def test_fastq_unzipped():
for thisdata in [data, datagz]:
# isntanciation
f = fastq.FastQ(thisdata)
assert f.data_format == "Illumina_1.8+"
# count lines
# rune it twice because we want to make sure re-running count_lines
# (decompression with zlib) works when run again.
assert f.count_lines() == 1000
assert f.count_lines() == 1000
assert f.count_reads() == 250
assert f.count_reads() == 250
# extract head of the file into an unzipped file
ft = TempFile()
f.extract_head(100, ft.name)
fcheck = fastq.FastQ(ft.name)
assert fcheck.count_lines() == 100
ft.delete()
# extract head of the file and zip output
ft = TempFile(suffix=".gz")
f.extract_head(100, ft.name)
fcheck = fastq.FastQ(ft.name)
assert fcheck.count_lines() == 100
ft.delete()
with FastQ(thisdata) as ff:
assert len(ff) == 250
with TempFile() as fh:
selection = f.select_random_reads(10, fh.name)
f.select_random_reads(selection, fh.name)
def test_fastq_unzipped():
for thisdata in [data, datagz]:
# isntanciation
f = fastq.FastQ(thisdata)
assert f.data_format == "Illumina_1.8+"
# count lines
# rune it twice because we want to make sure re-running count_lines
# (decompression with zlib) works when run again.
assert f.count_lines() == 1000
assert f.count_lines() == 1000
assert f.count_reads() == 250
assert f.count_reads() == 250
# extract head of the file into an unzipped file
ft = TempFile()
f.extract_head(100, ft.name)
fcheck = fastq.FastQ(ft.name)
assert fcheck.count_lines() == 100
ft.delete()
# extract head of the file and zip output
ft = TempFile(suffix=".gz")
f.extract_head(100, ft.name)
fcheck = fastq.FastQ(ft.name)
assert fcheck.count_lines() == 100
ft.delete()
with FastQ(thisdata) as ff:
assert len(ff) == 250
with TempFile() as fh:
selection = f.select_random_reads(10, fh.name)
f.select_random_reads(selection, fh.name)
def test_snpeff():
# a custom refrence
fh_log = TempFile()
mydata = snpeff.SnpEff(reference=sequana_data("JB409847.gbk"), log=fh_log.name)
with TempFile() as fh:
mydata.launch_snpeff(sequana_data("JB409847.vcf"), fh.name)
fh_log.delete()
# cleanup
try:
os.remove("snpEff.config")
except:
pass
try:
os.remove("snpEff_genes.txt")
except:
pass
try:
os.remove("snpEff_summary.html")
except:
pass
try:
snpeff.SnpEff(reference="dummy")
assert False
except SystemExit:
assert True
except:
assert False
def test_attrdict():
a = tools.AttrDict(value=1)
assert a.value == 1
assert 'value' in list(a.keys())
assert 1 in (a.values())
a.description = 'test'
assert a['description'] == 'test'
a['output'] = 'txt'
assert a.output == 'txt'
d = {'a':{'b':1}, 'aa':2}
ad = tools.AttrDict(**d)
assert ad.a.b == 1
ad.a.b = 2
assert ad.a.b == 2
ad['d'] = 4
assert ad.d == 4
try:
ad.update(1)
assert False
except:
assert True
# check json capabilities
fh = TempFile()
js = ad.to_json()
ad.to_json(filename=fh.name)
ad.from_json(fh.name)
fh.delete()
def test_add_locus_with_modification():
# Alter the original GBK to alter the locus name
data = open(sequana_data("JB409847.gbk"), "r").read()
newdata = data.replace("JB409847", "DUMMY_JB409847")
fh = TempFile(suffix="gbk")
with open(fh.name, 'w') as fout:
fout.write(newdata)
# Now we read this new GBK file that has a different locus name as
# compared to the fasta
mydata = snpeff.SnpEff(reference=fh.name)
