBL"): Target gene identifier type (e.g., "ENSEMBL", "SYMBOL", "ENTREZ") in
get_MSigDB or get_GeneOntology.
- progressbar (FALSE): Logical indicating whether to show a progress bar during
zenith_gsa execution.
- coef ('gendermale'): The coefficient/contrast in the fitted model to evaluate for gene set enrichment.
Best Practices
- Filter out low-expressed genes using
edgeR::filterByExpr to improve statistical power and model stability.
- Estimate precision weights using
variancePartition::voomWithDreamWeights to account for the mean-variance relationship in RNA-seq count data.
- Use
plotZenithResults to visualize the top gene set enrichment results across coefficients or cell types.
Common Pitfalls
- Gene identifier mismatch: Ensure the gene ID type specified in
get_MSigDB(..., to=...) matches the ID type used as rownames in the expression matrix and the dream fit.
- Running on raw counts: Always normalize counts using
calcNormFactors and apply voomWithDreamWeights before fitting models with dream.
Alternatives
limma::camera for fixed-effect linear models.EnrichmentBrowser::getGenesets for loading alternative gene set databases like KEGG or Enrichr.
Citations
- Hoffman G (2026). Zenith: Gene set analysis for linear mixed models. R package.
- Wu D, Smyth GK (2012). Camera: a competitive gene set test accounting for inter-gene correlation. Nucleic Acids Research.
References
