diff --git a/downloads-generation/models_class1_pan/GENERATE.sh b/downloads-generation/models_class1_pan/GENERATE.sh
index 87261dc52822a741fd699aad70225aef1d8e2336..112c03b723bdaf56ba68faba9acf2803264b0c85 100755
--- a/downloads-generation/models_class1_pan/GENERATE.sh
+++ b/downloads-generation/models_class1_pan/GENERATE.sh
@@ -99,11 +99,10 @@ for kind in combined
do
MODELS_DIR="models.unselected.${kind}"
- # For now we calibrate percentile ranks only for alleles for which there
- # is training data. Calibrating all alleles would be too slow.
- # This could be improved though.
- ALLELE_LIST=$(bzcat "$MODELS_DIR/train_data.csv.bz2" | cut -f 1 -d , | grep -v allele | uniq | sort | uniq)
- ALLELE_LIST+=$(echo " " $(cat additional_alleles.txt | grep -v '#') )
+ # Older method calibrated only particular alleles. We are now calibrating
+ # all alleles, so this is commented out.
+ #ALLELE_LIST=$(bzcat "$MODELS_DIR/train_data.csv.bz2" | cut -f 1 -d , | grep -v allele | uniq | sort | uniq)
+ #ALLELE_LIST+=$(echo " " $(cat additional_alleles.txt | grep -v '#') )
mhcflurry-class1-select-pan-allele-models \
--data "$MODELS_DIR/train_data.csv.bz2" \
@@ -114,17 +113,17 @@ do
$PARALLELISM_ARGS
cp "$MODELS_DIR/train_data.csv.bz2" "models.${kind}/train_data.csv.bz2"
- # For now we calibrate percentile ranks only for alleles for which there
- # is training data. Calibrating all alleles would be too slow.
- # This could be improved though.
+ # We are now calibrating all alleles.
+ # Previously had argument: --allele $ALLELE_LIST \
time mhcflurry-calibrate-percentile-ranks \
--models-dir models.${kind} \
--match-amino-acid-distribution-data "$MODELS_DIR/train_data.csv.bz2" \
--motif-summary \
--num-peptides-per-length 100000 \
- --allele $ALLELE_LIST \
+ --alleles-per-work-chunk 10 \
--verbosity 1 \
$PARALLELISM_ARGS
+
done
cp $SCRIPT_ABSOLUTE_PATH .
diff --git a/mhcflurry/calibrate_percentile_ranks_command.py b/mhcflurry/calibrate_percentile_ranks_command.py
index c11dd4b1040f0eaca2bf2115da9f15f1fff53f55..c28df1ffdbee8f2611fdbef377924d5d7d9a6f2b 100644
--- a/mhcflurry/calibrate_percentile_ranks_command.py
+++ b/mhcflurry/calibrate_percentile_ranks_command.py
@@ -85,6 +85,12 @@ parser.add_argument(
type=int,
default=4096,
help="Keras batch size for predictions")
+parser.add_argument(
+ "--alleles-per-work-chunk",
+ type=int,
+ metavar="N",
+ default=1,
+ help="Number of alleles per work chunk. Default: %(default)s.")
parser.add_argument(
"--verbosity",
type=int,
@@ -120,7 +126,26 @@ def run(argv=sys.argv[1:]):
else:
alleles = predictor.supported_alleles
- alleles = sorted(set(alleles))
+ allele_set = set(alleles)
+
+ if predictor.allele_to_sequence:
+ # Remove alleles that have the same sequence.
