# Bioconductor Vsn > The package implements a method for normalising microarray intensities from single- and multiple-color arrays. It can also be used for data from other technologies, as long as they have similar format. The method uses a robust variant of th - Skill: `biomate-ai/bioconductor-vsn` (Agent Skill) - Install (CLI): `npx skillmds@latest add biomate-ai/bioconductor-vsn` - Raw SKILL.md: https://api.skillmd.com/api/skills/biomate-ai/bioconductor-vsn/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/bioconductor-vsn --- # vsn ## Dependencies & Environment > Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment. - **Version:** 3.80.0 · **Bioconductor:** 3.23 · **R:** ≥ 4.6 - **Depends:** Biobase - **Imports:** affy, limma, lattice, ggplot2 - **Install:** `BiocManager::install("vsn")` ## When to Use - **Microarray Normalization**: Normalizing and variance-stabilizing unnormalised microarray intensity data (e.g., two-colour spotted cDNA arrays or single-colour arrays) using `justvsn` or `vsn2`. - **Affymetrix Preprocessing**: Applying variance stabilization, background correction, and transformation to Affymetrix genechip data via the `vsnrma` wrapper. - **Reference-Based Normalization**: Normalizing new test arrays against an existing reference dataset without altering the reference model using the `reference` argument in `vsn2`. - **Spike-in Calibration**: Fitting calibration and transformation parameters on a subset of features (like spike-in probes) using `vsn2`, and then applying the model to the complete dataset using `predict`. ## When NOT to Use - For summarizing Affymetrix data without VSN's specific variance stabilization, use `rma` from the `affy` package because it provides standard robust multi-array average summarization. - For addressing variance dependencies on factors other than the mean intensity (e.g., gene-inherent properties or sample-inherent transcriptional control tightness), use specialized models because VSN only addresses the dependence of variance on the mean intensity. - For reading and processing raw image quantitation files directly into R, use `read.maimages` from the `limma` package because `vsn` expects already-imported data structures. ## Data Requirements - **Input Format**: An `ExpressionSet`, `AffyBatch` (from the `affy` package), `RGList` (from the `limma` package), `NChannelSet`, or a raw numeric matrix. - **Data Structure**: Rows represent features (e.g., spots, probes) and columns represent samples or arrays. - **Normalization State**: Unnormalised, raw intensity data (do not log-transform prior to VSN, as it applies its own generalized logarithm, $glog_2$, transformation). ## Key Parameters - **strata**: Allows choosing different offset and scaling factors for different groups of rows (e.g., print-tip groups or sectors). - **calib**: Controls the calibration behavior (e.g., choosing the same offset and scaling factor throughout if calibration was already done). - **backgroundsubtract** (FALSE): When set to TRUE in `justvsn` for an `RGList`, subtracts local background estimates from the incoming data. - **lts.quantile**: Controls the robustness of the parameter estimation algorithm; setting it to 1 corresponds to least sum of squares regression (no outlier removal), useful for trusted spike-in sets. - **reference**: Allows passing an existing fitted `vsn` object to normalize new arrays against a reference dataset. - **ranks** (TRUE): Used in `meanSdPlot` to distribute data evenly along the x-axis by rank rather than average intensity. ## Best Practices - **Verify Variance Stabilization**: Use `meanSdPlot` after normalization to verify variance stabilization by checking if the running median of the standard deviation is approximately a horizontal line. - **Two-Step Fitting**: When fitting parameters on a subset of data (e.g., spike-ins) using `vsn2`, apply the model to the complete dataset using the `predict` method. - **Background Correction**: Avoid subtracting local background estimates unless there is actual local variability (like a spatial gradient), as VSN already estimates and subtracts an overall background estimate. - **Affymetrix Workflows**: For Affymetrix genechip data, use the `vsnrma` wrapper to seamlessly combine VSN's background correction, between-array normalization, and transformation with RMA summarization. ## Common Pitfalls - **Optimization Convergence Failure**: Encountering the error "L-BFGS-B needs finite values of 'fn'" due to unsuitable data or flat parameter space directions. *Fix*: Check data quality, experimental design, or prior preprocessing steps for incompatible measurements. - **Systematic Trends Post-Normalization**: Observing an overall trend in the `meanSdPlot` after normalization. *Fix*: Investigate raw data integrity or inadequate prior preprocessing, as this indicates the variance stabilization failed. - **Increased Random Noise**: Adding random noise to the signal by unnecessarily subtracting local background estimates. *Fix*: Rely on VSN's overall background estimate and leave `backgroundsubtract=FALSE` unless spatial gradients are explicitly present. - **Metadata Loss During Coercion**: Losing sample metadata when `justvsn` converts an `RGList` to an `NChannelSet`. *Fix*: Manually construct and assign an `AnnotatedDataFrame` to the `phenoData` slot of the resulting object. ## Alternatives - **affy**: Provides the standard `rma` function for Affymetrix genechip preprocessing without VSN's specific generalized logarithm transformation. - **limma**: Provides `read.maimages` for importing data and `lmFit`/`eBayes` for linear modeling and differential expression, often used downstream of VSN. - **quantreg**: Provides quantile regression, which is a more rigorous alternative to the simple running median used in `meanSdPlot` for assessing variance. ## Citations - Huber W, et al. (2002). Variance stabilization applied to microarray data calibration and to the quantification of differential expression. *Bioinformatics*, 18(Suppl 1), S96-S104. - Rocke DM, Durbin B (2001). A model for measurement error for gene expression arrays. *Journal of Computational Biology*, 8(6), 557-569. - Irizarry RA, et al. (2003). Summaries of Affymetrix Microarray Probe Level Data. *Nucleic Acids Research*, 31(4), e15. ## References - Homepage: https://bioconductor.org/packages/vsn - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/vsn/inst/doc/vsn.pdf --- ## Run this on BioMate This skill is the **knowledge layer** — when, why, and how to use `vsn`. 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=vsn)** — free to start. ▶ **[Open `vsn` on BioMate →](https://www.biomate.ai?ref=kb&pkg=vsn)**