def graph_dendrogram_branches(dfWindow,ranWindow,names,fileName):
# Get group, mean and standard deviation for AT
ATgroup,ATmean,ATstd = collect_sum_two_nucleotides(dfWindow,names,'A','T')
ranATgroup,ranATmean,ranATstd = collect_sum_two_nucleotides(ranWindow,names,'A','T')
ATelement = ATgroup.T[(GlobalVariables.plotLineLocationThree-GlobalVariables.methylationflank):(GlobalVariables.plotLineLocationFour+GlobalVariables.methylationflank)]
ranATelement = ranATgroup.T[(GlobalVariables.plotLineLocationThree-GlobalVariables.methylationflank):(GlobalVariables.plotLineLocationFour+GlobalVariables.methylationflank)]
print 'Extracted just element and methylation flank, size {0}'.format(len(ATelement))
# Title info
info = str(fileName) + ', '+ str(len(ATgroup.index)) + ' - ' "UCES"
# Plot settings
plt.suptitle(info,fontsize=10)
pp = PdfPages('Dendrogram_{0}.pdf'.format(fileName))
sns.set_palette("husl",n_colors=8)
palette = sns.color_palette()
set_link_color_palette(map(rgb2hex, palette))
sns.set_style('white')
#http://nbviewer.jupyter.org/gist/vals/150ec97a5b7db9c82ee9
link = linkage(ATelement.T)
plt.figure(figsize=(100,10))
den = dendrogram(link,labels=ATelement.T.index,leaf_font_size=3,color_threshold='#AAAAAA')#,link_color_func=lambda x:___[x] Have to make dict of len df, with clustered colors
plt.xticks(rotation=90,fontsize=8)
sns.despine()
plt.tight_layout()
sns.despine()
pp.savefig()
pp.close()
def graph_k_means(dfWindow,ranWindow,names,fileName):
# Parameters that all graphs will use
plt.figure(figsize=(7,7))
# Get group, mean and standard deviation for AT
ATgroup,ATmean,ATstd = collect_sum_two_nucleotides(dfWindow,names,'A','T')
ranATgroup,ranATmean,ranATstd = collect_sum_two_nucleotides(ranWindow,names,'A','T')
ATelement = ATgroup.T[(GlobalVariables.plotLineLocationThree-GlobalVariables.methylationflank):(GlobalVariables.plotLineLocationFour+GlobalVariables.methylationflank)]
ranATelement = ranATgroup.T[(GlobalVariables.plotLineLocationThree-GlobalVariables.methylationflank):(GlobalVariables.plotLineLocationFour+GlobalVariables.methylationflank)]
print 'Extracted just element and methylation flank, size {0}'.format(len(ATelement))
# Title info
info = str(fileName) + ', '+ str(len(ATgroup.index)) + ' - ' "UCES"
# Plot settings
sns.set_style('ticks')
plt.suptitle(info,fontsize=10)
pp = PdfPages('Kmeans_{0}.pdf'.format(fileName))
sns.set_palette("husl",n_colors=8)
# get the average first/last inset
upinset = ATgroup.T[GlobalVariables.plotLineLocationThree:GlobalVariables.plotLineLocationOne].mean()
downinset = ATgroup.T[GlobalVariables.plotLineLocationTwo:GlobalVariables.plotLineLocationFour].mean()
ATlist = [list(a) for a in zip(upinset,downinset)]
ATarray = np.array(ATlist)
print ATarray
#https://stackoverflow.com/questions/42398403/python-k-means-clustering-array
# kmeans = KMeans(n_clusters=5,random_state=0).fit(ATarray)
# see labels
#print kmeans.labels_
# predict new points
#kmeans.predict([],[])
# see where the centres of clusters are
#kmeans.cluster_centers_
