def real():
G = pd.read_pickle(utl.outpath + 'real/real.replicates.uptoF59.maxLikelihoods.regularized.LowCovRemoved.df');
G = G.s * (G.alt - G.null)
R = pd.read_pickle(utl.outpath + 'real/real.replicates.uptoF59.df');
F=pd.read_pickle(utl.outpath+'real/negativeControl.Simulations.maxLikelihoods.regularized.df');F=F.s*(F.alt-F.null)
kde=utl.getDensity(F,width=100)
q = np.sort(np.append(np.linspace(0.0, 1, 100)[:-1], np.linspace(0.999, 1, 1000)))
qq=pd.concat([utl.getQantilePvalues(G,kde,quantiles=q),utl.getQantilePvalues(F,kde,quantiles=q)],axis=1);qq.columns=['data','null'];
pplt.QQPval(qq, fname=utl.paperFiguresPath + 'qq.pdf')
reload(pplt)
def plotSNPPval(out):
scores = rutl.loadScores()
kde = utl.getDensity(scores, width=1);
pval = utl.getPvalKDE(out.sort_values(ascending=False).iloc[:1200], kde)
print pval.sort_values()
pval[pval >= 3].size
df = pd.DataFrame(pval)
df = pd.concat([df[df.index.get_level_values('CHROM') == ch] for ch in
['X', '2L', '2R', '3L', '3R', '4', '2LHet', '2RHet', '3LHet', '3RHet', 'XHet']])
fig = plt.figure(figsize=(7, 2), dpi=300);
pplt.Manhattan(df, fig=fig, markerSize=2, ticksize=8, sortedAlready=True);
[pplt.setSize(ax, 8) for ax in fig.get_axes()]
def computeLocalPval(x,i):
wins=np.array([200])*1000
df=[]
for i in X.index:
res=[]
for pad in wins:
x=X[(X.index>=i-pad) & (X.index<=i+pad)]
kde=utl.getDensity(x[x.index != i])
res+=[utl.getPvalKDE(pd.Series(x.loc[i]),kde)[0]]
df+=[pd.Series(res,index=wins,name=i)]
df=pd.DataFrame(df)
pd.concat([df.apply(lambda x:x.idxmax(),1),df.max(1)],1).plot.scatter(x=0,y=1)
a['pval']=df.max(1).values
o=a[a.pval>a.pval.quantile(0.999)]
pplt.Manhattan(a,Outliers=o)
df.max(1).plot()
y=utl.scan3way(x,winsize=10,f=np.mean)
x.sort_values()
y.sort_values()
def Simulation():
a=pd.read_pickle('{}ROC/{}.df'.format(utl.outpath, 'COMALE'));a=a.s*(a.alt-a.null);
pos=a.loc[(0.1,'COMALE',0.1,1,0)];neg=a.loc[(0.005,'COMALE',0.0,-1,0)]
F=pd.read_pickle(utl.outpath+'real/negativeControl.Simulations.maxLikelihoods.regularized.df').loc[0];F=F.s*(F.alt-F.null)
q=np.linspace(0,1,1200)
kde=utl.getDensity(F,width=50)
qq=pd.concat([utl.getQantilePvalues(pos,kde,quantiles=q),utl.getQantilePvalues(neg,kde,quantiles=q)],axis=1);qq.columns=['data','null'];
pplt.QQPval(qq)
plt.savefig(utl.paperFiguresPath + 'qqsim.pdf')
