# Bioconductor Siggenes > Identification of differentially expressed genes and estimation of the False Discovery Rate (FDR) using both the Significance Analysis of Microarrays (SAM) and the Empirical Bayes Analyses of Microarrays (EBAM). - Skill: `biomate-ai/bioconductor-siggenes` (Agent Skill) - Install (CLI): `npx skillmds@latest add biomate-ai/bioconductor-siggenes` - Raw SKILL.md: https://api.skillmd.com/api/skills/biomate-ai/bioconductor-siggenes/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Product & Planning - Author: biomate-ai (https://skillmd.com/u/biomate-ai) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/biomate-ai/bioconductor-siggenes --- # siggenes ## Dependencies & Environment > Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment. - **Version:** 1.86.0 · **Bioconductor:** 3.23 · **R:** ≥ 4.6 - **Depends:** Biobase, multtest - **Imports:** scrime - **System requirements:** URL - **Install:** `BiocManager::install("siggenes")` ## When to Use - **Significance Analysis of Microarrays**: When identifying differentially expressed genes and estimating the False Discovery Rate (FDR) using permutation-based methods via `sam()`. - **Empirical Bayes Analysis**: When computing posterior probabilities of differential expression using the Empirical Bayes Analysis of Microarrays via `ebam()`. - **Threshold Selection**: When visualizing the relationship between the tuning parameter (Delta), the number of significant genes, and the FDR using `delta.plot()` or `plot()`. - **Interactive Gene Identification**: When interactively exploring specific genes on a SAM plot to retrieve locus links and symbols using `identify()`. ## When NOT to Use - **For raw RNA-seq count data**: Use `DESeq2` or `edgeR` instead because `siggenes` assumes continuous, approximately normal or symmetric data and does not model negative binomial distributions. - **For complex experimental designs**: For complex experimental designs with multiple nested random effects or batch correction requirements, use `limma` instead because `siggenes` is primarily optimized for simpler two-class, multi-class, or paired designs. ## Data Requirements - **Input Format**: A matrix of normalized expression values or an `ExpressionSet` object. - **Data Type**: Continuous, normalized values (e.g., log-transformed microarray intensities). - **Class Labels**: A vector (`cl`) specifying the class labels for the samples (e.g., two-class unpaired, paired, or multi-class). ## Key Parameters - **B**: An integer specifying the number of permutations to perform in `sam()`. - **rand**: An integer seed used in `sam()` to ensure reproducible permutation results. - **y**: The delta value (numeric) passed to `plot()` to specify the minimum probability or threshold for a gene to be called differentially expressed. - **pos.stats**: An integer (0 to 4) in `plot()` controlling where general information (like significant genes and FDR) is plotted. - **sig.col**: A color specification in `plot()` for highlighting significant genes (can specify different colors for up- and down-regulated genes). - **chip**: A character string in `identify()` specifying the chip type used, allowing the function to retrieve gene symbols and locus links. ## Best Practices - Use `plot()` on a `SAM` object without specifying a numeric `y` to generate Delta plots, which helps in choosing an appropriate delta threshold based on the FDR. - Set the `rand` parameter in `sam()` to an integer (e.g., `rand=123`) to guarantee that your permutation-based FDR estimates are reproducible. - Use `identify()` on an active SAM plot to interactively click on points and retrieve gene-specific annotations. - When using `plot()` on an `EBAM` object, use the `pos.stats` argument to neatly position the summary statistics (number of significant genes and FDR) in a corner that doesn't obscure the data points. ## Common Pitfalls - **Unstable FDR estimates**: Running `sam()` with too few permutations leads to highly variable FDR estimates; fix this by increasing the `B` parameter (e.g., `B=100` or more). - **Missing gene annotations in plots**: Using `identify()` without providing an `ExpressionSet` or specifying the `chip` argument will fail to retrieve gene symbols; fix this by providing the correct `chip` character string. - **Misinterpreting the Delta parameter**: Guessing a delta value blindly can result in an unacceptably high FDR; fix this by always running `delta.plot()` first to visualize the trade-off between delta, significant genes, and FDR. ## Alternatives - **limma**: The gold standard for microarray differential expression, using moderated t-statistics which generally outperform SAM on very small datasets and handle complex linear models. - **samr**: The original R implementation of SAM by Tibshirani et al., though `siggenes` is more tightly integrated with the Bioconductor `ExpressionSet` ecosystem. - **DESeq2**: For count-based sequencing data, modeling the negative binomial distribution directly. ## Citations - Tusher, V.G., Tibshirani, R., and Chu, G. (2001). Significance analysis of microarrays applied to the ionizing radiation response. *PNAS*, 98, 5116-5121. - Efron, B., Tibshirani, R., Storey, J.D. and Tusher, V. (2001). Empirical Bayes Analysis of a Microarray Experiment. *JASA*, 96, 1151-1160. ## References - Homepage: https://bioconductor.org/packages/siggenes - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/siggenes/inst/doc/siggenes.pdf --- ## Run this on BioMate This skill is the **knowledge layer** — when, why, and how to use `siggenes`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=siggenes)** — free to start. ▶ **[Open `siggenes` on BioMate →](https://www.biomate.ai?ref=kb&pkg=siggenes)**