| name | bioconductor-affy |
| description | The package contains functions for exploratory oligonucleotide array analysis. The dependence on tkWidgets only concerns few convenience functions. 'affy' is fully functional without it. |
| when_to_use | Use when: Importing and summarizing raw Affymetrix probe-level data into an ExpressionSet using classic algorithms like rma() or mas5().; Performing quality control and exploratory data analysis on Affymetrix expression microarrays using MAplot(), boxplot(), and image().; Assessing 5'-to-3' RNA degradation gradients across samples using AffyRNAdeg() and plotAffyRNAdeg().. Not for: For newer Affymetrix Gene ST or Exon ST arrays, use oligo instead, because affy does not correctly handle the probe-design structure of these newer generation chips.; For Illumina, Agilent, or other non-Affymetrix microarray platforms, use limma or b |
| user-invocable | false |
affy
Dependencies & Environment
Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.
- Version: 1.90.0 · Bioconductor: 3.23 · R: ≥ 4.6
- Depends: BiocGenerics, Biobase
- Imports: affyio, BiocManager, preprocessCore
- Install:
BiocManager::install("affy")
When to Use
- Importing and summarizing raw Affymetrix probe-level data into an
ExpressionSet using classic algorithms like rma() or mas5().
- Performing quality control and exploratory data analysis on Affymetrix expression microarrays using
MAplot(), boxplot(), and image().
- Assessing 5'-to-3' RNA degradation gradients across samples using
AffyRNAdeg() and plotAffyRNAdeg().
When NOT to Use
- For newer Affymetrix Gene ST or Exon ST arrays, use
oligo instead, because affy does not correctly handle the probe-design structure of these newer generation chips.
- For Illumina, Agilent, or other non-Affymetrix microarray platforms, use
limma or beadarray because affy is strictly hardcoded for Affymetrix CDF structures.
Data Requirements
- Input Format: Raw Affymetrix data loaded as an
AffyBatch object (e.g., the Dilution dataset).
- Structure: Probe-level data containing
pm (perfect match) and mm (mismatch) intensities, mapped via a Chip Definition File (CDF) environment.
- Normalization State: Raw, un-normalized probe-level intensities (prior to running
normalize() or rma()).
Key Parameters
- pairs (TRUE): Logical in
MAplot() indicating whether to plot pairwise comparisons between arrays.
- plot.method ("smoothScatter"): Character string in
MAplot() specifying the method for plotting dense data.
- col (c(2,3,4)): Vector of colors passed to
boxplot() to differentiate arrays.
- genenames (NULL): Vector of probe set names to extract specific data in
probeset(), pmindex(), or mmindex().
- locations (NULL): A list of physical
pm and mm coordinates passed to probeset() for custom probe set definitions.
- which ("pm"): Character string in
indexProbes() specifying which probes to index ("pm", "mm", or "both").
- addcdf (FALSE): Logical in
cleancdfname() indicating whether to append "cdf" to the environment name.
- compress.cel (TRUE): Logical option set via
options() to allow reading compressed .CEL files.
Best Practices
- Run
AffyRNAdeg() and plotAffyRNAdeg() to check for RNA degradation gradients across samples before proceeding with normalization.
- Use
MAplot() with plot.method="smoothScatter" to visualize intensity-dependent biases between arrays.
- Use
boxplot() to compare the distribution of raw intensities across multiple arrays before normalization.
- Extract specific probe set data using
probeset() and inspect pm() and mm() values directly to understand probe-level behavior.
Common Pitfalls
- Missing CDF environment: Functions like
getCdfInfo() fail if the specific chip definition file (e.g., HG_U95Av2) is not installed. Fix: Install and load the corresponding CDF package from Bioconductor.
- Memory exhaustion with large datasets: Loading many arrays into an
AffyBatch can consume significant memory. Fix: Use rma() to directly compute the summarized ExpressionSet and discard raw probe data.
- Incorrect probe indexing: Using
pm() or mm() without knowing the exact probe locations can lead to misinterpretation. Fix: Use indexProbes() or pmindex() to retrieve the correct physical locations on the chip.
Alternatives
- oligo: For preprocessing newer whole-transcript (WT) Affymetrix arrays.
- affyPLM: For advanced probe-level linear modeling and detailed quality metrics (RLE, NUSE plots).
- limma: For downstream differential expression analysis after
rma() preprocessing.
Citations
- Gautier, L. et al. (2004). "affy—analysis of Affymetrix GeneChip data at the probe level." Bioinformatics, 20(3), 307-315.
- Irizarry, R. A. et al. (2003). "Exploration, normalization, and summaries of high density oligonucleotide array probe level data." Biostatistics, 4(2), 249-264.
References
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