+ new_allele_set = set()
+ sequence_to_allele = collections.defaultdict(set)
+ for allele in list(allele_set):
+ sequence_to_allele[predictor.allele_to_sequence[allele]].add(allele)
+ for equivalent_alleles in sequence_to_allele.values():
+ equivalent_alleles = sorted(equivalent_alleles)
+ keep = equivalent_alleles.pop(0)
+ new_allele_set.add(keep)
+ print(
+ "Sequence comparison reduced num alleles from",
+ len(allele_set),
+ "to",
+ len(new_allele_set))
+ allele_set = new_allele_set
+
+ alleles = sorted(allele_set)
distribution = None
if args.match_amino_acid_distribution_data:
@@ -171,7 +196,14 @@ def run(argv=sys.argv[1:]):
serial_run = not args.cluster_parallelism and args.num_jobs == 0
worker_pool = None
start = time.time()
- work_items = [{"allele": allele} for allele in alleles]
+
+ work_items = []
+ for allele in alleles:
+ if not work_items or len(
+ work_items[-1]['alleles']) >= args.alleles_per_work_chunk:
+ work_items.append({"alleles": []})
+ work_items[-1]['alleles'].append(allele)
+
if serial_run:
# Serial run
print("Running in serial.")
@@ -197,12 +229,13 @@ def run(argv=sys.argv[1:]):
chunksize=1)
summary_results_lists = collections.defaultdict(list)
- for (transforms, summary_results) in tqdm.tqdm(results, total=len(work_items)):
- predictor.allele_to_percent_rank_transform.update(transforms)
- if summary_results is not None:
- for (item, value) in summary_results.items():
- summary_results_lists[item].append(value)
- print("Done calibrating %d alleles." % len(work_items))
+ for work_item in tqdm.tqdm(results, total=len(work_items)):
+ for (transforms, summary_results) in work_item:
+ predictor.allele_to_percent_rank_transform.update(transforms)
+ if summary_results is not None:
+ for (item, value) in summary_results.items():
+ summary_results_lists[item].append(value)
+ print("Done calibrating %d alleles." % len(alleles))
if summary_results_lists:
for (name, lst) in summary_results_lists.items():
df = pandas.concat(lst, ignore_index=True)
@@ -223,12 +256,17 @@ def run(argv=sys.argv[1:]):
print("Predictor written to: %s" % args.models_dir)
-def do_calibrate_percentile_ranks(allele, constant_data=GLOBAL_DATA):
- return calibrate_percentile_ranks(
- allele,
- constant_data['predictor'],
- peptides=constant_data['calibration_peptides'],
- **constant_data["args"])
+def do_calibrate_percentile_ranks(alleles, constant_data=GLOBAL_DATA):
+ result_list = []
+ for (i, allele) in enumerate(alleles):
+ print("Processing allele", i + 1, "of", len(alleles))
+ result_item = calibrate_percentile_ranks(
+ allele,
+ constant_data['predictor'],
+ peptides=constant_data['calibration_peptides'],
+ **constant_data["args"])
+ result_list.append(result_item)
+ return result_list
def calibrate_percentile_ranks(
diff --git a/mhcflurry/class1_affinity_predictor.py b/mhcflurry/class1_affinity_predictor.py
index 9a3258cfe394f0743ca29c058b41e0d7e374e126..2b08a6ecd6e7c42bfb0c000d79b20f75f7cb6411 100644
--- a/mhcflurry/class1_affinity_predictor.py
+++ b/mhcflurry/class1_affinity_predictor.py
@@ -897,6 +897,20 @@ class Class1AffinityPredictor(object):
transform = self.allele_to_percent_rank_transform[normalized_allele]
return transform.transform(affinities)
except KeyError:
+ if self.allele_to_sequence:
+ # See if we have information for an equivalent allele
+ sequence = self.allele_to_sequence[normalized_allele]
+ other_alleles = [
+ other_allele for (other_allele, other_sequence)
+ in self.allele_to_sequence.items()
+ if other_sequence == sequence
+ ]
+ for other_allele in other_alleles:
+ if other_allele in self.allele_to_percent_rank_transform:
+ transform = self.allele_to_percent_rank_transform[
+ other_allele]
+ return transform.transform(affinities)
+
msg = "Allele %s has no percentile rank information" % (
allele + (
"" if allele == normalized_allele