# Here is the corresponding FASTA
fasta = sequana_data("JB409847.fasta")
with TempFile(suffix="fasta") as fh2:
mydata.add_locus_in_fasta(fasta, fh2.name)
# In theory, in the newly created fasta file, we should find back the
# DUMMY tag
# cleanup
try:
os.remove("snpEff.config")
except:
pass
data = open(fh2.name, "r").read()
assert "DUMMY" in data
fh.delete()
def test_attrdict():
a = tools.AttrDict(value=1)
assert a.value == 1
assert 'value' in list(a.keys())
assert 1 in (a.values())
a.description = 'test'
assert a['description'] == 'test'
a['output'] = 'txt'
assert a.output == 'txt'
d = {'a': {'b': 1}, 'aa': 2}
ad = tools.AttrDict(**d)
assert ad.a.b == 1
ad.a.b = 2
assert ad.a.b == 2
ad['d'] = 4
assert ad.d == 4
try:
ad.update(1)
assert False
except:
assert True
# check json capabilities
fh = TempFile()
js = ad.to_json()
ad.to_json(filename=fh.name)
ad.from_json(fh.name)
fh.delete()
def test_yeast_annotations():
from easydev import gsf
filename = gsf('msdas', "data", "YEAST_raw_sample.csv")
r = MassSpecReader(filename, verbose=verbose)
a = AnnotationsYeast(r, verbose=verbose)
a.df = a.df.ix[0:200] # 200 is enough to get gene name cases and ambiguous gene names cases
# e.g., ALD3_YEAST ['P54114', 'P40047']
a.get_uniprot_entries()
a.update_mapping()
a.set_annotations()
a.annotations.Sequence
t = TempFile()
a.to_csv(t.name)
t.delete()
t = TempFile()
a.to_pickle("test", overwrite=True)
try:
a.to_pickle("test", overwrite=False)
assert False
except IOError:
assert True
a.read_pickle("YEAST_annotations_test.pkl")
# create constructor given the annotations
a = AnnotationsYeast(r, verbose=verbose, annotations="YEAST_annotations_test.pkl")
a.get_uniprot_entries() # populate entry and entry_names in the df
a.plot_goid_histogram()
#cleanup
os.remove("YEAST_annotations_test.pkl")
def test_pacbio_input_bam(tmpdir):
# we need a summary and a bunch of images
filename = sequana_data("summary_pacbio_qc1.json")
# mock the PNG files found in the summary
import json
summary = json.load(open(filename))
pngname = sequana_data("no_data.jpg")
summary["images"]["gc_vs_length"] = pngname
summary["images"]["hist_gc_content"] = pngname
summary["images"]["hist_read_length"] = pngname
summary["images"]["hist_snr"] = pngname
summary["images"]["hist_zmw"] = pngname
summary_file = TempFile()
with open(summary_file.name, "w") as ff:
json.dump(summary, ff)
# Now that we have this new summary file, let us use it
# we also need an output handler
ff = TempFile()
from sequana.utils import config
config.output_dir = "/tmp"
#here, ff.name is of the form /tmp/djhfjh4dz so we need to remove the /tmp
pacbio_input_bam.PacbioInputBAMModule(summary_file.name, ff.name.split("/")[1])
# cleanup
summary_file.delete()
ff.delete()
def test_pacbio_input_bam(tmpdir):
# we need a summary and a bunch of images
filename = sequana_data("summary_pacbio_qc1.json")
# mock the PNG files found in the summary
import json
summary = json.load(open(filename))
pngname = sequana_data("no_data.jpg")
summary["images"]["gc_vs_length"] = pngname
summary["images"]["hist_gc_content"] = pngname
summary["images"]["hist_read_length"] = pngname
summary["images"]["hist_snr"] = pngname
summary["images"]["hist_zmw"] = pngname
summary_file = TempFile()
with open(summary_file.name, "w") as ff:
json.dump(summary, ff)