gs = gridspec.GridSpec(2,1,height_ratios=[1,1])
gs.update(hspace=.5)
# #https://stats.stackexchange.com/questions/9850/how-to-plot-data-output-of-clustering
ATstdinitial = [cluster.vq.kmeans(ATarray,i) for i in range(1,10)]
ax0 = plt.subplot(gs[0,:])
ax0.plot([var for (cent,var) in ATstdinitial])
cent,var = ATstdinitial[3]
assignment,cdist = cluster.vq.vq(ATarray,cent)
ax1 = plt.subplot(gs[1,:])
ax1.scatter(ATarray[:,0],ATarray[:,1],c=assignment)
def graph_interactive_bokeh(dfWindow, ranWindow, fileName):
seaborn.set_palette("husl", n_colors=8)
# Get group, mean and standard deviation for AT
ATgroup, ATmean, ATstd = collect_sum_two_nucleotides(
dfWindow, names, 'A', 'T')
ranATgroup, ranATmean, ranATstd = collect_sum_two_nucleotides(
ranWindow, names, 'A', 'T')
source = ColumnDataSource(data=dict(x=GlobalVariables.fillX,
mean=ATgroup.mean(),
std=ATgroup.std(),
rmean=ranATgroup.mean(),
rstd=ranATgroup.std()))
output_file('Interactive_{0}.html'.format(fileName))
p = figure(plot_width=1500,
plot_height=600,
min_border=10,
min_border_left=50,
toolbar_location="above",
title="Mean AT Content Across Base Pair Position")
p.line('x', 'mean', line_width=2, source=source)
p.yaxis.axis_label = "% AT Content"
p.xaxis.axis_label = "Nucleotide Postion"
p.background_fill_color = "#fafafa"
select = Select(title="Option:",
value="All",
option=["All", "Exonic", "Intronic", "Intergenic"])
#toggle = Toggle(label="Reverse Complement")
sd = figure(plot_width=1500,
plot_height=200,
x_range=p.x_range,
min_border=10,
min_border_left=50,
title="Standard Deviation")
sd.line('x', 'std', line_width=2, color='#3e1638', alpha=.5, source=source)
sd.background_fill_color = "#fafafa"
widgets = row(select, toggle)
show(column(widgets, p, sd))
def plot_line_graphs(elementDF, elementRC, collectDF, collectRC, allNames,
fileName, collectFile):
# Get group, mean and standard deviation for AT
# Plot settings
sns.set_style('ticks')
gs = gridspec.GridSpec(2, 1, height_ratios=[1, 1])
gs.update(hspace=.8) # setting the space between the graphs
pp = PdfPages('Fangs_{0}.pdf'.format(fileName))
plt.figure(figsize=(7, 7))
sns.set_palette("husl", n_colors=8) #(len(nucLine)*2)
ATgroupElement, ATmeanElement, ATstdElement = collect_sum_two_nucleotides(
elementDF, allNames, 'A', 'T')
ATgroupElementRC, ATmeanElementRC, ATstdElementRC = collect_sum_two_nucleotides(
elementRC, allNames, 'A', 'T')
# Plot the mean AT content with a std of 1
ax0 = plt.subplot(gs[0])
ax0.plot(GlobalVariables.fillX, ATmeanElement, linewidth=2, label='UCEs')
for dfNuc, file in zip(collectDF, collectFile):
ATgroup, ATmean, ATstd = collect_sum_two_nucleotides(
dfNuc, allNames, 'A', 'T')
ax0.plot(GlobalVariables.fillX, ATmean, linewidth=1,
alpha=0.3) #,label='{0}'.format(file)
ax0.axvline(x=GlobalVariables.plotLineLocationOne,
linewidth=.05,
linestyle='dashed',
color='#e7298a')
ax0.axvline(x=GlobalVariables.plotLineLocationTwo,
linewidth=.05,
linestyle='dashed',
color='#e7298a')
ax0.axvline(x=GlobalVariables.plotLineLocationThree,
linewidth=.05,
