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package org.broadinstitute.hellbender.tools.copynumber.arguments;
import org.broadinstitute.barclay.argparser.Argument;
import org.broadinstitute.hellbender.tools.copynumber.GermlineCNVCaller;
import org.broadinstitute.hellbender.utils.param.ParamUtils;
import java.io.Serializable;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
/**
* @author Mehrtash Babadi <[email protected]>
*/
public final class GermlineDenoisingModelArgumentCollection implements Serializable {
private static final long serialVersionUID = 1L;
public static final String MAX_BIAS_FACTORS_LONG_NAME = "max-bias-factors";
public static final String MAPPING_ERROR_RATE_LONG_NAME = "mapping-error-rate";
public static final String INTERVAL_PSI_SCALE_LONG_NAME = "interval-psi-scale";
public static final String SAMPLE_PSI_SCALE_LONG_NAME = "sample-psi-scale";
public static final String DEPTH_CORRECTION_TAU_LONG_NAME = "depth-correction-tau";
public static final String LOG_MEAN_BIAS_STANDARD_DEVIATION_LONG_NAME = "log-mean-bias-standard-deviation";
public static final String INIT_ARD_REL_UNEXPLAINED_VARIANCE_LONG_NAME = "init-ard-rel-unexplained-variance";
public static final String NUM_GC_BINS_LONG_NAME = "num-gc-bins";
public static final String GC_CURVE_STANDARD_DEVIATION_LONG_NAME = "gc-curve-standard-deviation";
public static final String COPY_NUMBER_POSTERIOR_EXPECTATION_MODE_LONG_NAME = "copy-number-posterior-expectation-mode";
public static final String ENABLE_BIAS_FACTORS_LONG_NAME = "enable-bias-factors";
public static final String ACTIVE_CLASS_PADDING_HYBRID_MODE_LONG_NAME = "active-class-padding-hybrid-mode";
public enum CopyNumberPosteriorExpectationMode {
MAP("map"),
EXACT("exact"),
HYBRID("hybrid");
final String pythonArgumentString;
CopyNumberPosteriorExpectationMode(final String pythonArgumentString) {
this.pythonArgumentString = pythonArgumentString;
}
}
@Argument(
doc = "Maximum number of bias factors.",
fullName = MAX_BIAS_FACTORS_LONG_NAME,
minValue = 0,
optional = true
)
private int maxBiasFactors = 5;
@Argument(
doc = "Typical mapping error rate.",
fullName = MAPPING_ERROR_RATE_LONG_NAME,
minValue = 0.,
maxValue = 1.,
optional = true
)
private double mappingErrorRate = 0.01;
@Argument(
doc = "Typical scale of interval-specific unexplained variance.",
fullName = INTERVAL_PSI_SCALE_LONG_NAME,
minValue = 0.,
optional = true
)
private double intervalPsiScale = 0.001;
@Argument(
doc = "Typical scale of sample-specific correction to the unexplained variance.",
fullName = SAMPLE_PSI_SCALE_LONG_NAME,
minValue = 0.,
optional = true
)
private double samplePsiScale = 0.0001;
@Argument(
doc = "Precision of read depth pinning to its global value.",
fullName = DEPTH_CORRECTION_TAU_LONG_NAME,
minValue = 0.,
optional = true
)
private double depthCorrectionTau = 10000.0;
@Argument(
doc = "Standard deviation of log mean bias.",
fullName = LOG_MEAN_BIAS_STANDARD_DEVIATION_LONG_NAME,
minValue = 0.,
optional = true
)
private double logMeanBiasStandardDeviation = 0.1;
@Argument(
doc = "Initial value of ARD prior precisions relative to the scale of interval-specific " +
"unexplained variance.",
fullName = INIT_ARD_REL_UNEXPLAINED_VARIANCE_LONG_NAME,
minValue = 0.,
optional = true
)
private double initARDRelUnexplainedVariance = 0.1;
@Argument(
doc = "Number of bins used to represent the GC-bias curves.",
fullName = NUM_GC_BINS_LONG_NAME,
minValue = 2,
optional = true
)
private int numGCBins = 20;
@Argument(
doc = "Prior standard deviation of the GC curve from flat.",
fullName = GC_CURVE_STANDARD_DEVIATION_LONG_NAME,
minValue = 0.,
optional = true
)
private double gcCurveStandardDeviation = 1.;
@Argument(
doc = "The strategy for calculating copy number posterior expectations in the coverage denoising model.",
fullName = COPY_NUMBER_POSTERIOR_EXPECTATION_MODE_LONG_NAME,
optional = true
)
private CopyNumberPosteriorExpectationMode copyNumberPosteriorExpectationMode =
CopyNumberPosteriorExpectationMode.HYBRID;
@Argument(
doc = "Enable discovery of bias factors.",
fullName = ENABLE_BIAS_FACTORS_LONG_NAME,
optional = true
)
private boolean enableBiasFactors = true;
@Argument(
doc = "If copy-number-posterior-expectation-mode is set to HYBRID, CNV-active intervals determined " +
"at any time will be padded by this value (in the units of bp) in order to obtain the set of " +
"intervals on which copy number posterior expectation is performed exactly.",
fullName = ACTIVE_CLASS_PADDING_HYBRID_MODE_LONG_NAME,
optional = true
)
private int activeClassPaddingHybridMode = 50000;
/**
* Generates arguments for the python CLI tool. Note that 'enable_explicit_gc_bias_modeling' is added
* by {@link GermlineCNVCaller}.
