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Commit 5c5b75fc authored by Tim O'Donnell's avatar Tim O'Donnell
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mhcflurry/class1_presentation_predictor.py
+ 65
− 13
View file @ 5c5b75fc
......@@ -89,7 +89,8 @@ class Class1PresentationPredictor(object):
verbose=1,
throw=True):
"""
Predict binding affinities.
Predict binding affinities across samples (each corresponding to up to
six MHC I alleles).
Two modes are supported: each peptide can be evaluated for binding to
any of the alleles in any sample (this is what happens when sample_names
......@@ -97,7 +98,8 @@ class Class1PresentationPredictor(object):
of the sample given by the i'th entry in sample_names.
For example, if we don't specify sample_names, then predictions
are taken for all combinations of samples and peptides:
are taken for all combinations of samples and peptides, for a result
size of num peptides * num samples:
>>> predictor = Class1PresentationPredictor.load()
>>> predictor.predict_affinity(
......@@ -114,7 +116,8 @@ class Class1PresentationPredictor(object):
3 PEPTIDE 1 sample2 34362.109211 C0202
In contrast, here we specify sample_names, so peptide is evaluated for
binding the alleles in the corresponding sample:
binding the alleles in the corresponding sample, for a result size equal
to the number of peptides:
>>> predictor.predict_affinity(
... peptides=["SIINFEKL", "PEPTIDE"],
......@@ -132,14 +135,14 @@ class Class1PresentationPredictor(object):
Parameters
----------
peptides : list of string
Peptide sequences
alleles : dict of string -> list of string
Keys are sample names, values are the alleles (genotype) for
that sample
sample_names : list of string [same length as peptides]
Sample names corresponding to each peptide. These are used to
lookup the alleles for each peptide in the alleles dict. If not
specified, then predictions are generated for all sample genotypes
across all peptides.
Sample names corresponding to each peptide. If None, then
predictions are generated for all sample genotypes across all
peptides.
include_affinity_percentile : bool
Whether to include affinity percentile ranks
verbose : int
......@@ -469,11 +472,33 @@ class Class1PresentationPredictor(object):
the binding affinity and processing predictions and other intermediate
results.
Example:
>>> predictor = Class1PresentationPredictor.load()
>>> predictor.predict_to_dataframe(
... peptides=["SIINFEKL", "PEPTIDE"],
... n_flanks=["NNN", "SNS"],
... c_flanks=["CCC", "CNC"],
... alleles={
... "sample1": ["A0201", "A0301", "B0702"],
... "sample2": ["A0101", "C0202"],
... },
... verbose=0)
peptide n_flank c_flank peptide_num sample_name affinity best_allele processing_score presentation_score
0 SIINFEKL NNN CCC 0 sample1 12906.787792 A0201 0.802466 0.140365
1 PEPTIDE SNS CNC 1 sample1 36827.681130 B0702 0.105260 0.004059
2 SIINFEKL NNN CCC 0 sample2 3588.413748 C0202 0.802466 0.338647
3 PEPTIDE SNS CNC 1 sample2 34362.109211 C0202 0.105260 0.004317
You can also specify sample_names, in which case peptide is evaluated
for binding the alleles in the corresponding sample only. See
`predict_affinity` for an examples.
Parameters
----------
peptides : list of string
Peptide sequences
alleles : list of string or string -> list of string dict
alleles : list of string or dict of string -> list of string
If you are predicting for a single sample, pass a list of strings
(up to 6) indicating the genotype. If you are predicting across
multiple samples, pass a dict where the keys are (arbitrary)
......@@ -481,9 +506,8 @@ class Class1PresentationPredictor(object):
sample.
sample_names : list of string [same length as peptides]
If you are passing a dict for 'alleles', you can use this
argument to
specify which peptides go with which samples. If it is None,
then predictions will be performed for each peptide across all
argument to specify which peptides go with which samples. If it is
None, then predictions will be performed for each peptide across all
samples.
n_flanks : list of string [same length as peptides]
Upstream sequences before the peptide. Sequences of any length can
......@@ -584,6 +608,34 @@ class Class1PresentationPredictor(object):
"""
Predict presentation across protein sequences.
Example:
>>> predictor = Class1PresentationPredictor.load()
>>> predictor.predict_sequences(
... sequences={
... 'protein1': "MDSKGSSQKGSRLLLLLVVSNLL",
... 'protein2': "SSLPTPEDKEQAQQTHH",
... },
... alleles={
... "sample1": ["A0201", "A0301", "B0702"],
... "sample2": ["A0101", "C0202"],
... },
... result="filtered",
... comparison_quantity="affinity",
... filter_value=500,
... verbose=0)
sequence_name pos peptide n_flank c_flank sample_name affinity best_allele affinity_percentile processing_score presentation_score
0 protein1 13 LLLLVVSNL MDSKGSSQKGSRL L sample1 38.206225 A0201 0.380125 0.017644 0.571060
1 protein1 14 LLLVVSNLL MDSKGSSQKGSRLL sample1 42.243472 A0201 0.420250 0.090984 0.619213
2 protein1 5 SSQKGSRLL MDSKG LLLVVSNLL sample2 66.749223 C0202 0.803375 0.383608 0.774468
3 protein1 6 SQKGSRLLL MDSKGS LLVVSNLL sample2 178.033474 C0202 1.820000 0.275019 0.482206
4 protein1 13 LLLLVVSNLL MDSKGSSQKGSRL sample1 202.208167 A0201 1.112500 0.058782 0.261320
5 protein1 12 LLLLLVVSNL MDSKGSSQKGSR L sample1 202.506582 A0201 1.112500 0.010025 0.225648
6 protein2 0 SSLPTPEDK EQAQQTHH sample1 335.529377 A0301 1.011750 0.010443 0.156798
7 protein2 0 SSLPTPEDK EQAQQTHH sample2 353.451759 C0202 2.674250 0.010443 0.150753
8 protein1 8 KGSRLLLLL MDSKGSSQ VVSNLL sample2 410.327286 C0202 2.887000 0.121374 0.194081
9 protein1 5 SSQKGSRL MDSKG LLLLVVSNLL sample2 477.285954 C0202 3.107375 0.111982 0.168572
Parameters
----------
sequences : str, list of string, or string -> string dict
......@@ -665,7 +717,7 @@ class Class1PresentationPredictor(object):
if all(isinstance(a, string_types) for a in alleles):
# Case (2) - a simple list of alleles
alleles = {
'genotype': alleles
'sample1': alleles
}
else:
# Case (3) - a list of lists
......@@ -730,7 +782,7 @@ class Class1PresentationPredictor(object):
result_df.peptide_num.map(pandas.Series(sequence_names)))
result_df.insert(
1,
"position_in_sequence",
"pos",
result_df.peptide_num.map(pandas.Series(position_in_sequence)))
del result_df["peptide_num"]
......
test/test_doctest.py
+ 3
− 0
View file @ 5c5b75fc
"""
Run doctests.
"""
import logging
logging.getLogger('matplotlib').disabled = True
logging.getLogger('tensorflow').disabled = True
import os
import doctest
......
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