linestyle='dashed',
color='#bd4973')
ax0.axvline(x=GlobalVariables.plotLineLocationFour,
linewidth=.05,
linestyle='dashed',
color='#bd4973')
ax0.set_ylabel('% AT Content', size=8)
ax0.set_xlabel('Position', size=6)
ax0.legend(loc=0, fontsize=5, labelspacing=0.1)
ax0.set_title('Mean AT Content', size=8)
ax0.set_yticks(ax0.get_yticks()[::2])
plt.xlim(0, GlobalVariables.num)
# Plot the std = 1
ax1 = plt.subplot(gs[1], sharex=ax0)
ax1.plot(GlobalVariables.fillX, ATmeanElementRC, linewidth=2, label='UCEs')
for dfNuc, file in zip(collectRC, collectFile):
ATgroup, ATmean, ATstd = collect_sum_two_nucleotides(
dfNuc, allNames, 'A', 'T')
ax1.plot(GlobalVariables.fillX, ATmean, linewidth=1,
alpha=0.3) #,label='{0}'.format(file)
ax1.axvline(x=GlobalVariables.plotLineLocationOne,
linewidth=.05,
linestyle='dashed',
color='#e7298a')
ax1.axvline(x=GlobalVariables.plotLineLocationTwo,
linewidth=.05,
linestyle='dashed',
color='#e7298a')
ax1.axvline(x=GlobalVariables.plotLineLocationThree,
linewidth=.05,
linestyle='dashed',
color='#bd4973')
ax1.axvline(x=GlobalVariables.plotLineLocationFour,
linewidth=.05,
linestyle='dashed',
color='#bd4973')
ax1.set_yticks(ax1.get_yticks()[::2])
ax1.set_xlabel('Position', size=6)
ax1.set_ylabel('% AT Content', size=8)
ax1.set_title('Mean AT Content, Reverse Completement Sorted', size=8)
plt.setp(ax1.get_xticklabels(), visible=True)
ax1.legend(loc=0, fontsize=5, labelspacing=0.05)
sns.despine()
pp.savefig()
pp.close()
def graph_signal_lines(dfWindow, names, ranWindow, fileName):
# Get group, mean and standard deviation for AT
ATgroup, ATmean, ATstd = collect_sum_two_nucleotides(
dfWindow, names, 'A', 'T')
ranATgroup, ranATmean, ranATstd = collect_sum_two_nucleotides(
ranWindow, names, 'A', 'T')
# File name
info = str(fileName) + ', ' + str(len(ATgroup.index)) + ' - ' "UCES"
# Plot settings
sns.set_style('ticks')
plt.suptitle(info, fontsize=10)
sns.set_palette("husl", n_colors=8) #(len(nucLine)*2)
plt.figure(figsize=(7, 7))
# Filename
pp = PdfPages('Signal_{0}.pdf'.format(fileName))
# Get smoothed mean, first and second derivatives
smoothMean, firstDer, secondDer = collect_smoothed_lines(ATmean)
ransmoothMean, ranfirstDer, ransecondDer = collect_smoothed_lines(
ranATmean)
gs = gridspec.GridSpec(3, 3, height_ratios=[1, 1, 1])
gs.update(hspace=.65)
# Plot smoothed mean AT
ax0 = plt.subplot(gs[0, :])
ax0.plot(GlobalVariables.fillX,
smoothMean,
linewidth=1,
alpha=0.9,
label='Element')
ax0.plot(GlobalVariables.fillX,
ransmoothMean,
linewidth=1,
alpha=0.9,
label='Random')
ax0.axvline(x=GlobalVariables.plotLineLocationOne,
linewidth=.05,
linestyle='dashed',
color='#e7298a')
ax0.axvline(x=GlobalVariables.plotLineLocationTwo,
linewidth=.05,
linestyle='dashed',
color='#e7298a')
ax0.axvline(x=GlobalVariables.plotLineLocationThree,
linewidth=.05,
linestyle='dashed',
color='#bd4973')
ax0.axvline(x=GlobalVariables.plotLineLocationFour,
linewidth=.05,
linestyle='dashed',
color='#bd4973')
ax0.set_yticks(ax0.get_yticks()[::2])
ax0.set_ylabel('% AT Content', size=8)
