| name | bioconductor-vsn |
| description | 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 |
| when_to_use | Use when: 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 array. Not for: 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 i |
| user-invocable | false |
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
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