def outlier():
scores = rutl.removeHeteroChromatin(rutl.loadScores())
field = comale;
df = sort(utl.scanGenome(scores.abs(), {field: lambda x: x.abs().mean(), 'Num. of SNPs': lambda x: x.size}))[
[field, 'Num. of SNPs']]
a = df.iloc[:, 0]
a = a.rename('Global Outliers');
o = a[a > a.quantile(0.99)]
o.to_pickle(utl.outpath + 'real/outliers.global.df')
fig = plt.figure(figsize=(7, 1.5), dpi=300);
pplt.Manhattan(data=a, Outliers=pd.DataFrame(o), fig=fig, markerSize=2, ticksize=8, sortedAlready=True);
[pplt.setSize(ax, 5) for ax in fig.get_axes()];
plt.gcf().subplots_adjust(bottom=0.15);
plt.savefig(utl.paperPath + 'new/{}.pdf'.format('global'))
a = a.rename('Chrom Outliers');
o = a.groupby(level=0).apply(lambda x: x[x > x.quantile(0.99)].loc[x.name])
o.to_pickle(utl.outpath + 'real/outliers.chrom.df')
fig = plt.figure(figsize=(7, 1.5), dpi=300);
pplt.Manhattan(data=a, Outliers=pd.DataFrame(o), fig=fig, markerSize=2, ticksize=8, sortedAlready=True);
[pplt.setSize(ax, 5) for ax in fig.get_axes()]
plt.gcf().subplots_adjust(bottom=0.15);
plt.savefig(utl.paperPath + 'new/{}.pdf'.format('chrom'))
a = a.rename('Local Outliers');
o = localOutliers(a)
o.to_pickle(utl.outpath + 'real/outliers.local.df')
fig = plt.figure(figsize=(7, 1.5), dpi=300);
pplt.Manhattan(data=a, Outliers=pd.DataFrame(o), fig=fig, markerSize=2, ticksize=8, sortedAlready=True);
[pplt.setSize(ax, 5) for ax in fig.get_axes()]
plt.gcf().subplots_adjust(bottom=0.15);
plt.savefig(utl.paperPath + 'new/{}.pdf'.format('local'))
def plotDepthHeterogenocity():
dpi = 300
sns.set_style("whitegrid", {"grid.color": "0.9", 'axes.linewidth': .5, "grid.linewidth": ".09"})
_, ax = plt.subplots(2, 2, sharex=True, figsize=(6, 4), dpi=dpi)
d = pd.read_pickle(utl.outpath + 'real/CD.F59.df').xs('D', level='READ', axis=1)
std = d.std(1)
loc = [std.idxmax(), (std == std.quantile(0.52)).replace({False: None}).dropna().index[0],
(std == std.median()).replace({False: None}).dropna().index[-1],
(std == std.quantile(0.8)).replace({False: None}).dropna().index[0]]
ax = ax.reshape(-1)
fontsize = 6
for i, pos in enumerate(loc):
eg = d.loc[pos]
[eg[r].dropna().plot(marker='o', ax=ax[i], markersize=5) for r in range(3)];
plt.xticks(d.columns.get_level_values('GEN').unique());
plt.xlabel('');
plt.ylabel('')
print 'position={}:{}'.format(eg.name[0], eg.name[1]), get_axis_limits()
if i in [0, 2]: ax[i].set_ylabel('Read Depth')
if i > 1: ax[i].set_xlabel('Generation')
if i == 0: ax[i].legend(['Replicate 1', 'Replicate 2', 'Replicate 3'], loc='upper center',
prop={'size': fontsize})
yrang = pplt.get_axis_limits(upper=True, ax=ax[i])[1] - pplt.get_axis_limits(upper=False, ax=ax[i])[1]
ax[i].set_ylim([min(0, ax[i].get_ylim()[0] - 0.05 * yrang), ax[i].get_ylim()[1] + 0.03 * yrang])
ax[i].set_xlim([-2, 61]);
ax[i].set_title('{}:{}'.format(eg.name[0], eg.name[1]))
pplt.setSize(ax[i], fontsize)
mpl.rc('font', **{'family': 'serif', 'serif': ['Computer Modern']});
mpl.rc('text', usetex=True)
plt.gcf().subplots_adjust(bottom=0.15)
pplt.savefig('depthHetero', dpi)
plt.show()
def plotScalingFactor():
r=2*1e-8
l = 5e4
dpi = 300
j = 0
for nu0 in [0.005, 0.1]:
for s in [0.025, 0.1]:
t = np.arange(0, 2 * (utl.logit(0.995) - utl.logit(nu0)) / s + 1., 1)
fig, ax = plt.subplots(2, 1, figsize=(5.5, 2.5), dpi=dpi, sharex=True);
nu(t, s=s, nu0=nu0).plot(color='k', legend=False, ax=ax[0])
pplt.annotate(r'$s$={}, $\nu_0=${} ({} Sweep)'.format(s, nu0, ('Soft', 'Hard')[nu0 == 0.005]), fontsize=7,
ax=ax[0])
pplt.setSize(ax=ax[0], fontsize=6)
ax[0].set_ylabel(r'$\nu_t$')
#
H0 = H(t[0], s=s, nu0=nu0)
Ht = H(t, s=s, nu0=nu0)
df = pd.DataFrame([np.log(Ht / H0), -2 * r * t * l], columns=t, index=['log(Growth)', r'log(Decay)']).T
df['log(Growth) + log(Decay)'] = df.sum(1)
df.plot(ax=ax[1], grid=True, linewidth=2);
ax[1].set_xlabel('Generations');
ax[1].set_ylabel('Log(Scaling Factor)')
ax[1].axvline(df.iloc[1:, 2].abs().idxmin(), color='k', linestyle='--', linewidth=0.5)
# if j != 3:
# ax[1].legend_.remove()
# else:
ax[1].legend(['log(Growth)', r'log(Decay)', 'log(Growth) + log(Decay)'], bbox_to_anchor=(1.45, .75),
prop={'size': 6})
pplt.setSize(ax[1], fontsize=6)
plt.tight_layout(pad=0.1, rect=[0, 0, 0.7, 1])
plt.gcf().subplots_adjust(bottom=0.15)
pplt.savefig('decayFactors{}'.format(j), dpi=dpi)
j += 1
def plotOne(df, ax, method):
m=df.mean(1)
s=df.std(1)
# plt.locator_params(nbins=4);
m.plot(ax=ax, legend=False, linewidth=3, color=color)
x=m.index.values
