Add generic constraint functions: annotate_with_mu(), count_variants(), downsampling_counts_expr(), filter_vep_transcript_csqs(), combine_functions(), filter_x_nonpar(), and filter_y_nonpar() #481
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averywpx
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gnomad/utils/constraint.py
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| agg[f"singleton_downsampling_counts_{pop}"] = downsampling_counts_expr( | ||
| freq_expr, freq_meta_expr, pop, singleton=True | ||
| ) | ||
| # count the variants that have same combination of `grouping` |
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Just double check that my description about counting the variants is accurate!
gnomad/utils/constraint.py
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| else: | ||
| return hl.int(freq_expr[i].AC > 0) | ||
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| # mark variants met with specified criteria in each downsampling as 1 |
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Same comment as above! Want to make sure the description is accurate :)
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Hmm, so the hl.map will apply _get_criteria to each downsampling requested. so that will return a list of 1's and 0's for each variant, where if the variant exists in the downsampling (AC >0) and any specified criteria is met then it is given a 1 in the list, otherwise it is given a zero.
Then the hl.agg.array_sum is a aggregation expression that when applied, will sum up all of these arrays of 1's and 0's to give an array that has one element per downsample in pop that is a count of variants that exist in the downsampling (AC >0) and, is requested, meet any specified criteria (singleton or max_af).
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A little bit more to clarify so we can make the comment more accurate: when you said "that is a count of variants that exist in the downsampling", it counts variants that have the same grouping annotation right?
gnomad/utils/constraint.py
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| lambda x: x[0] | ||
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| # variants met with specified criteria will be marked as 1 |
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Please let me know if my understanding of _get_criteria's job is correct :)
gnomad/utils/constraint.py
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| lambda x: x[0] | ||
| ) | ||
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| # variants met with specified criteria will be marked as 1 |
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Provided a suggested change, though actually since this is a function, we should probably just have a docstring here instead of the comment.
| # variants met with specified criteria will be marked as 1 | |
| # Variants meeting specified criteria (`singleton` or `max_af`) if requested or with an AC > 0 will return 1. |
gnomad/utils/constraint.py
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| else: | ||
| return hl.int(freq_expr[i].AC > 0) | ||
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| # mark variants met with specified criteria in each downsampling as 1 |
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Hmm, so the hl.map will apply _get_criteria to each downsampling requested. so that will return a list of 1's and 0's for each variant, where if the variant exists in the downsampling (AC >0) and any specified criteria is met then it is given a 1 in the list, otherwise it is given a zero.
Then the hl.agg.array_sum is a aggregation expression that when applied, will sum up all of these arrays of 1's and 0's to give an array that has one element per downsample in pop that is a count of variants that exist in the downsampling (AC >0) and, is requested, meet any specified criteria (singleton or max_af).
Co-authored-by: jkgoodrich <[email protected]>
gnomad/utils/constraint.py
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| :param max_af: Maximum variant allele frequency to keep. By default no allele frequency cutoff is applied. | ||
| :return: Aggregation Expression for an array of the variant counts in downsamplings for specified population. | ||
| """ | ||
| # Get indices of dictionaries in meta dictionaries that only have the "downsampling" key with specified "group" and "pop" values |
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Is there a consistent pattern of adding period at the end of each comment? I remember there are some suggestions in the constraint repo about adding period.
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As discussed, lets move to adding a period at the end of all comments
gnomad/utils/constraint.py
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| :param max_af: Maximum variant allele frequency to keep. By default no allele frequency cutoff is applied. | ||
| :return: Aggregation Expression for an array of the variant counts in downsamplings for specified population. | ||
| """ | ||
| # Get indices of dictionaries in meta dictionaries that only have the "downsampling" key with specified "group" and "pop" values |
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As discussed, lets move to adding a period at the end of all comments
Co-authored-by: jkgoodrich <[email protected]>
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Yay!!! Thank you so much for all the feedback! |
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