def setUp(self,):
d = dict()
d['ids'] = ['a','b','c','d','e']
d['frac1'] = [1.0,2.0,0.0,0.0,0.0]
d['frac2'] = [0.0,0.0,3.0,4.0,0.0]
d['frac3'] = [2.0,1.0,0.0,0.0,7.0]
d['frac4'] = [0.0,0.0,6.0,8.0,10.0]
df = pd.DataFrame(d, dtype=float)
df = df.set_index('ids')
self.elution = eff.Elut()
self.elution.df = df
d2 = dict()
d2['ids'] = ['a','b','c','d']
d2['frac1'] = [1.0,1.0,0.0,2.0]
d2['frac2'] = [0.0,0.0,1.0,4.0]
d2['frac3'] = [2.0,1.0,0.0,0.0]
d2['frac4'] = [0.0,0.0,6.0,10.0]
df2 = pd.DataFrame(d2, dtype=float)
df2 = df2.set_index('ids')
self.elution2 = eff.Elut()
self.elution2.df = df2
d3 = dict()
d3['ids'] = ['a','b','c','d']
d3['frac1'] = [1.0,1.0,0.0,1.0]
d3['frac2'] = [0.0,0.0,0.0,0.0]
d3['frac3'] = [0.0,0.0,1.0,0.0]
d3['frac4'] = [0.0,0.0,0.0,0.0]
df3 = pd.DataFrame(d3, dtype=float)
df3 = df3.set_index('ids')
self.elution3 = eff.Elut()
self.elution3.df = df3
d4 = dict()
d4['ids'] = ['e','f','g','h']
d4['frac1'] = [10.0,10.0,0.0,20.0]
d4['frac2'] = [0.0,0.0,10.0,40.0]
d4['frac3'] = [20.0,10.0,0.0,0.0]
d4['frac4'] = [0.0,0.0,60.0,10.0]
df4 = pd.DataFrame(d4, dtype=float)
df4 = df4.set_index('ids')
self.elution4 = eff.Elut()
self.elution4.df = df4
def main():
parser = argparse.ArgumentParser(
description=
"Calculate difference features between two fractionation experiments")
parser.add_argument("--elution_files",
action="store",
nargs='+',
dest="elution_files",
required=True,
help="Elution files (.elut)")
parser.add_argument(
"--features",
action="store",
nargs='+',
dest="features",
required=False,
default=['diffrac'],
help=
"Features to calculate: diffrac (L1-norm of difference) diffrac_percent diffrac_normalized pearsonr poisson mean_abundance emd zscore sliding_zscore fdr_correct sliding_fdr_correct"
)
parser.add_argument(
"--annotated_list",
action="store",
dest="annotated_list",
required=False,
default=None,
help=
"Filename of annotated ids, used for calculating zscores from compliment of list, default=None"
)
parser.add_argument("--contaminate_tag",
action="store",
dest="contaminate_tag",
required=False,
default='CONTAMINANT',
help="Filters entries with tag, default=CONTAMINANT")
parser.add_argument(
"--use_gmm",
action="store_true",
dest="use_gmm",
required=False,
default=False,
help=
"Fit sliding window distributions to Gaussian Mixture Model and use largest gaussian for calculating zscore, default=False"
)
parser.add_argument(
"--log_transform",
action="store_true",
dest="log_transform",
required=False,
default=False,
help=
"Use the log transform of the diffrac score to calculate sliding zscore, default=False"
)
parser.add_argument(
"--window_size",
action="store",
type=int,
dest="window_size",
required=False,
default=100,
help="Window size to use for calculating sliding zscore, default=100")
parser.add_argument(
"--output_file",
action="store",
dest="out_filename",
required=False,
default=None,
help="Filename of output file, default=None which prints to stdout")
args = parser.parse_args()
elutions = []
for efile in args.elution_files:
elut = eff.Elut()
elut.load(efile, format='tsv')
elut.threshold(thresh=1)
elutions.append(elut)
feature_df = pd.DataFrame()
if len(elutions) >= 2:
if 'diffrac' in args.features:
feature_series = calc_diffrac(elutions[0],
elutions[1],
normalize_totalCounts=False)
feature_series.name = 'diffrac'
feature_df = join_feature(feature_df, feature_series)
if 'diffrac_percent' in args.features:
feature_series = calc_diffrac(elutions[0],
elutions[1],
percent_totalCounts=True)
feature_series.name = 'diffrac_percent'
feature_df = join_feature(feature_df, feature_series)
if 'diffrac_normalized' in args.features:
feature_series = calc_diffrac(elutions[0],
elutions[1],
normalize_totalCounts=True)
feature_series.name = 'diffrac_normalized'
feature_df = join_feature(feature_df, feature_series)
if 'emd' in args.features:
feature_series = calc_emd(elutions[0], elutions[1])
feature_series.name = 'emd'
feature_df = join_feature(feature_df, feature_series)
if 'pearsonr' in args.features:
feature_series = calc_correlation(
elutions[0],
elutions[1],
correlation_func=lambda x, y: stats.pearsonr(x, y)[0])
feature_series.name = 'pearsonr'
feature_df = join_feature(feature_df, feature_series)
if 'poisson' in args.features:
print("WARNING: poisson not implemented")
#feature_series = calc_correlation(elutions[0], elutions[1])
#feature_series.name = 'poisson'
#feature_df = join_feature(feature_df,feature_series)
if 'mean_abundance' in args.features:
feature_series = calc_mean_abundance(elutions[0], elutions[1])
feature_series.name = 'mean_abundance'
feature_df = join_feature(feature_df, feature_series)
if args.annotated_list != None:
#kdrew: add in training labels
annotated_df = pd.read_table(args.annotated_list,
header=None,
names=['annotated'])
annotated = [
i in annotated_df['annotated'].values for i in feature_df.index
]
feature_df['annotated'] = annotated
print len(feature_df)
try:
feature_df = feature_df[~feature_df.index.str.
contains('CONTAMINANT')]
except AttributeError:
print "No contaminants"
print len(feature_df)
if 'zscore' in args.features:
if 'diffrac_normalized' not in args.features:
#kdrew: calculating diffrac_normalized
feature_series = calc_diffrac(elutions[0],
elutions[1],
normalize_totalCounts=False)
feature_series.name = 'diffrac'
feature_df = join_feature(feature_df, feature_series)
feature_series = calc_zscore(feature_df)
feature_series.name = 'zscore'
feature_df = join_feature(feature_df, feature_series)
if 'sliding_zscore' in args.features:
feature_series = calc_sliding_zscore(
feature_df,
window=args.window_size,
use_gmm=args.use_gmm,
log_transform=args.log_transform)
feature_series.name = 'sliding_zscore'
feature_df = join_feature(feature_df, feature_series)
if 'fdr_correct' in args.features:
fdr_df = calc_fdr_correct(feature_df)
feature_df = join_feature(feature_df, fdr_df)
if 'sliding_fdr_correct' in args.features:
sliding_fdr_df = calc_sliding_fdr_correct(feature_df)
feature_df = join_feature(feature_df, sliding_fdr_df)
if args.out_filename != None:
feature_df.sort_values(args.features[0],
ascending=False).to_csv(args.out_filename)
else:
print feature_df.sort_values(args.features[0], ascending=False)
