# Zenith

> Zenith

- Skill: `biomate-ai/zenith` (Agent Skill)
- Install (CLI): `npx skillmds@latest add biomate-ai/zenith`
- Raw SKILL.md: https://api.skillmd.com/api/skills/biomate-ai/zenith/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: biomate-ai (https://skillmd.com/u/biomate-ai)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/biomate-ai/zenith

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# 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.

```r
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
- Homepage: bioconductor.org/packages/zenith
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/zenith/inst/doc/zenith.html