From 72362eb267c5e44665aef2f40ca1a0e5e74b50ca Mon Sep 17 00:00:00 2001
From: Tim O'Donnell <timodonnell@gmail.com>
Date: Sat, 25 Nov 2017 10:12:23 -0500
Subject: [PATCH] README update (mhctools mention) and percentile rank tweaks
---
README.md | 18 ++++++++++++++++++
.../class1_affinity_predictor.py | 14 ++++++++------
test/test_class1_affinity_predictor.py | 3 +++
3 files changed, 29 insertions(+), 6 deletions(-)
diff --git a/README.md b/README.md
index bf02a88b..a4373e27 100644
--- a/README.md
+++ b/README.md
@@ -123,6 +123,24 @@ Run `mhcflurry-predict -h` for details.
See the [class1_allele_specific_models.ipynb](https://github.com/hammerlab/mhcflurry/blob/master/examples/class1_allele_specific_models.ipynb)
notebook for an overview of the Python API, including fitting your own predictors.
+## Scanning protein sequences for predicted epitopes
+
+The [mhctools](https://github.com/hammerlab/mhctools) package provides support
+for scanning protein sequences to find predicted epitopes. It supports MHCflurry
+as well as other binding predictors. Here is an example:
+
+```
+# First install mhctools if needed:
+pip install mhctools
+
+# Now generate predictions for protein sequences in FASTA format:
+mhctools \
+ --mhc-predictor mhcflurry \
+ --input-fasta-file INPUT.fasta \
+ --mhc-alleles A02:01,A03:01 \
+ --extract-subsequences \
+ --out RESULT.csv
+```
## Details on the downloadable models
diff --git a/mhcflurry/class1_affinity_prediction/class1_affinity_predictor.py b/mhcflurry/class1_affinity_prediction/class1_affinity_predictor.py
index 30f03ea1..c0ec13fb 100644
--- a/mhcflurry/class1_affinity_prediction/class1_affinity_predictor.py
+++ b/mhcflurry/class1_affinity_prediction/class1_affinity_predictor.py
@@ -747,23 +747,25 @@ class Class1AffinityPredictor(object):
df["prediction_low"] = numpy.exp(logs.quantile(0.05, axis=1))
df["prediction_high"] = numpy.exp(logs.quantile(0.95, axis=1))
- del df["normalized_allele"]
- del df["supported_peptide_length"]
if include_individual_model_predictions:
columns = sorted(df.columns, key=lambda c: c.startswith('model_'))
else:
columns = [
c for c in df.columns if c not in df_predictions.columns
]
+ columns.remove("normalized_allele")
+ columns.remove("supported_peptide_length")
- result = df[columns].copy()
if include_percentile_ranks:
if self.allele_to_percent_rank_transform:
- result["prediction_percentile"] = self.percentile_ranks(
- df.prediction, alleles=df.allele.values, throw=throw)
+ df["prediction_percentile"] = self.percentile_ranks(
+ df.prediction,
+ alleles=df.normalized_allele.values,
+ throw=throw)
+ columns.append("prediction_percentile")
else:
warnings.warn("No percentile rank information available.")
- return result
+ return df[columns].copy()
@staticmethod
def save_weights(weights_list, filename):
diff --git a/test/test_class1_affinity_predictor.py b/test/test_class1_affinity_predictor.py
index 4d7e67d6..952e4840 100644
--- a/test/test_class1_affinity_predictor.py
+++ b/test/test_class1_affinity_predictor.py
@@ -163,6 +163,7 @@ def test_class1_affinity_predictor_a0205_memorize_training_data():
peptides=df.peptide.values,
affinities=df.measurement_value.values,
)
+ predictor.calibrate_percentile_ranks()
ic50_pred = predictor.predict(df.peptide.values, allele=allele)
ic50_true = df.measurement_value.values
eq_(len(ic50_pred), len(ic50_true))
@@ -175,6 +176,8 @@ def test_class1_affinity_predictor_a0205_memorize_training_data():
ic50_pred_df = predictor.predict_to_dataframe(
df.peptide.values, allele=allele)
print(ic50_pred_df)
+ assert 'prediction_percentile' in ic50_pred_df.columns
+ assert ic50_pred_df.prediction_percentile.isnull().sum() == 0
ic50_pred_df2 = predictor.predict_to_dataframe(
df.peptide.values,
--
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