Target-level CNV track (Target CNVs)

The algorithm will produce a target-level CNV track, if you've chosen to create one when running the algorithm. The target-level CNV track is an annotation track, containing one annotation for every target in the input data. Inspection of the target-level CNV track can give you additional information about both the CNVs called in the region-level results, and those regions that have not been called. Note that a "statistically relevant" target is one that has a coverage higher than the specified coverage cutoff, AND is found on a a chromosome that was not identified as a coverage outlier in the chromosomal analysis step. The sample is not considered covered enough for statistical purposes if you have fewer than 49 targets that can be used to do the statistics.

Each target is annotated with the following information:

Target number:
Targets are numbered in the order in which they occur in the genome. This information is used by the results report (see section 22.10.5).
Case coverage:
The normalized coverage of the target in the case sample.
Baseline coverage:
The normalized coverage of the target in the baseline.
The length of the target region.
The p-value corresponds to the probability that an observation identical to the CNV, or even more of an outlier, would occur by chance under the null hypothesis. The null hypothesis is that of no CNVs in the data. The p-value in the target-level output reflects the global evidence for a CNV at that particular target. The target-level p-values are combined to produce the region-level p-values in the region-level CNV output.
FDR-corrected p-value:
The FDR-corrected p-values correct for false positives arising from carrying out a very high number of statistical tests. The FDR-corrected p-value will, therefore, always be larger than the uncorrected p-value.
Fold-change (raw):
The fold-change of the normalized case coverage compared to the normalized baseline coverage. The normalization corrects for the effects of different library sizes between the different samples. Negative fold-changes indicate deletions, and positive fold-changes indicate amplifications. A fold-change of 1.0 represents identical coverages.
Fold-change (adjusted):
As observed by Li et al (2012, [Li et al., 2012]), the fold-changes (raw) depend on the coverage. Therefore, the fold-changes have to be adjusted for statistical differences between targets with different sequencing depths, before the statistical tests are carried out. The results of this adjustment are found in the "Fold-change (adjusted)" column. Note that sometimes, this will mean that a change that appears to be an amplification in the "raw" fold-change column may appear to be a deletion in the "adjusted" fold-change column, or vice versa. This is simply because for a given coverage level, the raw fold-changes were skewed towards amplifications (or deletions), and this effect was corrected in the adjustment. Note: if your sample purity is less than 100%, you need to take that into account when interpreting the fold-change values. This is described in more detail in section 22.10.2.
Region (joined targets):
The region to which this target was classified to belong. The region may or may not have been predicted to be a CNV.
Regional fold-change:
The adjusted fold-change of the region to which this target belongs. This fold-change value is computed from all targets constituting the region.
Regional p-value:
The p-value of the region to which this target belongs. This is the p-value calculated from combining the p-values of the individual targets inside the region.
Regional consequence:
If the target is included in a CNV region, this column will show "Gain" or "Loss", depending on the direction of change detected for the region. Note, however, that the change detected for the region may be inconsistent with the fold-change for a single target in the region. The reason for this is typically statistical noise at the single target.
Regional effect size:
The effect size of a target-level CNV reflects the magnitude of the observed fold-change of the CNV region in which the target was found. The effect size of a CNV is classified into the following categories: "Strong" or "Weak". The effect size is "Strong" if the fold-change exceeds the fold-change cutoff specified in the parameter steps. Otherwise, the effect size will be "Weak". Note, however, that "Weak" CNV calls will be filtered from the region-level output.
The comments can include useful information for interpreting the CNV calls. Possible comments in the target-level output are:
  1. Low coverage target: If the target had a coverage under the specified coverage cutoff, it will be classified as low-coverage. Low-coverage targets were not used in calculating the statistical models, and will not have p-values.
  2. Disproportionate chromosome coverage: If the target occurred on a chromosome that was detected to have disproportionate coverage. In this case, the target was not used to set up the statistical models.
  3. Atypical fold-change in region: If there is a discrepancy between the direction of fold-change detected for the target and the direction of fold-change detected for the region, then the fold-change of the target is "atypical" compared to the region. This is usually due to statistical noise, and the regional fold-change is likely to be more accurate in the interpretation, especially for large regions.