def __init__(self, filename, verboseR=True):
Challenge.__init__(self, challenge_name='D8C2')
RTools.__init__(self, verboseR=verboseR)
self.filename = filename
self._path2data = os.path.split(os.path.abspath(__file__))[0]
def __init__(self, filename, verboseR=True):
Challenge.__init__(self, challenge_name='D8C2')
RTools.__init__(self, verboseR=verboseR)
self.filename = filename
def score(self, filename):
self.G = self._read_challenge(self.download_goldstandard())
self.T = self._read_challenge(filename)
#from scipy.stats.stats import pearsonr, spearmanr
G = self.G[self.G.columns[2:]].values
T = self.T[self.T.columns[2:]].values
# Using scipy, the pvalue are not the same as in matlab for several reasons.
# first scipy returns only 2-tail pvalue but more importantly, it is
# a rough approximation as mentionned in their doc and when compared
# to matlab differences can be large. So, we use R, which results are
# also differnt but much close (1-2% different
"""data = [spearmanr(G[i,:], T[i,:]) for i in range(0,50)]
rho_row = [x[0] for x in data]
pval_row = [x[1] for x in data]
# row correlation
data = [spearmanr(G[:,i], T[:,i]) for i in range(0,8)]
rho_col = [x[0] for x in data]
pval_col = [x[1] for x in data]
"""
from dreamtools.core.rtools import RTools
rtool = RTools(verboseR=False)
pval_row = []
pval_col = []
rho_row = []
rho_col = []
for i in range(0, 50):
rtool.session.t = T[i, :].copy()
rtool.session.g = G[i, :].copy()
rtool.session.run(
"results = cor.test(t, g, method='spearman', alternative='greater', exact=F)"
)
rho_row.append(rtool.session.results['estimate'])
pval_row.append(rtool.session.results['p.value'])
for i in range(0, 8):
rtool.session.t = T[:, i].copy()
rtool.session.g = G[:, i].copy()
rtool.session.run(
"results = cor.test(t, g, method='spearman', alternative='greater', exact=F)"
)
rho_col.append(rtool.session.results['estimate'])
pval_col.append(rtool.session.results['p.value'])
print("""
WARNING: the spearman correlation pvalue are computed using R. Pvalues are
slightly different from those computed using matlab and therefore the final
values may differ by a few percents to the pvlues reported in the original
challenge. \n""")
self.rho_col = rho_col
self.pval_col = pval_col
self.rho_row = rho_row
self.pval_row = pval_row
score1 = np.exp(np.nansum(np.log(pval_row)) / 50)
score2 = np.exp(np.nansum(np.log(pval_col)) / 8.)
score = sum(-np.log10([score1, score2])) / 2.
return {'score': score}
def __init__(self, filename, verboseR=True):
Challenge.__init__(self, challenge_name='D8C2')
RTools.__init__(self, verboseR=verboseR)
self.filename = filename
def score_challengeB(self, filenames):
# Ideally provide 3 filenames but if only 1 is given, try
# to infer the names of the 2 others
cor_pheno1 = []
cor_pheno2 = []
pval_pheno1 = []
pval_pheno2 = []
scores = []
from dreamtools.core.rtools import RTools
rtool = RTools(verboseR=False)
assert len(filenames) == 3, "Must provide 3 files"
self.golds = []
self.preds = []
gold_filenames = self.download_goldstandard('B')
print("Warning: your 3 submissions should be ordered as B1, B2, B3 files")
for tag in [1, 2, 3]:
#assumeing data and gs are sorted in the same way !!
gold = pd.read_csv(gold_filenames[tag-1], sep='[ \t]',
engine='python')
self.golds.append(gold)
#filename = 'DREAM5_SysGenB%s_your_Predictions.txt' % tag
#filename = self._pj([self.classpath, 'data', filename])
filename = filenames[tag-1]
pred1 = pd.read_csv(filename, sep='[ \t]', engine='python')
self.preds.append(pred1)
# correlation gs versus predicted
rtool.session.t = pred1.ix[0].values
rtool.session.g = gold.ix[0].values
rtool.session.run("results = cor.test(t, g, method='spearman', alternative='greater')")
T1 = rtool.session.results.copy()
rtool.session.t = pred1.ix[1].values
rtool.session.g = gold.ix[1].values
rtool.session.run("results = cor.test(t, g, method='spearman', alternative='greater')")
T2 = rtool.session.results.copy()
cor_pheno1.append(T1['estimate'])
cor_pheno2.append(T2['estimate'])
pval_pheno1.append(T1['p.value'])
pval_pheno2.append(T2['p.value'])
score = -(np.log(T1['p.value']) + np.log(T2['p.value']))
scores.append(score)
self.corp1 = cor_pheno1
self.corp2 = cor_pheno2
self.pval1 = pval_pheno1
self.pval2 = pval_pheno2
self.scores = scores
# This part now compute the pvalues using random prediction
random_scores = {0:[],1:[],2:[]}
from easydev import Progress
pb = Progress(self.N_pvalues, interval=1)
for ii in range(1, self.N_pvalues):
for tag in [0,1,2]:
#generate random coordinates
coord = random.sample(['RIL%s' % i for i in range(1,31)], 30)
coord2 = random.sample(['RIL%s' % i for i in range(1,31)], 30)
# Obtaining random scores
rtool.session.t = self.preds[tag].ix[0].ix[coord].values
rtool.session.g = self.golds[tag].ix[0].values
rtool.session.run("results = cor.test(t, g, method='spearman', alternative='greater')")
T1 = rtool.session.results.copy()
rtool.session.t = self.preds[tag].ix[1].ix[coord2].values
rtool.session.g = self.golds[tag].ix[1].values
rtool.session.run("results = cor.test(t, g, method='spearman', alternative='greater')")
T2 = rtool.session.results.copy()
random_scores[tag].append(-(np.log(T1['p.value']) + np.log(T2['p.value'])))
pb.animate(ii+1)
self.random_scores = random_scores
#Obtaining p-values
pvals = [sum(self.random_scores[k]>= self.scores[k])/float(self.N_pvalues)
for k in [0,1,2]]
self.pvals = pvals
df = pd.DataFrame({'scores':self.scores,
'correlation_phenotype1':cor_pheno1,
'correlation_phenotype2':cor_pheno2,
'pvalues_phenotype1':pval_pheno1,
'pvalues_phenotype2':pval_pheno2,
'pvalues':self.pvals})
df= df.T
df.columns = ['SysGenB1', 'SysGenB2', 'SysGenB3']
return df