# Now that we have this new summary file, let us use it
# we also need an output handler
ff = TempFile()
from sequana.utils import config
config.output_dir = "/tmp"
#here, ff.name is of the form /tmp/djhfjh4dz so we need to remove the /tmp
pacbio_input_bam.PacbioInputBAMModule(summary_file.name,
ff.name.split("/")[1])
# cleanup
summary_file.delete()
ff.delete()
def test_sequana_config():
s = snaketools.Module("compressor")
config = snaketools.SequanaConfig(s.config)
assert config.config.get("compressor")["source"] == "fastq.gz"
assert config.config.get("kraken:dummy") == None
# --------------------------------- tests different constructors
config = snaketools.SequanaConfig()
config = snaketools.SequanaConfig({"test": 1})
assert config.config.test == 1
# with a dictionary
config = snaketools.SequanaConfig(config.config)
# with a sequanaConfig instance
config = snaketools.SequanaConfig(config)
# with a non-yaml file
try:
json = sequana_data('test_summary_fastq_stats.json')
config = snaketools.SequanaConfig(json)
assert False
except:
assert True
try:
config = snaketools.SequanaConfig("dummy_dummy")
assert False
except:
assert True
# Test an exception
s = snaketools.Module("compressor")
config = snaketools.SequanaConfig(s.config)
config._recursive_update(config._yaml_code,
{"input_directory_dummy": "test"})
#config.check_config_with_schema(s.schema_config)
# loop over all pipelines, read the config, save it and check the content is
# identical. This requires to remove the templates. We want to make sure the
# empty strings are kept and that "no value" are kept as well
#
# field1: ""
# field2:
#
# is unchanged
from easydev import TempFile
output = TempFile(suffix=".yaml")
for pipeline in snaketools.pipeline_names:
config_filename = Module(pipeline)._get_config()
cfg1 = SequanaConfig(config_filename)
cfg1.cleanup() # remove templates and strip strings
cfg1.save(output.name)
cfg2 = SequanaConfig(output.name)
assert cfg2._yaml_code == cfg1._yaml_code
cfg2._update_config()
assert cfg1.config == cfg2.config
output.delete()
def test_input():
from easydev import TempFile
fh = TempFile(suffix=".fastq.gz")
filename = sequana_data('Hm2_GTGAAA_L005_R2_001.fastq.gz')
df = fastq_head.main(
[prog, '--input', filename, '--nlines', "100", "--output", fh.name])
df = fastq_head.main([prog, filename, "100", fh.name])
fh.delete()
def score_sc2(self, prediction_file):
fh = TempFile()
_, gs2 = self.download_gs()
script = self.classpath + os.sep + "DREAM_Olfaction_scoring_Q2.pl"
cmd = "perl %s %s %s %s"
cmd = cmd % (script, prediction_file, fh.name, gs2)
shellcmd(cmd)
df = pd.read_csv(fh.name, sep='\t', index_col=None).ix[0]
fh.delete()
return df
def score_sc2(self, prediction_file):
fh = TempFile()
_, gs2 = self.download_gs()
script = self.classpath + os.sep + "DREAM_Olfaction_scoring_Q2.pl"
cmd = "perl %s %s %s %s"
cmd = cmd % (script, prediction_file, fh.name, gs2)
shellcmd(cmd)
df = pd.read_csv(fh.name, sep='\t', index_col=None).ix[0]
fh.delete()
return df
def test_sequana_config():
s = snaketools.Module("quality_control")
config = snaketools.SequanaConfig(s.config)
assert config.config.get("kraken:dummy", "test") == "test"
assert config.config.get("kraken:dummy") == None
# --------------------------------- tests different constructors
config = snaketools.SequanaConfig()