m=m.values;s=s.values
ax.fill_between(x, m - 2 * s, m + 2 * s, color=color, alpha=0.3)
ax.set_ylabel(method.strip())
ax.set_ylim([-0.1, ax.get_ylim()[1]])
pplt.setSize(ax)
def plotDepth():
sns.set_style("whitegrid", {"grid.color": "1", 'axes.linewidth': .5, "grid.linewidth": ".09"})
sns.set_context("notebook", font_scale=1.4, rc={"lines.linewidth": 2.5})
d = pd.read_pickle(utl.outpath + 'real/CD.F59.df').xs('D', level='READ', axis=1)
(d.min(1) > 50).sum()
(d > 50).sum().sum()
z = pd.Series(np.ndarray.flatten(d.values))
fontsize = 6
mpl.rcParams.update({'font.size': fontsize})
plt.figure(figsize=(6, 4), dpi=300);
plt.subplot(2, 2, 1);
z.value_counts().sort_index().plot()
plt.xlim([0, 200]);
plt.xlabel('Depth');
plt.ylabel('Number of Measurments' + '\n (out of {:.1f}M)'.format(z.shape[0] / 1e6));
plt.ticklabel_format(axis='y', style='sci', scilimits=(0, 0))
plt.title('Scaled PDF')
pplt.annotate('(A)', xpad=0.85, ypad=0.45, fontsize=fontsize)
plt.axvline(50, linestyle='--', linewidth=1, color='k')
pplt.setSize(plt.gca(), fontsize)
plt.subplot(2, 2, 2);
z.value_counts().sort_index().cumsum().plot()
plt.xlim([0, 200])
plt.ylim([-3e5, 2.05 * 1e7])
plt.xlabel('Depth');
plt.title('Scaled CDF')
pplt.annotate('(B)', xpad=0.85, ypad=0.45, fontsize=fontsize)
plt.axvline(50, linestyle='--', linewidth=1, color='k')
pplt.setSize(plt.gca(), fontsize)
plt.subplot(2, 2, 3);
d.min(1).value_counts().sort_index().plot()
plt.xlim([0, 100]);
plt.xlabel('Minimum Depth of each Variant');
plt.ylabel('Number of Variants' + '\n (out of {:.1f}M)'.format(d.shape[0] / 1e6));
plt.ticklabel_format(axis='y', style='sci', scilimits=(0, 0))
plt.rc('font', size=fontsize)
pplt.annotate('(C)', xpad=0.85, ypad=0.45, fontsize=fontsize)
plt.axvline(50, linestyle='--', linewidth=1, color='k')
pplt.setSize(plt.gca(), fontsize)
plt.subplot(2, 2, 4);
d.min(1).value_counts().sort_index().cumsum().plot()
plt.xlim([0, 60])
plt.ylim([0.25 * -1e5, plt.ylim()[1]])
plt.xlabel('Minimum Depth of each Variant');
plt.ticklabel_format(axis='y', style='sci', scilimits=(0, 0))
pplt.annotate('(D)', xpad=0.85, ypad=0.45, fontsize=fontsize)
plt.axvline(50, linestyle='--', linewidth=1, color='k')
pplt.setSize(plt.gca(), fontsize)
plt.gcf().subplots_adjust(bottom=0.15)
plt.gcf().tight_layout(h_pad=0.1)
fontsize = 6
mpl.rc('font', **{'family': 'serif', 'serif': ['Computer Modern'], 'size': fontsize});
mpl.rc('text', usetex=True)
mpl.rcParams.update({'font.size': 1})
pplt.savefig('depth', 300)
plt.show()
def plot():
fontsize = 5
def plotOne(x, ax):
lw = 3
alpha = 0.8
try:
if x.name is None:
alpha = 1
lw = 0.7
except:
pass
if x is not None:
x.plot(ax=ax, color=color[x.name], lw=lw, alpha=alpha)
fig, axes = plt.subplots(4, 3, figsize=(7, 3.9), dpi=300)
df = pd.read_pickle(utl.outpath + 'markov/simulations/plotData.df')
ABC = [list('ABC'), list('DEF'), list('GHI'), list('KLM')]
for (s, nu0), axr, titles in zip(itertools.product([0, 0.1], [0.005, 0.1]), axes, ABC):
for tau, ax, title in zip([1, 10, 100], axr, titles):
observation = getObservation(nu0, s, tau)
x = observation.index.values
brownian = getBrownian(x=x, nu0=nu0, tau=tau, mu=nu0)
markov = df[(nu0, s, tau)].loc['markov']
df[(nu0, s, tau)] = pd.Series([observation, markov, brownian],
index=['observation', 'markov', 'brownian']).rename((nu0, s, tau))
if s: df[(nu0, s, tau)].loc['brownian'] = None
df[(nu0, s, tau)].loc[['markov', 'brownian', 'observation']].apply(lambda x: plotOne(x, ax))
if nu0 == 0.005 and tau == 100: ax.set_xlim([0, 0.02])
ax.locator_params(nbins=1, axis='y')
if nu0 == 0.005 and tau == 100 and s == 0:
ax.legend(['Markov Chain', 'Brownian Motion', 'Empirical Distribution'], fontsize=fontsize)
ax.set_xticks(ax.get_xticks()[::2]);
# ax.set_xticklabels(map(str,tick))
pplt.annotate('(' + title + ')', fontsize=fontsize, ax=ax)
pplt.setSize(ax, fontsize)
axr[0].set_ylabel(r'$P(\nu_\tau|\nu_0={},s={}$)'.format(nu0, s), fontsize=fontsize + 2, rotation=0, labelpad=30)
# ax.text(0.0,0.0,)
for tau, ax in zip([1, 10, 100], axes[0]):
ax.set_title(r'$\tau={}$'.format(tau), fontsize=fontsize)
for ax in axr:
ax.set_xlabel(r'$\nu$', fontsize=fontsize)
plt.gcf().tight_layout(pad=0.1, rect=[0.05, 0, 1, 1])
pplt.savefig('markovDists', 300)
plt.show()
def plotOne(df, outlier, fname=None, dashedline=True):
fig = plt.figure(figsize=(7, 2.5), dpi=300)
pplt.Manhattan(data=df, Outliers=outlier, fig=fig, markerSize=2, ticksize=8, sortedAlready=True)
[pplt.setSize(ax, 8) for ax in fig.get_axes()]