ax0.set_title('Fitted Mean AT Content', size=8)
# First derivative
ax1 = plt.subplot(gs[1, :], sharex=ax0)
ax1.plot(GlobalVariables.fillX,
firstDer,
linewidth=1,
alpha=0.8,
label='Element')
ax1.plot(GlobalVariables.fillX,
ranfirstDer,
linewidth=1,
alpha=0.8,
label='Random')
ax1.axvline(x=GlobalVariables.plotLineLocationOne,
linewidth=.05,
linestyle='dashed',
color='#e7298a')
ax1.axvline(x=GlobalVariables.plotLineLocationTwo,
linewidth=.05,
linestyle='dashed',
color='#e7298a')
ax1.axvline(x=GlobalVariables.plotLineLocationThree,
linewidth=.05,
linestyle='dashed',
color='#bd4973')
ax1.axvline(x=GlobalVariables.plotLineLocationFour,
linewidth=.05,
linestyle='dashed',
color='#bd4973')
ax1.axhline(y=0, linewidth=.1, alpha=0.3) #,color='#bd4973'
ax1.set_yticks(ax1.get_yticks()[::2])
ax1.set_ylabel('Amplitude', size=8)
ax1.set_title('First Derivative of Fitted Mean', size=8)
# Second derivative
ax2 = plt.subplot(gs[2, :], sharex=ax0)
peakMean = signal.find_peaks_cwt(secondDer, np.arange(1, 45)).astype(int)
print 'Found peaks for elements second derivative'
ax2.plot(GlobalVariables.fillX,
secondDer,
linewidth=1,
alpha=0.7,
label='Element')
ax2.plot(GlobalVariables.fillX,
ransecondDer,
linewidth=1,
alpha=0.7,
label='Random')
ax2.scatter(peakMean, secondDer[peakMean], marker='.') #,color='#ae3e9e'
ax2.axvline(x=GlobalVariables.plotLineLocationOne,
linewidth=.05,
linestyle='dashed',
color='#e7298a')
ax2.axvline(x=GlobalVariables.plotLineLocationTwo,
linewidth=.05,
linestyle='dashed',
color='#e7298a')
ax2.axvline(x=GlobalVariables.plotLineLocationThree,
linewidth=.05,
linestyle='dashed',
color='#bd4973')
ax2.axvline(x=GlobalVariables.plotLineLocationFour,
linewidth=.05,
linestyle='dashed',
color='#bd4973')
ax2.axhline(y=0, linewidth=.1, alpha=0.3) #,color='#bd4973'
ax2.set_ylabel('Amplitude', size=8)
ax2.set_xlabel('Position', size=6)
ax2.set_yticks(ax2.get_yticks()[::2])
ax2.set_title('Second Derivative of Fitted Mean', size=8)
sns.despine()
plt.savefig(pp, format='pdf')
print 'Plotted mean, first and second derivatives for elements and random regions'
# Frequency of inflection points
gs = gridspec.GridSpec(1, 1, height_ratios=[1])
ax3 = plt.subplot(gs[0])
inflectionList, infUCEpeaks = locate_second_derivative_inflection_points(
ATgroup)
raninflectionList, raninfUCEpeaks = locate_second_derivative_inflection_points(
ranATgroup)
IFbins = GlobalVariables.num / 10
ax3.hist(inflectionList, IFbins, alpha=0.3, label='Element')
ax3.hist(raninflectionList, IFbins, alpha=0.3, label='Random')
ax3.axvline(x=GlobalVariables.plotLineLocationOne,
linewidth=.05,
linestyle='dashed',
color='#e7298a')
ax3.axvline(x=GlobalVariables.plotLineLocationTwo,
linewidth=.05,
linestyle='dashed',
color='#e7298a')
ax3.axvline(x=GlobalVariables.plotLineLocationThree,
linewidth=.05,
linestyle='dashed',
color='#bd4973')
ax3.axvline(x=GlobalVariables.plotLineLocationFour,
linewidth=.05,
linestyle='dashed',
color='#bd4973')
ax3.set_yticks(ax3.get_yticks()[::2])
ax3.set_ylabel('Frequency', size=8)
ax3.legend(loc=0, fontsize=5, labelspacing=0.1)