*/
public List generatePythonArguments(final GermlineCNVCaller.RunMode runMode) {
final List arguments = new ArrayList<>(Arrays.asList(
String.format("--psi_s_scale=%e", samplePsiScale),
String.format("--mapping_error_rate=%e", mappingErrorRate),
String.format("--depth_correction_tau=%e", depthCorrectionTau),
String.format("--q_c_expectation_mode=%s", copyNumberPosteriorExpectationMode.pythonArgumentString)));
if (runMode == GermlineCNVCaller.RunMode.COHORT) {
arguments.addAll(Arrays.asList(
String.format("--max_bias_factors=%d", maxBiasFactors),
String.format("--psi_t_scale=%e", intervalPsiScale),
String.format("--log_mean_bias_std=%e", logMeanBiasStandardDeviation),
String.format("--init_ard_rel_unexplained_variance=%e", initARDRelUnexplainedVariance),
String.format("--num_gc_bins=%d", numGCBins),
String.format("--gc_curve_sd=%e", gcCurveStandardDeviation),
String.format("--active_class_padding_hybrid_mode=%d", activeClassPaddingHybridMode)));
if (enableBiasFactors) {
arguments.add("--enable_bias_factors=True");
} else {
arguments.add("--enable_bias_factors=False");
}
//this python argument is not exposed but we add it for completeness and logging purposes
arguments.add("--disable_bias_factors_in_active_class=False");
}
return arguments;
}
public void validate() {
ParamUtils.isPositive(maxBiasFactors,
String.format("Maximum number of bias factors (%s) must be positive.",
MAX_BIAS_FACTORS_LONG_NAME));
ParamUtils.isPositive(mappingErrorRate,
String.format("Mapping error rate (%s) must be positive.",
MAPPING_ERROR_RATE_LONG_NAME));
ParamUtils.isPositive(intervalPsiScale,
String.format("Typical scale of interval-specific unexplained variance (%s) must be positive.",
INTERVAL_PSI_SCALE_LONG_NAME));
ParamUtils.isPositive(samplePsiScale,
String.format("Typical scale of sample-specific correction to the unexplained variance (%s) must be positive.",
SAMPLE_PSI_SCALE_LONG_NAME));
ParamUtils.isPositive(depthCorrectionTau,
String.format("Precision of read depth pinning to its global value (%s) must be positive.",
DEPTH_CORRECTION_TAU_LONG_NAME));
ParamUtils.isPositive(logMeanBiasStandardDeviation,
String.format("Standard deviation of log mean bias (%s) must be positive.",
LOG_MEAN_BIAS_STANDARD_DEVIATION_LONG_NAME));
ParamUtils.isPositive(initARDRelUnexplainedVariance,
String.format("Initial value of ARD prior precision relative to the scale of " +
"interval-specific unexplained variance (%s) must be positive.",
INIT_ARD_REL_UNEXPLAINED_VARIANCE_LONG_NAME));
ParamUtils.isPositive(gcCurveStandardDeviation,
String.format("Prior standard deviation of the GC curve from flat (%s) must be positive.",
GC_CURVE_STANDARD_DEVIATION_LONG_NAME));
ParamUtils.isPositiveOrZero(activeClassPaddingHybridMode,
String.format("CNV-active interval padding in HYBRID copy-number posterior expectation mode (%s) must " +
"be non-negative.",
ACTIVE_CLASS_PADDING_HYBRID_MODE_LONG_NAME));
}
}
