num_cov = len(pred_df.columns)
covariates = np.zeros((num_cov + 2,))
covariates[:num_cov] = covariates_df.values
# loop over proteins to get values for current time step
for p in node_list:
pred_df.ix[time, p] = regGBR[stim][p].predict(covariates)
# zero out covariate we are inhibiting, again
pred_df.ix[time, test_inhib_targets[test_inhib]] = 0
pred_dict[test_inhib][stim] = pred_df
out_path = 'results/sakev-{0}-{1}-Prediction'.format(cell_line, test_inhib)
utilities.write_MIDAS(pred_dict[test_inhib], test_inhib_targets[test_inhib], out_path, datatype='Experimental', cell_line=cell_line)
# BT549
###########################################
cell_line = 'BT549'
test_inhib_targets = {'TestInhib1' : ['EGFR_pY1068', 'EGFR_pY1173', 'HER2_pY1248'],
'TestInhib2' : ['Src_pY416','Src_pY527'],
'TestInhib3' : ['mTOR_pS2448'],
'TestInhib4' : ['EGFR_pY1068', 'EGFR_pY1173'],
'TestInhib5' : []}
print '----------- ' + cell_line + ' ------------'
data = pd.read_csv('data/{0}_main.csv'.format(cell_line), header=0)
inhibs = set(data['Inhibitor'])
stims = set(data['Stimulus'])
# loop over times
for tidx in range(1,len(times)):
time = times[tidx]
# get covariates for this time step and scale
covariates_df = ((pred_df.ix[times[tidx-1], :]) - scalarblah.mean_[:-3]) / scalarblah.std_[:-3]
# zero out covariate we are inhibiting
try:
covariates_df.ix[test_inhib_targets[test_inhib]] = 0
except:
pass
num_cov = len(pred_df.columns)
covariates = np.zeros((num_cov + 3,))
covariates[:num_cov] = covariates_df.values
# loop over proteins to get values for current time step
for p in node_list:
pred_df.ix[time, p] = regGBR[stim][p].predict(covariates)
# zero out covariate we are inhibiting, again
pred_df.ix[time, test_inhib] = 0
pred_dict[test_inhib][stim] = pred_df
### SAVE TO MIDAS
for node in node_list:
out_path = 'results/sakev-{0}-Prediction-Insilico'.format(node)
utilities.write_MIDAS(pred_dict[node], node, out_path, datatype='inSilico', cell_line='inSilico')