ax3.set_xlabel('Inflection Point Location', size=8)
sns.despine()
plt.savefig(pp, format='pdf')
print 'Plotted inflection point frequency for elements and random regions'
gs = gridspec.GridSpec(2, 1, height_ratios=[1, 1])
gs.update(hspace=.65)
ax5 = plt.subplot(gs[0], sharex=ax0)
endRange = 25
widths = np.arange(1, endRange)
cwtmatr = signal.cwt(firstDer, signal.ricker, widths)
ax5.imshow(cwtmatr,
cmap='RdPu',
extent=[
0, (GlobalVariables.num - GlobalVariables.window), 1,
endRange
],
aspect='auto',
vmax=abs(cwtmatr).max(),
vmin=-abs(cwtmatr).max())
ax5.set_xlabel('Position', size=6)
ax5.set_yticks(ax5.get_yticks()[::2])
ax5.set_title(
'Continuous Wavelet Transformation Convolved Over Range {0}-{1} for the First Derivative, Element'
.format(widths[0], endRange),
size=8)
ax6 = plt.subplot(gs[1], sharex=ax0)
cwtmatran = signal.cwt(ranfirstDer, signal.ricker, widths)
ax6.imshow(cwtmatran,
cmap='RdPu',
extent=[
0, (GlobalVariables.num - GlobalVariables.window), 1,
endRange
],
aspect='auto',
vmax=abs(cwtmatran).max(),
vmin=-abs(cwtmatran).max())
ax6.set_xlabel('Position', size=6)
ax6.set_yticks(ax5.get_yticks()[::2])
ax6.set_title(
'Continuous Wavelet Transformation Convolved Over Range {0}-{1} for the First Derivative, Random Regions'
.format(widths[0], endRange),
size=8)
sns.despine()
plt.savefig(pp, format='pdf')
print 'Plotted continuous wavelet transformation for elements and random regions'
gs = gridspec.GridSpec(3, 3, height_ratios=[2, 1, 1])
gs.update(hspace=.65)
# Short Fourier Transform
ax7 = plt.subplot(gs[0, :], sharex=ax0)
sbins = 30
f1, t1, Zxx1 = signal.stft(firstDer,
fs=1.0,
window='hann',
nperseg=sbins,
noverlap=None) #,nperseg=11,noverlap=5
ax7.pcolormesh(t1, f1, np.abs(Zxx1), cmap='RdPu')
ax7.axvline(x=GlobalVariables.plotLineLocationOne,
linewidth=.05,
linestyle='dashed',
color='#5fc85b')
ax7.axvline(x=GlobalVariables.plotLineLocationTwo,
linewidth=.05,
linestyle='dashed',
color='#5fc85b')
ax7.axvline(x=GlobalVariables.plotLineLocationThree,
linewidth=.05,
linestyle='dashed',
color='#96c85b')
ax7.axvline(x=GlobalVariables.plotLineLocationFour,
linewidth=.05,
linestyle='dashed',
color='#96c85b')
ax7.set_ylabel('Frequency', size=8)
ax7.set_xlabel('Position', size=6)
ax7.set_yticks(ax7.get_yticks()[::2])
ax7.set_title(
'Short Fourier Transform over {0} bins for Elements'.format(sbins),
size=8)
# First Derivative
ax8 = plt.subplot(gs[1, :], sharex=ax0)
ax8.plot(GlobalVariables.fillX, firstDer, linewidth=1)
ax8.axvline(x=GlobalVariables.plotLineLocationOne,
linewidth=.05,
linestyle='dashed',
color='#e7298a')
ax8.axvline(x=GlobalVariables.plotLineLocationTwo,
linewidth=.05,
linestyle='dashed',
color='#e7298a')
ax8.axvline(x=GlobalVariables.plotLineLocationThree,
linewidth=.05,
linestyle='dashed',
color='#bd4973')
ax8.axvline(x=GlobalVariables.plotLineLocationFour,
linewidth=.05,
linestyle='dashed',
color='#bd4973')
ax8.axvspan(GlobalVariables.window,
GlobalVariables.plotLineLocationThree,
label='',
alpha=0.1,
facecolor='#863eae')
