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Fixed a few typos and clarified some doc points.

This commit is contained in:
Geraldine Van der Auwera 2013-08-26 17:33:17 -04:00
parent 42d771f748
commit ed465cd2a5
2 changed files with 20 additions and 13 deletions
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2
protected/java/src/org/broadinstitute/sting/gatk/walkers/variantrecalibration/VariantDataManager.java
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@ -264,7 +264,7 @@ public class VariantDataManager {
Collections.sort( data, new VariantDatum.VariantDatumLODComparator() );
final int numToAdd = minimumNumber - trainingData.size();
if( numToAdd > data.size() ) {
throw new UserException.BadInput( "Error during negative model training. Minimum number of variants to use in training is larger than the whole call set. One can attempt to lower the --numBadVariants arugment but this is unsafe." );
throw new UserException.BadInput( "Error during negative model training. Minimum number of variants to use in training is larger than the whole call set. You can try lowering the --numBadVariants argument but this is unsafe." );
}
int index = 0, numAdded = 0;
while( numAdded < numToAdd && index < data.size() ) {

31
protected/java/src/org/broadinstitute/sting/gatk/walkers/variantrecalibration/VariantRecalibrator.java
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@ -79,14 +79,14 @@ import java.util.*;
* Create a Gaussian mixture model by looking at the annotations values over a high quality subset of the input call set and then evaluate all input variants.
*
* <p>
* This walker is the first pass in a two-stage processing step. This walker is designed to be used in conjunction with ApplyRecalibration walker.
* This walker is the first pass in a two-stage processing step. This walker is designed to be used in conjunction with the ApplyRecalibration walker.
*</p>
*
* <p>
* The purpose of the variant recalibrator is to assign a well-calibrated probability to each variant call in a call set.
* One can then create highly accurate call sets by filtering based on this single estimate for the accuracy of each call.
* You can then create highly accurate call sets by filtering based on this single estimate for the accuracy of each call.
* The approach taken by variant quality score recalibration is to develop a continuous, covarying estimate of the relationship
* between SNP call annotations (QD, MQ, HaplotypeScore, and ReadPosRankSum, for example) and the the probability that a SNP is a true genetic
* between SNP call annotations (QD, MQ, HaplotypeScore, and ReadPosRankSum, for example) and the probability that a SNP is a true genetic
* variant versus a sequencing or data processing artifact. This model is determined adaptively based on "true sites" provided
* as input, typically HapMap 3 sites and those sites found to be polymorphic on the Omni 2.5M SNP chip array. This adaptive
* error model can then be applied to both known and novel variation discovered in the call set of interest to evaluate the
@ -94,12 +94,7 @@ import java.util.*;
* the log odds ratio of being a true variant versus being false under the trained Gaussian mixture model.
* </p>
*
* <p>
* NOTE: In order to create the model reporting plots Rscript needs to be in your environment PATH (this is the scripting version of R, not the interactive version).
* See <a target="r-project" href="http://www.r-project.org">http://www.r-project.org</a> for more info on how to download and install R.
* </p>
*
* <h3>Input</h3>
* <h3>Inputs</h3>
* <p>
* The input raw variants to be recalibrated.
* <p>
@ -127,6 +122,17 @@ import java.util.*;
* -rscriptFile path/to/output.plots.R
* </pre>
*
* <h3>Caveat</h3>
*
* <ul>
* <li>The values used in the example above are only meant to show how the command lines are composed.
* They are not meant to be taken as specific recommendations of values to use in your own work, and they may be
* different from the values cited elsewhere in our documentation. For the latest and greatest recommendations on
* how to set parameter values for you own analyses, please read the Best Practices section of the documentation.</li>
*
* <li>In order to create the model reporting plots Rscript needs to be in your environment PATH (this is the scripting version of R, not the interactive version).
* See <a target="r-project" href="http://www.r-project.org">http://www.r-project.org</a> for more info on how to download and install R.</li>
* </ul>
*/
@DocumentedGATKFeature( groupName = HelpConstants.DOCS_CAT_VARDISC, extraDocs = {CommandLineGATK.class} )
@ -155,7 +161,7 @@ public class VariantRecalibrator extends RodWalker<ExpandingArrayList<VariantDat
* Training - Input variants which are found to overlap with these training sites are used to build the Gaussian mixture model.
* Truth - When deciding where to set the cutoff in VQSLOD sensitivity to these truth sites is used.
* Known - The known / novel status of a variant isn't used by the algorithm itself and is only used for reporting / display purposes.
* Bad - In addition to using the worst 3% of variants as compared to the Gaussian mixture model, we can also supplement the list with a database of known bad variants.
* Bad - In addition to using the set of worst ranked variants as compared to the Gaussian mixture model (see -numBad argument), we can also supplement the list with a database of known bad variants.
*/
@Input(fullName="resource", shortName = "resource", doc="A list of sites for which to apply a prior probability of being correct but which aren't used by the algorithm (training and truth sets are required to run)", required=true)
public List<RodBinding<VariantContext>> resource = Collections.emptyList();
@ -175,7 +181,8 @@ public class VariantRecalibrator extends RodWalker<ExpandingArrayList<VariantDat
/**
* The expected transition / transversion ratio of true novel variants in your targeted region (whole genome, exome, specific
* genes), which varies greatly by the CpG and GC content of the region. See expected Ti/Tv ratios section of the GATK best
* practices documentation (http://www.broadinstitute.org/gatk/guide/topic?name=best-practices) for more information. Normal whole genome values are 2.15 and for whole exome 3.2. Note
* practices documentation (http://www.broadinstitute.org/gatk/guide/topic?name=best-practices) for more information.
* Normal values are 2.15 for human whole genome values and 3.2 for human whole exomes. Note
* that this parameter is used for display purposes only and isn't used anywhere in the algorithm!
*/
@Argument(fullName="target_titv", shortName="titv", doc="The expected novel Ti/Tv ratio to use when calculating FDR tranches and for display on the optimization curve output figures. (approx 2.15 for whole genome experiments). ONLY USED FOR PLOTTING PURPOSES!", required=false)
@ -335,7 +342,7 @@ public class VariantRecalibrator extends RodWalker<ExpandingArrayList<VariantDat
engine.evaluateData( dataManager.getData(), badModel, true );
if( badModel.failedToConverge || goodModel.failedToConverge ) {
throw new UserException("NaN LOD value assigned. Clustering with this few variants and these annotations is unsafe. Please consider " + (badModel.failedToConverge ? "raising the number of variants used to train the negative model (via --numBad 3000, for example)." : "lowering the maximum number of Gaussians allowed for use in the model (via --maxGaussians 4, for example).") );
throw new UserException("NaN LOD value assigned. Clustering with this few variants and these annotations is unsafe. Please consider " + (badModel.failedToConverge ? "raising the number of variants used to train the negative model (via --numBadVariants 3000, for example)." : "lowering the maximum number of Gaussians allowed for use in the model (via --maxGaussians 4, for example).") );
}
engine.calculateWorstPerformingAnnotation( dataManager.getData(), goodModel, badModel );