def make_pass(stressor): #print "Parsing data for '%s'...\n" % stressor experiments = [ DataSet.load(input % stressor) for input in inputs ] reference = experiments[0] for data in experiments: # Find the ratio between the amount of red and green fluorescence that was # detected. This ratio is assumed to be one for most data analysis # purposes, so the raw data needs to be corrected. green, red = 0, 0 for feature in data: red += feature.signal.red.intensity green += feature.signal.green.intensity data.intensity_ratio = red / green data.log_ratio = math.log(red / green, 2) def correction(feature): feature.log_ratio -= data.log_ratio return feature data.apply(correction) for data in experiments: for feature, zero in zip(data, reference): feature.normed_ratio = feature.log_ratio - zero.log_ratio for data in experiments: def irrational(feature): return math.isnan(feature.normed_ratio) def noisy(feature): return (feature.signal.red.signal_to_noise < 1) or \ (feature.signal.green.signal_to_noise < 1) def unnamed(feature): return feature.name in ('None', 'EMPTY') # This filter was proposed by team JKRW. def inconsistent(feature): return feature.regression_quality < 0.5 data.prune(irrational) data.prune(noisy) data.prune(unnamed) data.prune(inconsistent) for data, output in zip(experiments, outputs): print "Saving %d features for '%s'." % (len(data), stressor) data.save(output % stressor) print
#!/usr/bin/env python from __future__ import division from microarray import DataSet inputs = [ 'data/A+D.000.gpr', 'data/A+D.030.gpr', 'data/A+D.060.gpr', 'data/A+D.180.gpr' ] for input in inputs: data = DataSet.load(input) print len(data)