ax8.axvspan(GlobalVariables.plotLineLocationThree,
GlobalVariables.plotLineLocationFour,
label='',
alpha=0.1,
facecolor='#ae3e9e')
ax8.axvspan(GlobalVariables.plotLineLocationFour,
(GlobalVariables.num - GlobalVariables.window -
GlobalVariables.window),
label='',
alpha=0.1,
facecolor='#ae3e66')
ax8.set_yticks(ax8.get_yticks()[::2])
ax8.set_xlabel('Position', size=6)
ax8.set_ylabel('Amplitude', size=8)
ax8.set_title('First Derivative of Fitted Mean for Elements', size=8)
ysdElement = get_element_coordinates(firstDer)
frq2sd, Y2sd = perform_fourier_transfroms(ysdElement)
ysdUp = get_up_stream_flank_coordinates(firstDer)
frq3sd, Y3sd = perform_fourier_transfroms(ysdUp)
ysdDown = get_down_stream_flank_coordinates(firstDer)
frq4sd, Y4sd = perform_fourier_transfroms(ysdDown)
print 'Performed fourier transform for elements'
#FFT for sections of the smoothed second derivative
ax9 = plt.subplot(gs[2, 0])
ax9.plot(frq3sd, abs(Y3sd), linewidth=1, color='#863eae')
ax9.set_ylabel('|Y(freq)|', size=8)
ax9.set_xlabel('Freq(Hz)', size=6) #AT Rate Change
ax9.set_yticks(ax9.get_yticks()[::2])
ax10 = plt.subplot(gs[2, 1], sharey=ax9)
plt.setp(ax10.get_yticklabels(), visible=False)
ax10.plot(frq2sd, abs(Y2sd), linewidth=1, color='#ae3e9e')
ax10.set_title('Power Series for Highlighted Regions, Elements',
size=8) # Power Spectrum Analysis for FFT
ax10.set_xlabel('Freq(Hz)', size=6)
ax11 = plt.subplot(gs[2, 2], sharey=ax9)
plt.setp(ax11.get_yticklabels(), visible=False)
ax11.plot(frq4sd, abs(Y4sd), linewidth=1, color='#ae3e66')
ax11.set_xlabel('Freq(Hz)', size=6)
sns.despine()
plt.savefig(pp, format='pdf')
print 'Plotted short fourier transform and fast fourier transform for elements'
gs = gridspec.GridSpec(3, 3, height_ratios=[2, 1, 1])
gs.update(hspace=.65)
ax12 = plt.subplot(gs[0, :], sharex=ax0)
sbins = 30
ranf1, rant1, ranZxx1 = signal.stft(ranfirstDer,
fs=1.0,
window='hann',
nperseg=sbins,
noverlap=None) #,nperseg=11,noverlap=5
ax12.pcolormesh(rant1, ranf1, np.abs(ranZxx1), cmap='RdPu')
ax12.axvline(x=GlobalVariables.plotLineLocationOne,
linewidth=.05,
linestyle='dashed',
color='#5fc85b')
ax12.axvline(x=GlobalVariables.plotLineLocationTwo,
linewidth=.05,
linestyle='dashed',
color='#5fc85b')
ax12.axvline(x=GlobalVariables.plotLineLocationThree,
linewidth=.05,
linestyle='dashed',
color='#96c85b')
ax12.axvline(x=GlobalVariables.plotLineLocationFour,
linewidth=.05,
linestyle='dashed',
color='#96c85b')
ax12.set_ylabel('Frequency', size=8)
ax12.set_xlabel('Position', size=6)
ax12.set_yticks(ax12.get_yticks()[::2])
ax12.set_title(
'Short Fourier Transform over {0} bins for Random Regions'.format(
sbins),
size=8)
# First Derivative
ax13 = plt.subplot(gs[1, :], sharex=ax0)
ax13.plot(GlobalVariables.fillX, ranfirstDer, linewidth=1)
ax13.axvline(x=GlobalVariables.plotLineLocationOne,
linewidth=.05,
linestyle='dashed',
color='#e7298a')
ax13.axvline(x=GlobalVariables.plotLineLocationTwo,
linewidth=.05,
linestyle='dashed',
color='#e7298a')
ax13.axvline(x=GlobalVariables.plotLineLocationThree,
linewidth=.05,
