Add TrainGCNV input specifying subset list of samples for training #294
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mwalker174
requested changes
Feb 10, 2022
wdl/TrainGCNV.wdl
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| @@ -111,7 +125,8 @@ workflow TrainGCNV { | |||
| } | |||
| } | |||
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| Array[Int] sample_indices = select_first([RandomSubsampleStringArray.subsample_indices_array, range(length(samples))]) | |||
| Array[Int] sample_indices = select_first([GetSubsampledIndices.subsample_indices_array, RandomSubsampleStringArray.subsample_indices_array, range(length(samples))]) | |||
| Array[String] sample_ids = select_first([GetSubsampledIndices.subsampled_strings_array, RandomSubsampleStringArray.subsampled_strings_array, samples]) | |||
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I think it would be simpler/clearer to move this into the scatter below:
scatter (i in sample_indices) {
String sample_ids = samples[i]
call cov.CondenseReadCounts as CondenseReadCounts {
input:
counts = count_files[i],
sample = samples[i],
num_bins = condense_num_bins,
expected_bin_size = condense_bin_size,
condense_counts_docker = condense_counts_docker,
runtime_attr_override=condense_counts_runtime_attr
}
}
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Also minor style comment, I might rename this to something making it clear it isn't an input. I usually add an extra underscore to the end, or you could call it maybe_subsetted_sample_ids for example.
wdl/Utils.wdl
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| RuntimeAttr default_attr = object { | ||
| cpu_cores: 1, | ||
| mem_gb: 3.75, |
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Suggested change
| mem_gb: 3.75, | |
| mem_gb: 1, |
wdl/Utils.wdl
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Comment on lines
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| all_strings = ['~{sep="','" all_strings}'] | ||
| subset_strings = {'~{sep="','" subset_strings}'} |
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It would be more robust/scalable to use write_lines() to write the strings to a file and read them in, rather than defining them inline (who knows what would happen if we fed this 10k samples for example).
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Updates since review:
Testing since review:
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Updates
Add
sample_ids_training_subsetinput toTrainGCNVto allow Terra users to specify a subsetted list of sample IDs on which to train the gCNV model, while keeping the same batching they will use for GatherBatchEvidence. Intended as an alternative to the random downsampling option if the user wants a greater level of control over the samples chosen.Testing
TrainGCNVontest_smalltest set and specified four sample IDs for training, out of order. Verified that the four were sorted according to the order of thesamplesinput (and other sample-level inputs) and the rest of the tasks were performed on the subset of samples.