Time-Series Copy Number Variation Analysis
Also known as: longitudinal CNV analysis, temporal copy number analysis, time-series CNV profiling, serial CNV analysis
Time-series copy number variation (CNV) analysis is a computational genomics pipeline that characterizes chromosomal gains and losses across multiple sequential samples from the same individual or tumor. By comparing copy number profiles at successive time points — such as diagnosis, mid-treatment, relapse — it reconstructs the clonal dynamics and evolutionary trajectories driving genome instability, enabling researchers to track how sub-populations expand, contract, or acquire new aberrations over time.
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When to use it
Use time-series CNV analysis when you have two or more genomic samples taken at different time points from the same subject — most commonly in cancer genomics (primary tumor, ctDNA, relapse biopsy) or in developmental biology with sequential sampling. It is the appropriate method for studying clonal evolution under treatment pressure, identifying which CNV events precede resistance, or reconstructing the order in which chromosomal instability events accumulate. Do NOT use this method when only a single time point is available (use standard CNV analysis instead), when sample purity is below approximately 20% without special low-purity tools, when matched normal tissue is absent and no robust normalization strategy is in place, or when the biological question concerns population-level CNV frequencies rather than within-individual evolution.
Strengths & limitations
- Directly captures genome evolution over time, enabling causal inference about which copy number events precede phenotypic changes such as treatment resistance.
- Allele-specific calling combined with temporal modeling allows reconstruction of clonal phylogenies with chromosomal resolution.
- Compatible with both whole-genome sequencing (high resolution) and whole-exome sequencing (lower cost) data, and adaptable to circulating tumor DNA from liquid biopsies.
- Joint modeling of multiple time points increases statistical power to detect sub-clonal CNV events that would be missed in single-sample analysis.
- Publicly available, well-documented tools (MEDICC2, HATCHet, Battenberg, PURPLE) support reproducible implementation.
- Requires high-quality DNA from multiple time points; degraded or low-input material (e.g., FFPE archival tissue) substantially increases noise and can render temporal comparisons unreliable.
- Purity and ploidy estimation can be incorrect in highly heterogeneous or near-diploid tumors, propagating errors into all downstream CNV calls and clonal inferences.
- Sub-clonal resolution is limited by sequencing depth; detecting clones below roughly 5–10% cellular prevalence requires very high coverage (100x+ WGS), which is costly.
- Biological interpretations of clonal trees are inherently uncertain — the same observed data may be consistent with multiple evolutionary histories — so conclusions about directionality should be qualified.
Frequently asked
How many time points are needed for a meaningful analysis?
A minimum of two time points (e.g., baseline and relapse) is sufficient to detect changes and infer a simple two-state evolutionary model. Three or more time points allow reconstruction of branching clonal trees and temporal ordering of events. More time points increase resolution but also raise costs and data integration complexity. With only two samples the method can still identify which CNVs appeared or disappeared, but cannot resolve the order of independent branching events.
Can I use this method with liquid biopsy (ctDNA) data instead of tissue biopsies?
Yes, and this is increasingly common. ctDNA-based time-series CNV analysis enables non-invasive longitudinal monitoring. However, ctDNA samples typically have low tumor fraction, often 1–10%, which demands ultra-deep sequencing (500x–1000x) and specialized low-purity CNV callers (e.g., ichorCNA, PLOIDYTEST). Standard tissue-calibrated pipelines should not be applied to low-fraction ctDNA without modification.
How does time-series CNV analysis differ from standard CNV analysis?
Standard CNV analysis processes each sample independently and reports copy number states at a single time point. Time-series CNV analysis jointly models multiple samples from the same individual to identify which copy number segments changed between time points, estimates clonal prevalence at each time point, and reconstructs the evolutionary history of those changes. The key added value is the temporal and clonal dimension, which standard single-sample tools do not provide.
What sequencing depth is recommended?
For whole-genome sequencing of solid tumor biopsies, 30–60x is commonly used for detecting clonal CNV events; sub-clonal detection below 10% prevalence requires 100x or more. For whole-exome sequencing, 100–200x is typical. Matched normal samples should match or exceed tumor depth. If sequencing depths differ substantially across time points, depth-aware normalization or down-sampling is necessary before segmentation to avoid artefactual apparent changes.
Which tools are recommended for time-series CNV analysis?
MEDICC2 is widely used for multi-sample joint CNV and clonal tree inference. HATCHet jointly estimates purity, ploidy, and allele-specific copy numbers across multiple samples. Battenberg provides allele-specific segmentation suited to paired tumor-normal designs. PURPLE integrates CNV, purity/ploidy, and structural variant information. For ctDNA, ichorCNA is a standard low-purity option. Tool choice depends on data type (WGS vs WES), availability of matched normals, and whether allele-specific or total copy number is the primary endpoint.
Sources
- Dentro, S. C., et al. (2021). Characterizing genetic intra-tumor heterogeneity across 2,658 human cancer genomes. Cell, 184(8), 2239-2254. link ↗
- Zaccaria, S., & Raphael, B. J. (2020). Accurate quantification of copy-number aberrations and whole-genome duplications in multi-sample tumor sequencing data. Nature Communications, 11(1), 4301. link ↗
How to cite this page
ScholarGate. (2026, June 3). Time-Series Copy Number Variation Analysis. ScholarGate. https://scholargate.app/en/bioinformatics/time-series-copy-number-variation-analysis
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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