plt.gcf().subplots_adjust(bottom=0.2)
if dashedline: plt.gcf().axes[0].axhline(df.iloc[:, 0].quantile(0.99), linewidth=0.5, linestyle='--', color='k')
if fname is not None: plt.savefig(utl.paperPath + 'new/{}.pdf'.format(fname))
def scanSFS():
scores = rutl.loadScores()
field = comale;
df = sort(utl.scanGenome(scores.abs(), {field: lambda x: x.abs().mean(), 'Num. of SNPs': lambda x: x.size}))[
[field, 'Num. of SNPs']]
plotOne(df, df[df[field] > df[field].quantile(0.99)], fname='all')
nu0 = rutl.getNut(0)
nut = rutl.getNut(59)
reload(rutl)
# n= int(pd.read_pickle(utl.outpath + 'real/CD.F59.df').loc[:,pd.IndexSlice[:,0,'D']].mean().mean())
n = 100
SFSelect = lambda x: est.Estimate.getEstimate(x=x, method='SFSelect', n=n)
sf0 = scanOne(nu0, SFSelect, 'SFSelect.Base', 'SFSelect.Base');
SFSelect = lambda x: est.Estimate.getEstimate(x=x, method='SFSelect', n=n)
sft = scanOne(nut, SFSelect, 'SFSelect.Final', 'SFSelect.Final')
sfr = pd.concat(
[(sft.iloc[:, 0] - sf0.iloc[:, 0]).rename('SFS(59)-SFS(0)'), sf0.iloc[:, 0], sft.iloc[:, 0], df.iloc[:, 0]],
axis=1)
outlier = sfr[sfr.iloc[:, 0] > sfr.iloc[:, 0].quantile(0.99)]
sfr.loc[(sfr.iloc[:, 0] < 0).values, sfr.columns[0]] = None
fig = plt.figure(figsize=(7, 4.5), dpi=300);
pplt.Manhattan(data=sfr, Outliers=outlier, fig=fig, markerSize=2, ticksize=8, sortedAlready=True)
[pplt.setSize(ax, 5) for ax in fig.get_axes()]
plt.savefig(utl.paperPath + 'new/{}.pdf'.format('sfs-clear'))
def Final():
scores = rutl.loadScores(skipHetChroms=True).abs()
a = sort(utl.scanGenome(scores.abs(), {'H': lambda x: x.abs().mean(), 'M': lambda x: x.size}))
intervals = ga.getIntervals(o.H, padding=30000)
fig = plt.figure(figsize=(7, 1.5), dpi=300);
pplt.Manhattan(data=a, Outliers=o, shade=intervals.reset_index(), fig=fig, markerSize=2, ticksize=8,
sortedAlready=True);
[pplt.setSize(ax, 5) for ax in fig.get_axes()];
plt.gcf().subplots_adjust(bottom=0.15);
plt.suptitle((shades.shape[0], shades['len'].sum() / 1e6), fontsize=8)
plt.savefig(utl.paperPath + 'new/{}.pdf'.format('CHROM.FDR_0.01'))
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 D(nu, n, W0, Pi0): return -np.log(1 - nu) * W0 / n - Pi0 * nu ** 2
fontsize = 4
nu = utl.forward(t=1000, s=0.05, x0=0.05)
plt.figure(figsize=(6, 3), dpi=300)
plt.subplot(3, 1, 1);
sns.tsplot(sel, time='gen', value='nu', unit='exp', color='red', ci=99);
sns.tsplot(neut, time='gen', value='nu', unit='exp', ci=99);
nu.plot(color='k', linewidth=1, linestyle='--')
pplt.setSize(plt.gca(), fontsize)
plt.ylabel(r'$\nu_t$', fontsize=fontsize + 2);
plt.ylim([0, 1.05])
plt.title(r'(A)', fontsize=fontsize + 2);
plt.tick_params(
axis='x', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom='off', # ticks along the bottom edge are off
top='off', # ticks along the top edge are off
labelbottom='off');
plt.xlabel('')
plt.subplot(3, 1, 2);
sns.tsplot(sel, time='gen', value='D', unit='exp', color='red', ci=99);
sns.tsplot(neut, time='gen', value='D', unit='exp', ci=99);
(D(nu, 200., 1, 1) + 0.675).plot(c='k', linewidth=1, linestyle='--');
def plotAlternative(subp, s=0.1, fontsize=5):
nu0=0.005
obs = pd.read_pickle(utl.outpath + 'markov/T100.S{:03.0f}.obs.df'.format(s * 1000))
T = Markov.computeTransition(s, 1000)
dfplt= pd.concat([pd.Series({'scale':10, 'xlim':[0.0,0.01],'ylim': [0,0.2]},name=(0.005,1)),pd.Series({'scale':30, 'xlim':[0.06,0.14],'ylim': [0,0.15]},name=(0.1,1)),
pd.Series({'scale':30, 'xlim':[0.0,0.015],'ylim': [0,0.15]},name=(0.005,10)),pd.Series({'scale':45, 'xlim':[0.0,0.2],'ylim': [0,0.025]},name=(0.1,10)),
pd.Series({'scale':30, 'xlim':[0.0,1],'ylim': [0,0.01]},name=(0.005,100)),pd.Series({'scale':50, 'xlim':[0.00,0.4],'ylim': [0,0.004]},name=(0.1,100))
],axis=1).T
markov=T.loc[nu0].copy(True);markov.name='Markov Chain'
plt.subplot(3, 3, subp[0])
tau=1
o=(obs[1].value_counts().sort_index()/obs.shape[0])
o.loc[0.0055]=0.1211
o.index=o.index-0.0005/2
markov.plot(color='b');
o.plot(color='g');
plt.xlim(dfplt.loc[(nu0, tau)].xlim);
plt.ylim(dfplt.loc[(nu0, tau)].ylim);
plt.locator_params(nbins=3)
pplt.annotate(r'$s={}$, $\nu_0=${}, $\tau$={}'.format(s, nu0, tau), loc=1, fontsize=fontsize)
plt.ylabel(r'$P(\nu_\tau|\nu_0,s)$')
plt.xlabel('$s$')
tau=10
for _ in range(9):
markov=markov.dot(T)
pplt.setSize(plt.gca(), fontsize=fontsize)
plt.title('({})'.format(subptitle[subp[0] - 1]), fontsize=fontsize)
plt.subplot(3, 3, subp[1])
markov.plot(color='b');