config = snaketools.SequanaConfig({"test":1})
assert config.config.test == 1
# with a dictionary
config = snaketools.SequanaConfig(config.config)
# with a sequanaConfig instance
config = snaketools.SequanaConfig(config)
# with a non-yaml file
try:
json = sequana_data('test_summary_fastq_stats.json')
config = snaketools.SequanaConfig(json)
assert False
except:
assert True
try:
config = snaketools.SequanaConfig("dummy_dummy")
assert False
except:
assert True
# Test an exception
s = snaketools.Module("quality_control")
config = snaketools.SequanaConfig(s.config)
config._recursive_update(config._yaml_code, {"input_directory_dummy": "test"})
# loop over all pipelines, read the config, save it and check the content is
# identical. This requires to remove the templates. We want to make sure the
# empty strings are kept and that "no value" are kept as well
#
# field1: ""
# field2:
#
# is unchanged
from easydev import TempFile
output = TempFile(suffix=".yaml")
for pipeline in snaketools.pipeline_names:
config_filename = Module(pipeline)._get_config()
cfg1 = SequanaConfig(config_filename)
cfg1.cleanup() # remove templates and strip strings
cfg1.save(output.name)
cfg2 = SequanaConfig(output.name)
assert cfg2._yaml_code == cfg1._yaml_code
cfg2._update_config()
assert cfg1.config == cfg2.config
output.delete()
def sbmlqual_from_datasets(identifier):
# a simple model
s1 = SIF()
s2 = SIF(cnodata("PKN-" + identifier + ".sif"))
fh = TempFile()
s2.to_sbmlqual(fh.name)
s1.read_sbmlqual(fh.name)
fh.delete()
assert s1 == s2
s3 = SIF(cnodata("PKN-" + identifier + ".xml"))
assert s1 == s3 and s2 == s3
def test_phosphogrid():
m = MassSpecReader(get_yeast_small_data(), verbose=False)
gene_names = set(list(m.df.Protein))
p = phosphogrid.PhosphoGRID(directory = "../share/data")
p.run(gene_names=gene_names)
fh = TempFile(suffix='.sif')
p.export2sif(filename=fh.name)
p.plot()
#p.run()
fh.delete()
def score_A(self, filename):
from easydev import TempFile
fh = TempFile()
script = self._pj(
[self.classpath, 'weighted_average_concordance_index.pl'])
datadir = self._pj([self.classpath, 'data'])
cmd = "perl %s %s %s %s"
cmd = cmd % (script, filename, datadir, fh.name)
shellcmd(cmd, verbose=True, ignore_errors=True)
try:
df = pd.read_csv(fh.name, sep='\t', header=None)
except:
print("Something wrong in the Scoring while executing \n %s. " %
cmd)
print(
"\n The D7C4 challenge requires a Perl package to be installed"
)
print("See D7C4 documentation e.g., on dreamtools.readthedocs.org")
import sys
sys.exit(1)
df.columns = [
'DrugID', 'probabilistic c-index',
'weighted probabilistic c-index', 'zscores'
]
df = df.set_index('DrugID')
fh.delete()
ws = (df.sum() / df.sum().ix['zscores'])
ws = ws.ix['weighted probabilistic c-index']
results = df.mean()
results['weight average probabilistic c-index'] = ws
del results['zscores']
# Finally compute pvalues based on precomputed scores
precomp = pd.read_csv(self._pj([
self.classpath, 'data', 'DREAM7_DrugSensitivity1_drug_zscores.txt'
]),
sep='\t',
skiprows=6,
header=None)
overall_mean = precomp.ix[31][1]
overall_var = precomp.ix[31][2]
pval = 1 - (.5 * (math.erf(
(ws - overall_mean) / (math.sqrt(2 * overall_var))) + 1))
results['weight average probabilistic c-index p-value'] = pval
