| name | bioconductor-dnacopy |
| description | Implements the circular binary segmentation (CBS) algorithm to segment DNA copy number data and identify genomic regions with abnormal copy number. |
| when_to_use | Use when: Copy Number Segmentation: Finding change-points in array DNA copy number data (e.g., array CGH) using the segment() function.; Identifying Gains and Losses: Determining regions of gained and lost copy number by plotting segment means with plot().; Outlier Smoothing: Smoothing single point outliers in log-ratio data prior to segmentation using smooth.CNA().. Not for: For allele-specific copy number estimation or tumor purity/ploidy correction, use PureCN because DNAcopy only segments total copy number.; For single-cell DNA copy number profiling where specialized noise models are required, use AneuFinder because D |
| user-invocable | false |
DNAcopy
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: Imports
- System requirements: URL
- Install:
BiocManager::install("DNAcopy")
When to Use
- Copy Number Segmentation: Finding change-points in array DNA copy number data (e.g., array CGH) using the
segment() function.
- Identifying Gains and Losses: Determining regions of gained and lost copy number by plotting segment means with
plot().
- Outlier Smoothing: Smoothing single point outliers in log-ratio data prior to segmentation using
smooth.CNA().
When NOT to Use
- For allele-specific copy number estimation or tumor purity/ploidy correction, use
PureCN because DNAcopy only segments total copy number.
- For single-cell DNA copy number profiling where specialized noise models are required, use
AneuFinder because DNAcopy assumes bulk-level signal distributions.
- For multi-sample joint segmentation, use
copynumber because DNAcopy processes samples independently.
Data Requirements
- Input Object: Data must be converted into a
CNA object using the CNA() function.
- Required Columns: A matrix or vector of log-ratio data, a vector of
Chromosome identifiers, and a vector of Position (map locations).
- Data Type: Typically specified as
data.type="logratio".
Key Parameters
- undo.splits (
"sdundo"): Method to get rid of unnecessary change-points due to local trends.
- undo.SD (
3): The number of standard deviations used when undo.splits="sdundo" to remove splits.
- plot.type (
"w", "s", "c", "p"): Determines the layout of the plot() function (e.g., whole genome, by chromosome, across studies, or ordered by plateau).
- p.method (
"perm"): Method for p-value computation. The default is a faster hybrid approach, but full permutations can be forced.
- nperm (
10000): Number of permutations used in the hybrid method.
- alpha: Significance level for the test to accept change-points.
- verbose (
1): Controls the printing of progress during the segment() run.
Best Practices
- Smooth Data First: Always run
smooth.CNA() on your CNA object to mitigate the effect of single point outliers before running segment().
- Undo Local Trends: Use
undo.splits="sdundo" with undo.SD=3 in segment() to remove false-positive change-points caused by local data trends.
- Subset Large Data: Use
subset.CNA() to subset by chromosome and sample so that segmentation does not have to be run on a whole data set at once.
- Determine Thresholds: Use
plot() with plot.type="p" to order segments by their chromosome means, helping to visually determine the plateaus for calling gains (e.g., 0.4) and losses (e.g., -0.6).
Common Pitfalls
- Over-segmentation from Local Trends: The algorithm may find change-points due to local trends rather than true copy number shifts. Fix: Apply the
undo.splits="sdundo" parameter in segment().
- Slow Computation on Large Datasets: Running the full permutation algorithm takes O(N^2) computations. Fix: Rely on the default hybrid approach (Gaussian approximation + permutation) rather than setting
p.method='perm', or reduce nperm.
- Plotting Clutter: Plotting multiple chromosomes at once can be hard to read. Fix: Use
plot.type="s" to plot by chromosome within a study.
Alternatives
- copynumber: For multi-sample segmentation and joint segmentation of copy number data.
- PureCN: For joint estimation of tumor purity, ploidy, and absolute copy number.
- AneuFinder: For copy number analysis of single-cell sequencing data.
Citations
- Olshen, A. B., Venkatraman, E. S., Lucito, R., and Wigler, M. (2004). Circular binary segmentation for the analysis of array-based dna copy number data. Biostatistics, 5:557–72.
- Venkatraman, E. S. and Olshen, A. B. (2007). A faster circular binary segmentation algorithm for the analysis of array cgh data. Bioinformatics, 23:657–63.
References
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