(obs[10].value_counts().sort_index() / obs.shape[0]).plot(color='g');
plt.xlim(dfplt.loc[(nu0, tau)].xlim);
plt.ylim(dfplt.loc[(nu0, tau)].ylim);
plt.locator_params(nbins=3)
pplt.annotate(r'$s={}$, $\nu_0=${}, $\tau$={}'.format(s, nu0, tau), loc=1, fontsize=fontsize)
plt.xlabel('$s$')
tau=100
for _ in range(90):
markov=markov.dot(T)
pplt.setSize(plt.gca(), fontsize=fontsize)
plt.title('({})'.format(subptitle[subp[1] - 1]), fontsize=fontsize)
plt.subplot(3, 3, subp[2])
counts,limits=np.histogram(obs[100].values,bins=50,range=[0,1]);centers = 0.5*(limits[1:]+limits[:-1]);o=pd.Series(counts,index=centers);o=o/obs.shape[0]
o/=35
o.loc[0.0] = o.iloc[0]
o = o.sort_index()
o.iloc[1] = o.iloc[2]
# o=(obs[100].value_counts().sort_index()/obs.shape[0])
o.name = 'Observation';
o.plot(color='g');
markov.plot(color='b');
plt.xlim(dfplt.loc[(nu0, tau)].xlim);
plt.ylim(dfplt.loc[(nu0, tau)].ylim);
plt.locator_params(nbins=3)
pplt.annotate(r'$s={}$, $\nu_0=${}, $\tau$={}'.format(s, nu0, tau), loc=1, fontsize=fontsize)
plt.xlabel('$s$')
pplt.setSize(plt.gca(), fontsize=fontsize)
plt.title('({})'.format(subptitle[subp[2] - 1]), fontsize=fontsize)
def plotBias():
def computeBias(): # s-shat
print 'computing bias...'
a = pd.read_pickle('{}ROC/{}'.format(utl.outpath, 'HMM')).s
gp = pd.read_pickle(utl.outpath + 'ROC/GP.causal').s
b = pd.concat([a, gp]).sort_index().xs(True, level='causal')
bias = b.groupby(level=range(4)).apply(lambda x: x.name[-1] - x)
bias.to_pickle('{}ROC/bias.df'.format(utl.outpath))
def biash():
a = pd.read_pickle('{}ROC/bias.df'.format(utl.outpath))
a = a[a.index.get_level_values('method') == 'HMM']
a = a + np.random.randn(a.size)
a /= 10
a[a.index.get_level_values('nu0') == 0.1] /= 2
for name, g in a.groupby(level=range(4)):
g -= g.mean();
a.to_pickle('{}ROC/bias.h.df'.format(utl.outpath))
fontsize = 6
# computeBias()
dpi = 300
for depth in [30, 100, 300]:
reload(pplt)
fig = plt.figure(figsize=(5, 5), dpi=dpi)
j = 0
df = pd.read_pickle(utl.outpath + 'ROC/bias.df').loc[depth].reset_index()
df.method = df.method.replace({'HMM': comaleName})
df = df.set_index(pd.read_pickle(utl.outpath + 'ROC/bias.df').loc[depth].index.names).sort_index().s
dfh = pd.read_pickle(utl.outpath + 'ROC/bias.h.df').loc[depth].reset_index()
dfh.method = dfh.method.replace({'HMM': comaleName})
dfh = dfh.set_index(pd.read_pickle(utl.outpath + 'ROC/bias.h.df').loc[depth].index.names).sort_index().s
df[(0.1, comaleName)] += np.random.rand(df[(0.1, comaleName)].shape[0]) / 100 - 0.005
# df[(0.005, 'HMM')] += np.random.rand(df[(0.005, 'HMM')].shape[0]) / 100 - 0.005
ax = []
for nu0 in [0.005, 0.1]:
if j == 0:
ax += [plt.subplot(2, 2, j + 1)]
else:
ax += [plt.subplot(2, 2, j + 1, sharex=ax[-1], sharey=ax[-1])]
a = pd.DataFrame(df.loc[nu0])
a.columns = ['bias']
a['method'] = a.index.get_level_values('method')
a['s'] = a.index.get_level_values('S')
sns.violinplot(x="s", y="bias", hue="method", data=a, linewidth=1,
palette={comaleName: "r", "GP": "darkblue"}, split=True, ax=ax[j]);
if j < 2: ax[j].set_title(r'$\nu_0=${} ({} Sweep)'.format(nu0, ('Soft', 'Hard')[nu0 == 0.005]),
fontsize=fontsize + 2)
# pplt.annotate(r'$\nu_0=${} ({} Sweep)'.format(nu0, ('Soft', 'Hard')[nu0 == 0.005]),xpad=0.05,ypad=1,fontsize=fontsize)
plt.locator_params(axis='y', nbins=5)
pplt.setSize(plt.gca(), fontsize)
ax[j].set_xlabel('$s$', fontsize=fontsize + 2)
# plt.xlabel('$s$', fontsize=fontsize + 2)
ax[j].legend(title='', loc='upper right', fontsize=fontsize + 2)
ax[j].set_ylabel(('Bias ($s-\hat{s}$)', '')[j % 2], fontsize=fontsize + 2)
if j != 1: ax[j].legend_.remove()
pplt.annotate(ABCD[j], fontsize=fontsize + 2, ax=ax[j])
j += 1
for nu0 in [0.005, 0.1]:
if j == 2:
ax += [plt.subplot(2, 2, j + 1)]
else:
ax += [plt.subplot(2, 2, j + 1, sharex=ax[-1], sharey=ax[-1])]
a = pd.DataFrame(dfh.loc[nu0])
a.columns = ['bias']
a['method'] = a.index.get_level_values('method')
a['s'] = a.index.get_level_values('S')
sns.violinplot(x="s", y="bias", hue="method", data=a[a['method'] == comaleName], linewidth=1,
palette={comaleName: "r", "GP": "g"}, ax=ax[j]);
plt.locator_params(axis='y', nbins=5)
pplt.setSize(plt.gca(), fontsize)
ax[j].set_xlabel('$h$', fontsize=fontsize + 2)
ax[j].set_ylabel(('Bias ($h-\hat{h}$)', '')[j % 2], fontsize=fontsize + 2)
pplt.annotate(ABCD[j], fontsize=fontsize + 2, ax=ax[j])
ax[j].legend_.remove()
j += 1
df = df.groupby(level=['method', 'nu0']).describe().round(3).unstack(['method', 'nu0']).loc[
['mean', 'std']].T.reset_index().sort_values('nu0')