return {'Results': results}
def test_read_ic50():
# -------------------------------- functionalities
r = IC50(ic50_test)
# we can also instanciate from a valid dataframe
r = IC50(r)
# test repr
r
# and print statement
print(r)
# the copy method
assert r == r.copy()
r.hist()
r.plot_ic50_count()
r.cosmicIds
f = TempFile()
r.to_csv(f.name)
f.delete()
# columns may be duplicated
r = IC50(ic50_test)
df = pd.concat([r.df, r.df[999]], axis=1)
# create new instance that should raise an error
try:
IC50(df)
assert False
except:
assert True
# ---------------------------------------- different IC50 formats
# test all files available
for key in testing.keys() :
filename = testing[key].location
if filename.startswith('ic50_test'):
ic = IC50(filename)
# some specific checks:
#ic = IC50(testing['ic50_test_header_drug_prefix_only'].location)
#assert ic.df.shape == (2,2)
#assert all(ic.df.columns == ['1','2'])
ic = IC50(testing['ic50_test_header_no_drug_prefix'].location)
assert ic.drugIds == [1, 2]
ic = IC50(testing['ic50_test_header_drug_prefix_only'].location)
assert ic.drugIds == [1, 2]
ic = IC50(testing['ic50_test_header_mixed_drug_prefix'].location)
assert ic.drugIds == [1, 2]
def test_read_ic50():
# -------------------------------- functionalities
r = IC50(ic50_test)
# we can also instanciate from a valid dataframe
r = IC50(r)
# test repr
r
# and print statement
print(r)
# the copy method
assert r == r.copy()
r.hist()
r.plot_ic50_count()
r.cosmicIds
f = TempFile()
r.to_csv(f.name)
f.delete()
# columns may be duplicated
r = IC50(ic50_test)
df = pd.concat([r.df, r.df[999]], axis=1)
# create new instance that should raise an error
try:
IC50(df)
assert False
except:
assert True
# ---------------------------------------- different IC50 formats
# test all files available
for key in testing.keys():
filename = testing[key].location
if filename.startswith('ic50_test'):
ic = IC50(filename)
# some specific checks:
#ic = IC50(testing['ic50_test_header_drug_prefix_only'].location)
#assert ic.df.shape == (2,2)
#assert all(ic.df.columns == ['1','2'])
ic = IC50(testing['ic50_test_header_no_drug_prefix'].location)
assert ic.drugIds == [1, 2]
ic = IC50(testing['ic50_test_header_drug_prefix_only'].location)
assert ic.drugIds == [1, 2]
ic = IC50(testing['ic50_test_header_mixed_drug_prefix'].location)
assert ic.drugIds == [1, 2]
def test_d2c1():
s = D2C1()
s.test()
filename = s.download_template()
d = s.score(filename)
assert_almost_equal(d['AUPR'], 0.2563463, 7)
from easydev import TempFile
fh = TempFile()
s._create_templates(filename=fh.name)
fh.delete()
s.score_and_compare_with_lb(s.download_template())
def score_sc1(self, prediction_file):
"""Compute all results and compare user prediction with all official participants
This scoring function can take a long time (about 5-10 minutes).
"""
fh = TempFile()
gs1, _ = self.download_gs()
script = self.classpath + os.sep + "DREAM_Olfaction_scoring_Q1.pl"
cmd = "perl %s %s %s %s"
cmd = cmd % (script, prediction_file, fh.name, gs1)
shellcmd(cmd)
df = pd.read_csv(fh.name, sep='\t', index_col=None).ix[0]
fh.delete()
return df
def score_sc1(self, prediction_file):
"""Compute all results and compare user prediction with all official participants
This scoring function can take a long time (about 5-10 minutes).
