Better error bars for samples with small contamination in CalculateContamination #7003
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fleharty
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Jan 28, 2021
| final double contamination = contaminationOppositeDepth / totalDepthWeightedByOppositeFrequency; | ||
| final double contaminationEstimate = contaminationOppositeDepth / totalDepthWeightedByOppositeFrequency; | ||
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| final double coeff1 = homs.stream().mapToDouble(ps -> oppositeAlleleFrequency.applyAsDouble(ps) * ps.getTotalCount()).sum(); |
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Could you specify what equation this is constructing by putting the tex in javadoc?
| final DoubleUnaryOperator errorFunc = c -> homs.isEmpty() ? 1 : Math.sqrt(coeff1*c*(1-c) + coeff2*c*c) / totalDepthWeightedByOppositeFrequency; | ||
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| // we're going to binary search to find the largest contamination whose expected standard error brings it within range of | ||
| // our estimate. That is, suppose we estimate a contamination of 0.03 and the standard error of 0.05 is 0.02. Then 0.05 is |
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standard error of 0.05
Could you make this clearer?
| // the upper end of our 1-sigma confidence interval. | ||
| // this binary search is far from optimized (in fact we could solve this explicitly with a messy closed-form solution) | ||
| // but it converges to a precision of 1e-6 in 20 iterations of the square root of a linear function. This is fast enough. | ||
| double top = 1.0; |
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Could you either find a standard binary search, or generalize this in MathUtils?
If you add it in MathUtils, create a simple test for it.
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Back to you @fleharty. |
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@davidbenjamin |
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@fleharty github wants your official approving review to merge. |
fleharty
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May 12, 2021
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@davidbenjamin Sorry about that, should work now. |
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@fleharty this is for you.