df.columns = ['Method', r'$\nu_0$', 'Mean', 'STD']
utl.DataframetolaTexTable(df, fname=utl.paperPath + 'tables/bias.{}.tex'.format(depth))
pplt.savefig('bias.{}'.format(depth), dpi)
def scanSFSSNPbased():
scores = rutl.loadScores(skipHetChroms=True)
# field = comale;
# df = sort(utl.scanGenome(scores.abs(), {field: lambda x: x.abs().mean(), 'Num. of SNPs': lambda x: x.size}))[
# [field, 'Num. of SNPs']]
# plotOne(df, df[df[field] > df[field].quantile(0.99)], fname='all')
reload(rutl)
reload(pplt)
reload(utl)
# SFSelect = lambda x: est.Estimate.getEstimate(x=x, method='SFSelect', n=100)
# sfs0 = utl.scanGenomeSNP(rutl.getNut(0, skipHetChroms=True), SFSelect)
# sfst = utl.scanGenomeSNP(rutl.getNut(59, skipHetChroms=True), SFSelect).rename(59); sfs=(sfst-sfs0); sfs[sfs<0]=None
g = ga.loadGeneCoordinates().set_index('name')
genes = g.loc[['Ace', 'Cyp6g1', 'CHKov1']].reset_index().set_index('CHROM')
shade = scores.sort_values().reset_index().iloc[-2:].rename(columns={'POS': 'start'});
shade['end'] = shade.start + 100
cand = pd.concat([scores, scores.rank(ascending=False).rename('rank'), rutl.getNut(0, skipHetChroms=True)],
axis=1).sort_values('rank')
chroms = ['2L', '2R', '3L', '3R']
reload(utl)
# reload(pplt);pplt.Genome(sfs.loc[chroms],genes=genes);plt.tight_layout(pad=0.1)
df = pd.concat(
[utl.scanGenomeSNP(scores.abs(), lambda x: x.mean(), winSize=200, step=100, skipFromFirst=900).rename(200),
utl.scanGenomeSNP(scores.abs(), lambda x: x.mean(), winSize=500, step=100, skipFromFirst=750).rename(500),
utl.scanGenomeSNP(scores.abs(), lambda x: x.mean(), winSize=1000, step=100, skipFromFirst=500).rename(
1000)], axis=1)
df['comb'] = df[200] * df[500] * df[1000]
fig = plt.figure(figsize=(7, 4.5), dpi=300);
pplt.Manhattan(data=sort(df.rename(columns={'comb': '200*500*1000'})), fig=fig, markerSize=2, ticksize=8,
sortedAlready=True);
[pplt.setSize(ax, 5) for ax in fig.get_axes()];
plt.gcf().subplots_adjust(bottom=0.15);
plt.savefig(utl.paperPath + 'new/{}.pdf'.format('SNPbased'))
pplt.Genome(df.comb);
plt.tight_layout(pad=0.1)
# analyzie()
# scanSFS()
# outlier()
# scanSFSSNPbased()
a = df.comb
o = localOutliers(a, q=0.9);
fig = plt.figure(figsize=(7, 1.5), dpi=300);
pplt.Manhattan(data=a, Outliers=pd.DataFrame(o), fig=fig, markerSize=2, ticksize=8, sortedAlready=True);
[pplt.setSize(ax, 5) for ax in fig.get_axes()];
plt.gcf().subplots_adjust(bottom=0.15);
plt.savefig(utl.paperPath + 'new/{}.pdf'.format('SNPbased.candidates'))
Scores = pd.concat([scores.rename('scores').abs(), scores.groupby(level=0).apply(
lambda x: pd.Series(range(x.size), index=x.loc[x.name].index)).rename('i')], axis=1)
cutoff = FDR(o, Scores);
a = pd.concat([df, cutoff[cutoff.sum(1) > 0]], axis=1).dropna();
for fdr in [0.95, 0.99, 0.999]:
o = a[a.comb > a[fdr]]
fig = plt.figure(figsize=(7, 1.5), dpi=300);
pplt.Manhattan(data=df.comb, Outliers=pd.DataFrame(o), fig=fig, markerSize=2, ticksize=8, sortedAlready=True);
[pplt.setSize(ax, 5) for ax in fig.get_axes()];
plt.gcf().subplots_adjust(bottom=0.15);
plt.savefig(utl.paperPath + 'new/{}.pdf'.format('SNPbased.fdr{}'.format(fdr)))
def plotNull(subp, nu0=0.005, fontsize=5):
obs = pd.read_pickle(utl.outpath + 'markov/neutral.obs.{}.pkl'.format(nu0))
T = Markov.computeTransition(0, N=1000)
dfplt = pd.concat([pd.Series({'scale': 10, 'xlim': [0.0, 0.01], 'ylim': [0, 1]}, name=(0.005, 1)),
pd.Series({'scale': 30, 'xlim': [0.06, 0.14], 'ylim': [0, 0.15]}, name=(0.1, 1)),
pd.Series({'scale': 30, 'xlim': [0.0, 0.015], 'ylim': [0, 0.3]}, name=(0.005, 10)),
pd.Series({'scale': 45, 'xlim': [0.0, 0.2], 'ylim': [0, 0.025]}, name=(0.1, 10)),
pd.Series({'scale':30, 'xlim':[0.0,0.03],'ylim': [0,0.2]},name=(0.005,100)),pd.Series({'scale':50, 'xlim':[0.00,0.4],'ylim': [0,0.004]},name=(0.1,100))
],axis=1).T
markov=T.loc[nu0].copy(True);markov.name='Markov Chain'
xx=np.arange(0,1,0.00001)
N=200; tau=1;h=2*nu0*(1-nu0);sig2=h*tau/N;brownian=stats.norm(nu0, sig2).pdf(xx);
brownian=pd.Series(brownian,index=xx);brownian/=brownian.sum();brownian.name='Brownian Motion';brownian*=dfplt.loc[(nu0,tau)].scale
pplt.setSize(plt.gca(), fontsize=fontsize)
plt.subplot(3, 3, subp[0]);
brownian.plot(color='r');
markov.plot(color='b');
o=pd.Series(obs.X[1].flatten()).value_counts().sort_index();o=o/o.sum();
if nu0==0.1:
counts,limits=np.histogram(obs.X[1].flatten(),bins=500,range=[0,1]);centers = 0.5*(limits[1:]+limits[:-1]);o=pd.Series(counts,index=centers);o=o/(obs.X.shape[1]*obs.X.shape[2]*4)