"""
fh = TempFile()
gs1, _ = self.download_gs()
script = self.classpath + os.sep + "DREAM_Olfaction_scoring_Q1.pl"
cmd = "perl %s %s %s %s"
cmd = cmd % (script, prediction_file, fh.name, gs1)
shellcmd(cmd)
df = pd.read_csv(fh.name, sep='\t', index_col=None).ix[0]
fh.delete()
return df
def test_d2c1():
s = D2C1()
s.test()
filename = s.download_template()
d = s.score(filename)
assert_almost_equal(d['AUPR'], 0.2563463, 7)
from easydev import TempFile
fh = TempFile()
s._create_templates(filename=fh.name)
fh.delete()
s.score_and_compare_with_lb(s.download_template())
def test_read_write_from_cnograph():
c = CNOGraph(cnodata("PKN-ToyPB.sif"))
fh = TempFile(suffix='.xml')
c.to_sbmlqual(fh.name)
c2 = CNOGraph(fh.name)
assert c == c2
fh.delete()
c = CNOGraph(cnodata("PKN-ToyPB.sif"))
c.expand_and_gates()
fh = TempFile(suffix='.xml')
c.to_sbmlqual(fh.name)
c2 = CNOGraph(fh.name)
fh.delete()
assert c == c2
def test_simple_sbmlqual():
# a simple example with simple OR, simple link, mix of OR and AND and single ANd
c = CNOGraph()
c.add_reaction("!A=C")
c.add_reaction("C=D")
c.add_reaction("B=C")
c.expand_and_gates()
c.add_reaction("a1=b")
c.add_reaction("a2=b")
c.add_reaction("D^b=E")
fh = TempFile(suffix='.xml')
c.to_sbmlqual(fh.name)
c2 = CNOGraph(fh.name)
fh.delete()
assert c == c2
def test_MSReader():
# we can just create an instance
r = MassSpecReader(verbose=verbose)
# fails if wrong file
try:
r = MassSpecReader("dummy.csv", verbose=verbose)
assert False
except:
assert True
filename = yeast.get_yeast_filenames()[0]
r = MassSpecReader(filename, verbose=verbose)
print(r)
r.mode
r.N
r.df
r.measurements
r.metadata
try:
r.mode = None
assert False
except:
assert True
r.sort_psites_ors_only()
r['DIG1']
r['DIG1',"S142"]
r['DIG1_S142']
try:
r['DIG1', 'S142', 'dummy']
assert False
except:
assert True
r.sequences
r.psites
from easydev import TempFile
f = TempFile()
r.to_csv(f.name)
f.delete()
def test_yeast_june():
#y = yeast.YEAST2MIDAS(get_yeast_small_data(), get_yeast_raw_data(), verbose=False)
#y.cleanup_june()
#y.cleanup_june()
#len(y.df)<100
filename = gsf("msdas", "data", "PKN-yeastScaffold.sif")
data.cleanup_june()
c,m,e = data.export_pkn_and_midas_june(filename)
from easydev import TempFile
f = TempFile()
data.to_midas(f.name)
f.delete()
cv = data.get_cv()
m = data.get_midas()
data.pcolor_na()
data.plot_timeseries("DIG1_S126+S127")
def score_A(self, filename):
from easydev import TempFile
fh = TempFile()
script = self._pj([self.classpath,
'weighted_average_concordance_index.pl'])
datadir = self._pj([self.classpath, 'data'])
cmd = "perl %s %s %s %s"
cmd = cmd % (script, filename, datadir , fh.name)
shellcmd(cmd, verbose=True, ignore_errors=True)
try:
df = pd.read_csv(fh.name, sep='\t', header=None)
except:
print("Something wrong in the Scoring while executing \n %s. " % cmd)
print("\n The D7C4 challenge requires a Perl package to be installed")
print("See D7C4 documentation e.g., on dreamtools.readthedocs.org")
import sys
sys.exit(1)
df.columns = ['DrugID', 'probabilistic c-index',
'weighted probabilistic c-index', 'zscores']
df = df.set_index('DrugID')
fh.delete()
ws = (df.sum() / df.sum().ix['zscores'])
ws = ws.ix['weighted probabilistic c-index']
results = df.mean()
results['weight average probabilistic c-index'] = ws
del results['zscores']
# Finally compute pvalues based on precomputed scores
precomp = pd.read_csv(self._pj([self.classpath, 'data',
'DREAM7_DrugSensitivity1_drug_zscores.txt']), sep='\t',
skiprows=6, header=None)