linestyle='dashed',
color='#bd4973')
ax13.axvline(x=GlobalVariables.plotLineLocationFour,
linewidth=.05,
linestyle='dashed',
color='#bd4973')
ax13.axvspan(GlobalVariables.window,
GlobalVariables.plotLineLocationThree,
label='',
alpha=0.1,
facecolor='#863eae')
ax13.axvspan(GlobalVariables.plotLineLocationThree,
GlobalVariables.plotLineLocationFour,
label='',
alpha=0.1,
facecolor='#ae3e9e')
ax13.axvspan(GlobalVariables.plotLineLocationFour,
(GlobalVariables.num - GlobalVariables.window -
GlobalVariables.window),
label='',
alpha=0.1,
facecolor='#ae3e66')
ax13.set_yticks(ax13.get_yticks()[::2])
ax13.set_xlabel('Position', size=6)
ax13.set_ylabel('Amplitude', size=8)
ax13.set_title('First Derivative of Fitted Mean for Random Regions',
size=8)
ranysdElement = get_element_coordinates(ranfirstDer)
ranfrq2sd, ranY2sd = perform_fourier_transfroms(ranysdElement)
ranysdUp = get_up_stream_flank_coordinates(ranfirstDer)
ranfrq3sd, ranY3sd = perform_fourier_transfroms(ranysdUp)
ranysdDown = get_down_stream_flank_coordinates(ranfirstDer)
ranfrq4sd, ranY4sd = perform_fourier_transfroms(ranysdDown)
print 'Performed fourier transform for random regions'
#FFT for sections of the smoothed second derivative
ax14 = plt.subplot(gs[2, 0])
ax14.plot(ranfrq3sd, abs(ranY3sd), linewidth=1, color='#863eae')
ax14.set_ylabel('|Y(freq)|', size=8)
ax14.set_xlabel('Freq(Hz)', size=6) #AT Rate Change
ax14.set_yticks(ax14.get_yticks()[::2])
ax15 = plt.subplot(gs[2, 1], sharey=ax9)
plt.setp(ax15.get_yticklabels(), visible=False)
ax15.plot(ranfrq2sd, abs(ranY2sd), linewidth=1, color='#ae3e9e')
ax15.set_title('Power Series for Highlighted Regions, Random Regions',
size=8)
ax15.set_xlabel('Freq(Hz)', size=6)
ax16 = plt.subplot(gs[2, 2], sharey=ax9)
plt.setp(ax16.get_yticklabels(), visible=False)
ax16.plot(ranfrq4sd, abs(ranY4sd), linewidth=1, color='#ae3e66')
ax16.set_xlabel('Freq(Hz)', size=6)
sns.despine()
pp.savefig()
pp.close()
print 'Plotted short fourier transform and fast fourier transform for random regions'
def graph_cluster(dfWindow, ranWindow, pdMeth, rnMeth, names, fileName):
plt.figure(figsize=(7, 7))
# Get group, mean and standard deviation for AT
ATgroup, ATmean, ATstd = collect_sum_two_nucleotides(
dfWindow, names, 'A', 'T')
ranATgroup, ranATmean, ranATstd = collect_sum_two_nucleotides(
ranWindow, names, 'A', 'T')
ATelement = ATgroup.T[(GlobalVariables.plotLineLocationThree -
GlobalVariables.methylationflank):(
GlobalVariables.plotLineLocationFour +
GlobalVariables.methylationflank)]
ranATelement = ranATgroup.T[(GlobalVariables.plotLineLocationThree -
GlobalVariables.methylationflank):(
GlobalVariables.plotLineLocationFour +
GlobalVariables.methylationflank)]
print 'Extracted just element and methylation flank, size {0}'.format(
len(ATelement))
# Title info
info = str(fileName) + ', ' + str(len(ATgroup.index)) + ' - ' "UCES"
# Plot settings
sns.set_style('ticks')
plt.suptitle(info, fontsize=10)
pp = PdfPages('Cluster_{0}.pdf'.format(fileName))
sns.set_palette("husl", n_colors=8) #(len(nucLine)*2)