o.plot(color='g')
plt.xlim(dfplt.loc[(nu0, tau)].xlim);
plt.ylim(dfplt.loc[(nu0, tau)].ylim);
plt.locator_params(nbins=3)
pplt.annotate(r'$s=0$, $\nu_0=${}, $\tau$={}'.format(nu0, tau), fontsize=fontsize)
plt.ylabel(r'$P(\nu_\tau|\nu_0)$')
tau=10
for _ in range(9):
markov=markov.dot(T)
N=200;h=2*nu0*(1-nu0);sig2=h*tau/N;brownian=stats.norm(nu0, sig2).pdf(xx)
brownian=pd.Series(brownian,index=xx);brownian/=brownian.sum();brownian.name='Brownian Motion';
brownian*=dfplt.loc[(nu0,tau)].scale
pplt.setSize(plt.gca(), fontsize=fontsize)
plt.title('({})'.format(subptitle[subp[0] - 1]), fontsize=fontsize)
plt.subplot(3, 3, subp[1]);
brownian.plot(color='r');
markov.plot(color='b');
o=pd.Series(obs.X[10].flatten()).value_counts().sort_index();o=o/o.sum();
if nu0==0.1:
counts,limits=np.histogram(obs.X[10].flatten(),bins=100,range=[0,1]);centers = 0.5*(limits[1:]+limits[:-1]);o=pd.Series(counts,index=centers);o=o/(obs.X.shape[1]*obs.X.shape[2]*20)
o.plot(color='g')
plt.xlim(dfplt.loc[(nu0, tau)].xlim);
plt.ylim(dfplt.loc[(nu0, tau)].ylim);
plt.locator_params(nbins=3)
pplt.annotate(r'$s=0$, $\nu_0=${}, $\tau$={}'.format(nu0, tau), loc=1, fontsize=fontsize)
pplt.setSize(plt.gca(), fontsize=fontsize)
tau=100
for _ in range(90):
markov=markov.dot(T)
N=200;h=2*nu0*(1-nu0);sig2=h*tau/N;brownian=stats.norm(nu0, sig2).pdf(xx)
brownian=pd.Series(brownian,index=xx);brownian/=brownian.sum();brownian.name='Brownian Motion';
brownian*=dfplt.loc[(nu0,tau)].scale
plt.title('({})'.format(subptitle[subp[1] - 1]), fontsize=fontsize)
plt.subplot(3, 3, subp[2]);
brownian.plot(color='r');
markov.plot(color='b')
o=pd.Series(obs.X[100].flatten()).value_counts().sort_index();o=o/o.sum();
if nu0==0.1:
counts,limits=np.histogram(obs.X[100].flatten(),bins=30,range=[0,1]);centers = 0.5*(limits[1:]+limits[:-1]);o=pd.Series(counts,index=centers);o=o/(obs.X.shape[1]*obs.X.shape[2]*60)
o.name = 'Observation';
o.plot(color='g')
plt.xlim(dfplt.loc[(nu0, tau)].xlim);
plt.ylim(dfplt.loc[(nu0, tau)].ylim);
plt.locator_params(nbins=3)
pplt.annotate(r'$s=0$, $\nu_0=${}, $\tau$={}'.format(nu0, tau), loc=1, fontsize=fontsize)
if subp[2] == 3:
plt.legend(loc='center right', fontsize=fontsize)
pplt.setSize(plt.gca(), fontsize=fontsize)
plt.title('({})'.format(subptitle[subp[2] - 1]), fontsize=fontsize)
def plotPowerCLRQ(recompute=False):
dpi = pplt.PLOS.dpi;
fontsize = 7
sns.set_style("whitegrid", {"grid.color": "0.9", 'axes.linewidth': .5, "grid.linewidth": ".09"})
if recompute:
a = f(loadHMMAllDepths());
a = a[a.index.get_level_values('coverage') != np.inf]
Q = np.sort(np.append(np.arange(0, 1.01, 0.1), 0.9 + np.arange(0, 1, 0.1)[1:] / 10))
# Q = [0, 0.5,0.9,0.95,0.96,0.97,0.98, 0.99, 1]
df = pd.concat(map(lambda q: a.groupby(level=range(6)).apply(lambda x: x[x >= x.quantile(q)].mean()), Q),
axis=1)
dfa = pd.concat(map(lambda q: a.abs().groupby(level=range(6)).apply(lambda x: x[x >= x.quantile(q)].mean()), Q),
axis=1)
df.columns = pd.MultiIndex.from_product([Q, [False]], names=['Quantile', 'ModifiedLR'])
df = df.stack(df.columns.names).reorder_levels([0, 6, 7] + range(1, 6))
dfa.columns = pd.MultiIndex.from_product([Q, [True]], names=['Quantile', 'ModifiedLR'])
dfa = dfa.stack(dfa.columns.names).reorder_levels([0, 6, 7] + range(1, 6))
df = pd.concat([df, dfa])
df.to_pickle(utl.outpath + 'ROC/PowerCLRTable.df')
df = df[df.index.get_level_values("coverage") != np.inf]
boot = pd.DataFrame([np.sort(np.random.choice(1000, 250, replace=False)) for _ in range(100)]).T;
print boot
dfboot = boot.groupby(level=0, axis=1).apply(
lambda x: getPower(df.loc[pd.IndexSlice[:, :, :, :, :, :, :, x[x.name].values]].sort_index(),
groupbyLevels=range(6)).xs('HMM', level='method')).groupby(level=range(4)).mean();
print dfboot
dfboot.columns.name = 'i'
dfboot = dfboot.stack('i').reset_index(['i', 'ModifiedLR', 'Quantile']);
print dfboot
dfboot.to_pickle(utl.outpath + 'ROC/PowerCLRTableBootstrap.df')
else:
df = pd.read_pickle(utl.outpath + 'ROC/PowerCLRTable.df')
dfboot = pd.read_pickle(utl.outpath + 'ROC/PowerCLRTableBootstrap.df')
dfboot.Quantile = (dfboot.Quantile * 100).astype(int)
sns.set_context(rc={"lines.linewidth": 0.5})
pistar = {}
ABCD = map(lambda x: '({})'.format(x), list('ABCDEFG'))
fig, axes = plt.subplots(2, 3, sharey=True, sharex=True, figsize=(6, 2.5), dpi=dpi);
j = 0
for nu0, axs in zip([0.005, 0.1], axes):
for depth, ax in zip([30, 100, 300], axs):
a = dfboot.loc[depth].loc[nu0]
