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

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