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feat: add interval logic for l2g features #812

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@xyg123 xyg123 commented Oct 3, 2024

✨ Context

Adding interval based features to the l2g model, based on the feature list (opentargets/issues#3521).
opentargets/issues#3512

🛠 What does this PR implement

  • Implementation of PCHIC-based interval features for the L2G gene prediction model.
  • Added back interval processing steps into the L2G feature generation step.

🙈 Missing

More features from anderson + thurman.

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  • Do these changes cover one single feature (one change at a time)?
  • Did you read the contributor guideline?
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  • Did you write any new necessary tests?
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# feature will be the same for any gene associated with a studyLocus)
local_max.withColumn(
"regional_maximum",
f.max(local_feature_name).over(Window.partitionBy("studyLocusId")),
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Why is it maximum? According to the table and what we discussed it should be mean?
https://docs.google.com/spreadsheets/d/1wUs1AprRCCGItZmgDhc1fF5BtwCSosdzFv4NQ8V6Dtg/edit?gid=452826388#gid=452826388

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Thank you for the changes Jack!!!

The logic to build the features looks good! Please see my comments, but they are more along the lines of how we process the interval data in the L2G step.
I suggested processing all interval sources to make the process simpler, but since the code is accommodated to take source names and paths individually and changing it is a mess, it's also fine to leave it like that as long as the interval_paths parameter is correctly configured.

The implemented changes wouldn't run, because of the creation of a Interval dataset with a mismatching schema. I would encourage you to:

  • add any features you add to the test_l2g_feature_matrix.py suite, to make sure that the code doesnt crash
  • In the same file, add a semantic test for the common logic
  • Update the documentation pages
  • Pull dev branch to bring the changes to the feature matrix step

# intervals
"pchicMean",
"pchicMeanNeighbourhood",
"enhTssMean",
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I'd like to have more descriptive feature names

Suggested change
"enhTssMean",
"enhancerTssCorrelationMean",

"pchicMean",
"pchicMeanNeighbourhood",
"enhTssMean",
"enhTssMeanNeighbourhood",
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Suggested change
"enhTssMeanNeighbourhood",
"enhancerTssCorrelationMeanNeighbourhood",

"pchicMeanNeighbourhood",
"enhTssMean",
"enhTssMeanNeighbourhood",
"dhsPmtrMean",
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Suggested change
"dhsPmtrMean",
"dhsPromoterCorrelationMean",

"enhTssMean",
"enhTssMeanNeighbourhood",
"dhsPmtrMean",
"dhsPmtrMeanNeighbourhood",
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Suggested change
"dhsPmtrMeanNeighbourhood",
"dhsPromoterCorrelationMeanNeighbourhood",

@@ -282,6 +289,11 @@ class LocusToGeneConfig(StepConfig):
wandb_run_name: str | None = None
hf_hub_repo_id: str | None = "opentargets/locus_to_gene"
download_from_hub: bool = True
# interval_sources: dict[str, str] | None = {
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I would remove this

lambda x, y: x.unionByName(y, allowMissingColumns=True),
# create interval instances by parsing each source
[
Intervals.from_source(
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See my comment in config.py. I wouldn't split the logic into different sources of data so you don't have to iterate and then perform the union

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I'd make this more simple. We are now not so interested in adjusting which interval sources we might want. Because we only use it for L2G, I think the process is simpler if we compute all interval data, and then we pick what we want to include based on the features.
This way you only need to provide one path for the intervals (that leads to the folder that contains them all), compute everything, and then let the list of features decide what is ingested.

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With the processed interval dataset, we still want to update it with the latest gene index every release right?
But this join is a part of the interval processing step, and it is done differently for each interval source, some source have gene names attached already, while others require an overlap of genomic regions.

So, Maybe we can bring back v2g step in a dag (but only intervals, "intervals" step)?

Or we process it in this list format, which I agree looks very ugly and messy.

how="inner",
)
.drop("start", "end", "vi_chromosome", "position"),
_schema=Intervals.get_schema(),
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I don't think this works. You're converting the interval dataset into a variant to gene format, so the schema has changed.
What I would do: this logic converts the raw intervals into variant/gene relationships. I would create a method in the Intervals dataset (with a name different to v2g) to compute this. This could be useful later on, so having it inside L2G is not great.

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Yes, so I moved this into a function to overlap with variant index in interval datasets, and I included "variantId" into the interval schema, is this the easiest way to address this without making a new v2g style dataset?

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I don't think it is ideal, as the unit of this dataset would be a variant instead of an interval. Would collect the variants into a list work?

@@ -1,6 +1,6 @@
"""Factory that computes features based on an input list."""

from __future__ import annotations
from __future__ import annotations # noqa: I001
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Why is this?

@@ -127,6 +135,12 @@ class FeatureFactory:
"vepMeanNeighbourhood": VepMeanNeighbourhoodFeature,
"vepMaximum": VepMaximumFeature,
"vepMaximumNeighbourhood": VepMaximumNeighbourhoodFeature,
"pchicMean": PchicMeanFeature,
"pchicMeanNeighbourhood": PchicMeanNeighbourhoodFeature,
"enhTssMean": EnhTssMeanFeature,
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update feature names

@github-actions github-actions bot added size-L and removed size-M labels Oct 17, 2024
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3 participants