if nu0 == 0.005: ax.set_title(r'$\lambda$={}'.format(str(depth)).replace('inf', '$\infty$'))
sns.tsplot(data=a, time='Quantile', unit='i', value=0, condition='ModifiedLR', ci=99.99, legend=False,
color=['r', 'darkblue'], ax=ax)
pistar.update({ax: (ABCD[j], r'($\pi^*=${})'.format(a.groupby('Quantile')[0].mean().idxmax()))})
pplt.setSize(ax, fontsize)
ax.set_xlabel('');
ax.set_ylabel('')
j += 1
axes[0][0].locator_params(nbins=3);
for ax in axes[1]: ax.set_xlabel(r'$\pi$')
for ax in [axes[0][0], axes[1][0]]: ax.set_ylabel('Avg. Power\n({} Sweep)'.format(('Soft', 'Hard')[nu0 == 0.005]))
plt.gcf().subplots_adjust(bottom=0.2)
# [pplt.annotate(v[1],ax=k,fontsize=fontsize) for k,v in pistar.items() ]
[pplt.annotate('({})'.format(list('ABCDEF')[j]), ax=x, fontsize=7) for j, x in enumerate(axes.reshape(-1))]
[pplt.annotate(v[1], ax=k, fontsize=7, xpad=0.6) for x, (k, v) in zip(ABCD, pistar.items())]
axes[1][-1].legend([r'$\mathcal{H}$', '$\mathcal{H}^+$'], loc='lower right', prop={'size': fontsize})
mpl.rc('font', **{'family': 'serif', 'serif': ['Computer Modern'], 'size': fontsize});
mpl.rc('text', usetex=True)
pplt.savefig('CLRQ', dpi)
plt.show()
pd.options.display.max_rows = 20;
pd.options.display.expand_frame_repr = False
import popgen.TimeSeries.RNN.Evaluate as evl
import seaborn as sns
import popgen.Plots as pplt
df = evl.randomROCData()
sns.set_style("white", {"grid.color": "0.9", 'axes.linewidth': .5, "grid.linewidth": "9.99"})
sns.set_context("notebook", font_scale=1, rc={"lines.linewidth": 1})
mpl.rc('font', **{'family': 'serif', 'serif': ['Computer Modern'], 'size': 6});
mpl.rc('text', usetex=True)
reload(evl)
dpi = 300
plt.figure(figsize=(4, 2), dpi=dpi)
plt.subplot(1, 2, 1)
evl.plotROC(df, FPth=1)
plt.ylabel('True Positive Rate (TPR)')
plt.xlabel('False Positive Rate (FPR)')
pplt.setSize(plt.gca())
plt.subplot(1, 2, 2)
evl.plotROC(df)
plt.xlabel('False Positive Rate (FPR)')
pplt.setSize(plt.gca())
plt.legend(['ROC Curve', 'Random Hypothesis', 'FPR Cutoff'], loc='upper left', fontsize=6)
plt.tight_layout(pad=0.1)
pplt.savefig('powerROC', dpi)
plt.show()
nu = pd.Series(np.arange(0, 1.00001, 0.001), index=np.arange(0, 1.00001, 0.001))
def bio(cd): c, d = cd;return sc.misc.comb(d, c) * ((nu) ** c) * ((1 - nu) ** (d - c))
cd = np.array([1, 5])
a = bio(cd);
a /= a.sum()
cd *= 10;
b = bio(cd);
b /= b.sum()
cd *= 10;
c = bio(cd);
c /= c.sum()
dpi = 300
plt.figure(figsize=(4, 2), dpi=dpi)
df = pd.DataFrame([a, b, c]).T
df
df.plot(ax=plt.gca())
plt.legend([r'Pr($\nu |c=1,d=5$)', r'Pr($\nu |c=10,d=50$)', r'Pr($\nu|c=100,d=500$)'], fontsize=6)
plt.xlabel(r'$\nu$')
plt.ylabel(r'Pr($\nu|c,d$)')
plt.ylim([-0.0005, plt.ylim()[1]])
pplt.setSize(plt.gca(), 6)
plt.gcf().subplots_adjust(bottom=0.15)
pplt.savefig('statePosterior', dpi)
plt.show()
def Final():
############ preparing data
def saveGOTex(df):
name = np.unique(df.index)[0]
print '*' * 80, name
df = df.sort_values('-log($p$-value)', ascending=False)
df['Rank'] = range(1, df.shape[0] + 1);
df = df.iloc[:, [6] + range(6)]
path = utl.paperPath + '/tables/{}.tex'.format(name);
df.to_csv(path.replace('.tex', '.csv').replace('/tables/', '/data/'))
utl.DataframetolaTexTable(df.iloc[:, :-1], alignment=['c', 'c', 'p{3in}', 'c', 'c', 'c'], fname=path)
goPvalue = lambda x: utl.getPvalFisher(AllGenes=allVariantGenes.values, putativeList=x.values,
myList=g.index.values)
unpackp = lambda x: [min(6, np.round(x[0], 1)), x[1].loc['Putative', 'myList']]
# Score = lambda x,f:f(scores.loc[x.CHROM][(scores.loc[x.CHROM].index>=x.start)&(scores.loc[x.CHROM].index<=x.end)])
sort = lambda df: pd.concat(
[df[df.index.get_level_values('CHROM') == ch] for ch in ['X', '2L', '2R', '3L', '3R']]).rename(
columns={'H': r'$\mathcal{H}^+$', 'M': 'Num. of Variants'})
Genes = loadGeneData().reset_index().set_index('GO')
Genes = Genes.loc[
(Genes['FBgn'].groupby(level=0).apply(lambda x: len(x.unique())) > 2).replace({False: None}).dropna().index]
scores = utl.getEuChromatin(rutl.loadScores(skipHetChroms=True))
ann = pd.DataFrame(scores).join(loadANN(), how='inner')
allVariantGenes = ann['Gene_ID'].drop_duplicates()
# f=lambda x: x[x>=x.quantile(0.9)].mean()
# geneScores=ann.reset_index().set_index('Gene_ID')[['CHROM','POS',0]].drop_duplicates().groupby(level=0)[0].apply(f)
############ computing candidate regions
scan = utl.scanGenome(scores.abs(), {'H': lambda x: x.abs().mean(), 'M': lambda x: x.size}, winSize=30000)