overall_mean = precomp.ix[31][1]
overall_var = precomp.ix[31][2]
pval = 1 - (.5 * (math.erf((ws - overall_mean)/(math.sqrt(2*overall_var))) + 1))
results['weight average probabilistic c-index p-value'] = pval
return {'Results': results}
def test_config_parser():
s1 = ParamsGA()
s2 = ParamsGeneral()
c1 = CNOConfigParser()
c1.add_section(s2)
c1.add_section(s1)
s1 = ParamsGA()
s2 = ParamsGeneral()
c2 = CNOConfigParser()
c2.add_section(s2)
c2.add_section(s1)
assert c1 == c2
from easydev import TempFile
fh = TempFile()
c1.save(fh.name)
c2 = CNOConfigParser(fh.name)
fh.delete()
assert c1 == c2
def test_models():
data = np.array([[1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0],
[1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0],
[1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0],
[1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1],
[1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1],
[1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0],
[1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0],
[1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0],
[1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0],
[1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1],
[1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0],
[1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1],
[1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1],
[1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1],
[1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1]])
columns = [u'EGF=PI3K', u'TNFa=PI3K', u'Jnk=cJun', u'PI3K=Akt', u'Raf=Mek', u'!Akt=Mek', u'Mek=p90RSK', u'Mek=Erk', u'Erk=Hsp27', u'TNFa=Jnk', u'TNFa=NFkB', u'TNFa=Hsp27', u'EGF=Raf', u'EGF^TNFa=PI3K', u'Raf^!Akt=Mek', u'Erk^TNFa=Hsp27']
df = pd.DataFrame(data, columns=columns)
fh = TempFile()
df.to_csv(fh.name)
m1 = Models(df)
m2 = Models(m1)
m3 = Models(fh.name, index_col=0) # there is an index column with no name
fh.delete()
# trying a stupid constructor
try:
Models(1)
assert False
except:
assert True
return m1, m2
assert m1 == m2
assert m1 == m3
# plots
m1.plot()
m1.plot(1)
m1.plot('cv')
m1.errorbar()
m1.heatmap()
# exporters
fh = TempFile()
m1.to_csv(fh.name)
fh.delete()
fh = TempFile()
m1.to_sif(fh.name)
fh.delete()
# m1 and m2 are identical. Adding them gets rid of duplicates so it should be
# equal to itself.
m1 == m1 + m2
def score_A(self, filename):
from easydev import TempFile
fh = TempFile()
script = self._pj([self._path2data,
'weighted_average_concordance_index.pl'])
datadir = self._pj([self._path2data, 'data'])
cmd = "perl %s %s %s %s"
cmd = cmd % (script, filename, datadir , fh.name)
shellcmd(cmd, verbose=True, ignore_errors=True)
df = pd.read_csv(fh.name, sep='\t', header=None)
df.columns = ['DrugID','probabilistic c-index',
'weighted probabilistic c-index', 'zscores']
df = df.set_index('DrugID')
fh.delete()
ws = (df.sum() / df.sum().ix['zscores'])
ws = ws.ix['weighted probabilistic c-index']
results = df.mean()
results['weight average probabilitis c-index'] = ws
del results['zscores']
# Finally compute pvalues based on precomputed scores
precomp = pd.read_csv(self._pj([self._path2data, 'data',
'DREAM7_DrugSensitivity1_drug_zscores.txt']), sep='\t',
skiprows=6, header=None)
overall_mean = precomp.ix[31][1]
overall_var = precomp.ix[31][2]
pval = 1 - (.5 * (math.erf((ws - overall_mean)/(math.sqrt(2*overall_var))) + 1))
results['weight average probabilitis c-index p-value'] = pval
return {'Results': results}
