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zenith

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bioconductor-zenith

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Use bioconductor-zenith to prepare gene-set enrichment results and a top-results heatmap from [eBayes-processed dream fit], using [matching GeneSetCollection] and [coefficient or contrast].

Required inputs

  • [eBayes-processed dream fit]
  • [matching GeneSetCollection]
  • [coefficient or contrast]

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SKILL.md

name: bioconductor-zenithdescription: 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().when_to_use: Use when: Example usage of zenith on GEUVAIDIS RNA-seq; Example usage of zenith on RNA-seq; Differential expression analysis (zenith); Data normalization and preprocessing (zenith); Quality control and preprocessing (zenith). Not for: Requires R ≥ 4.2.0 and Bioconductor ≥ 3.16user-invocable: false

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)

# 1. Filter low-expressed genes
data(pickrell)
geneCounts = exprs(pickrell.eset)
df_metadata = pData(pickrell.eset)
dsgn = model.matrix(~ gender, df_metadata)
keep = filterByExpr(geneCounts, dsgn, min.count=5)

# 2. Compute library size normalization
dge = DGEList(counts = geneCounts[keep,])
dge = calcNormFactors(dge)

# 3. Estimate precision weights
vobj = voomWithDreamWeights(dge, ~ gender, df_metadata)

# 4. Fit linear (mixed) model
fit = dream(vobj, ~ gender, df_metadata)
fit = eBayes(fit)

# 5. Load gene sets and run zenith
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