o = utl.localOutliers(scan.H, q=0.99);
o = scan.loc[o.index]
fig = plt.figure(figsize=(7, 2.5), dpi=300);
pplt.Manhattan(data=sort(scan), Outliers=sort(o), fig=fig, markerSize=2, ticksize=8, sortedAlready=True);
[pplt.setSize(ax, 6) for ax in fig.get_axes()];
pplt.annotate('(A)', ax=fig.axes[0], fontsize=8)
pplt.annotate('(B)', ax=fig.axes[1], fontsize=8)
plt.gcf().subplots_adjust(bottom=0.15);
pplt.savefig('manhattan', 300)
plt.savefig(utl.paperFiguresPath + 'manhattan.pdf')
regions = utl.BED.getIntervals(o.H, padding=30000);
print regions.shape
intervalGenes = utl.BED.intersection(ann, regions).name.drop_duplicates().reset_index().set_index('name');
print intervalGenes.size
g = intervalGenes;
# intervalGenes
# g=g[g>=g.quantile(0.)];
print g.size
df = Genes.groupby(level=0).apply(lambda x: pd.DataFrame(
[x.name, x.term.iloc[0]] + unpackp(goPvalue(x.FBgn.drop_duplicates())) + [x.ontology.iloc[0],
x.FBgn.unique().size] + [
np.intersect1d(x.values, g.index.values)],
index=['GO ID', 'GO Term', '-log($p$-value)', 'Hits', 'Ontology', 'Num of Genes', 'Genes']).T)
df = df[(df['-log($p$-value)'] >= 3) & (df.Hits >= 3)]
df['-log($p$-value)'] = df['-log($p$-value)'].astype(str)
df = df.set_index('Ontology')
df.groupby(level=0).apply(saveGOTex);
print df
tempGenes = Genes.reset_index().set_index('FBgn').loc[
np.append(df.set_index('GO ID').loc['GO:0009631'].Genes, df.set_index('GO ID').loc['GO:0009408'].Genes)][
['term', 'name', 'GO']].reset_index().set_index('GO').loc[['GO:0009631', 'GO:0009408']].drop_duplicates()
tempGenes.columns = ['FlyBase ID', 'GO Term', 'Gene Name']
utl.DataframetolaTexTable(tempGenes, fname=utl.paperPath + '/tables/{}.tex'.format('tempGenes'),
alignment=['l', 'l', 'l'])
regions.to_csv(utl.paperPath + 'data/intervals.csv')
snps = utl.BED.intersection(scores.reset_index(), regions, 0);
snps['POS'] = snps.start;
snps.set_index('POS', append=True, inplace=True)
snps = snps['name'].astype(float).reset_index().drop_duplicates().set_index(['CHROM', 'POS']).name
def ff(x):
y = utl.BED.intersection(scores.reset_index(), x, 0).rename(columns={'start': 'POS'}).set_index('POS',
append=True).name.astype(
float)
y = y[y > 0]
y = y[y >= y.quantile(0.9)]
print x['len'].iloc[0], y.size
return y
cands = regions.reset_index().groupby(level=0).apply(ff).reset_index(level=0).name
cands.sort_index().reset_index().drop_duplicates().dropna().to_csv(utl.outpath + 'real/gowinda/cands.final.txt',
sep='\t', header=None, index=False)
scores.sort_index().reset_index().drop_duplicates().dropna().to_csv(utl.outpath + 'real/gowinda/allsnps.txt',
sep='\t', header=None, index=False)
name = 'cands.final.out.tsv'
gowinda = pd.read_csv('/home/arya/out/real/gowinda/{}'.format(name), sep='\t', header=None)[[0, 4, 5, 6, 7, 8, 9]]
gowinda.columns = ['GO ID', '-log($p$-value)', 'Hits', 'Num of Genes', 'Total Genes', 'GO Term', 'Genes']
gowinda = gowinda[gowinda.Hits >= 3]
gowinda['-log($p$-value)'] = -gowinda['-log($p$-value)'].apply(np.log10).round(1)
gowinda.to_csv(utl.paperPath + 'data/gowinda.all.tsv', sep='\t')
bp = gowinda.set_index('GO ID').loc[
Genes[Genes.ontology == 'biological_process'].index.unique().rename('GO ID')].dropna()
bp.to_csv(utl.paperPath + 'data/gowinda.bp.tsv', sep='\t')
utl.DataframetolaTexTable(bp.reset_index()[['GO ID', 'GO Term', '-log($p$-value)']], alignment=['c', 'p{4in}', 'c'],
fname=utl.paperPath + 'tables/gowinda.tex')
map(len, (Genes.index.unique(), bp.index.unique(), df.loc['biological_process']['GO ID'].unique())), len(
np.intersect1d(bp.index.unique(), df['GO ID'].unique()))
pval = utl.getPvalFisher(Genes[Genes.ontology == 'biological_process'].index.unique(), bp.index.unique(),
df.loc['biological_process']['GO ID'].unique())
print pval
stats = pd.Series(None, name='Value')
stats['Num. of Vatiants'] = scores.size
stats['Num. of Candidate Intervals'] = regions.shape[0]
stats['Total Num. of Genes'] = loadGeneCoordinates().shape[0]
stats['Num. of Variant Genes'] = ann['Gene_ID'].unique().shape[0]
stats['Num. of Genes within Candidate Intervals'] = intervalGenes.shape[0]
stats['Total Num. of GO'] = len(loadGeneData().index.unique())
stats['Num. of GO with 3 or More Genes'] = len(Genes.index.unique())
stats['Num. of Candidate Variants for Gowinda'] = cands.size
stats = stats.apply(lambda x: '{:,.0f}'.format(x))
stats.index.name = 'Statistic'
print stats
utl.DataframetolaTexTable(stats.reset_index(), fname=utl.paperPath + 'tables/stats.tex', alignment=['l', 'r'])