# Use the row_colors to color those with similar SD?
huslPalette = sns.husl_palette(8, s=.45)
elementColors, positionColors = make_dictionary_for_colors(
ATelement, huslPalette)
heatmap0 = sns.clustermap(ATelement.T,
cmap='RdPu',
vmin=0,
vmax=100,
xticklabels=50,
col_cluster=False,
row_colors=elementColors,
col_colors=positionColors)
plt.setp(heatmap0.ax_heatmap.tick_params(labelsize=8))
plt.setp(heatmap0.ax_heatmap.set_yticks([]))
plt.setp(heatmap0.ax_heatmap.yaxis.tick_right())
plt.setp(
heatmap0.ax_heatmap.set_ylabel('{0} UCEs'.format(len(
ATelement.T.index)),
size=8))
plt.setp(heatmap0.ax_heatmap.set_xlabel('Position', size=10))
plt.setp(heatmap0.ax_heatmap.tick_params(labelsize=10))
plt.setp(
heatmap0.ax_heatmap.axvline(x=GlobalVariables.plotLineLocationOneFull,
linewidth=.05,
linestyle='dashed',
color='#96c85b',
alpha=0.5))
plt.setp(
heatmap0.ax_heatmap.axvline(x=GlobalVariables.plotLineLocationTwoFull,
linewidth=.05,
linestyle='dashed',
color='#5fc85b',
alpha=0.5))
plt.setp(
heatmap0.ax_heatmap.axvline(
x=GlobalVariables.plotLineLocationThreeFull,
linewidth=.05,
linestyle='dashed',
color='#5fc85b',
alpha=0.5))
plt.setp(
heatmap0.ax_heatmap.axvline(x=GlobalVariables.plotLineLocationFourFull,
linewidth=.05,
linestyle='dashed',
color='#96c85b',
alpha=0.5))
plt.setp(
heatmap0.ax_heatmap.set_title('Mean AT Content per Element', size=12))
# ATOrdered = heatmap0.dendrogram_row.reordered_ind
sns.despine()
pp.savefig()
# Use the row_colors to color those with similar SD?
ranelementColors, ranpositionColors = make_dictionary_for_colors(
ranATelement, huslPalette)
heatmap1 = sns.clustermap(ranATelement.T,
cmap='RdPu',
vmin=0,
vmax=100,
xticklabels=50,
col_cluster=False,
row_colors=ranelementColors,
col_colors=ranpositionColors)
plt.setp(heatmap1.ax_heatmap.tick_params(labelsize=8))
plt.setp(heatmap1.ax_heatmap.set_yticks([]))
plt.setp(heatmap1.ax_heatmap.yaxis.tick_right())
plt.setp(
heatmap1.ax_heatmap.set_ylabel('{0} UCEs'.format(
len(ranATelement.T.index)),
size=8))
plt.setp(heatmap1.ax_heatmap.set_xlabel('Position', size=10))
plt.setp(heatmap1.ax_heatmap.tick_params(labelsize=10))
plt.setp(
heatmap1.ax_heatmap.axvline(x=GlobalVariables.plotLineLocationOneFull,
linewidth=.05,
linestyle='dashed',
color='#96c85b',
alpha=0.5))
plt.setp(
heatmap1.ax_heatmap.axvline(x=GlobalVariables.plotLineLocationTwoFull,
linewidth=.05,
linestyle='dashed',
color='#5fc85b',
alpha=0.5))
plt.setp(
heatmap1.ax_heatmap.axvline(
x=GlobalVariables.plotLineLocationThreeFull,
linewidth=.05,
linestyle='dashed',
color='#5fc85b',
alpha=0.5))
plt.setp(
heatmap1.ax_heatmap.axvline(x=GlobalVariables.plotLineLocationFourFull,
linewidth=.05,
linestyle='dashed',
color='#96c85b',
alpha=0.5))
plt.setp(
heatmap1.ax_heatmap.set_title('Mean AT Content per Random Region',
size=12))
# ranATOrdered = heatmap1.dendrogram_row.reordered_ind
sns.despine()
pp.savefig()
print 'Plotted cluster plot for mean AT content for all elements and random regions'
# Various combinations to plot on heatmaps, just for element plus methylation flanks
# Frequency x Tissue x ID X Location
FreqPlusID, FreqMinusID = collect_methylation_by_index(pdMeth, 'id')
FreqPlusTis, FreqMinusTis = collect_methylation_by_index(pdMeth, 'tissue')
XPlus, XMinus = collect_tissue_by_id_dataframe(pdMeth, 'id', 'tissue')
ranFreqPlusID, ranFreqMinusID = collect_methylation_by_index(rnMeth, 'id')
ranFreqPlusTis, ranFreqMinusTis = collect_methylation_by_index(
rnMeth, 'tissue')
ranXPlus, ranXMinus = collect_tissue_by_id_dataframe(
rnMeth, 'id', 'tissue')
# Remove UCEs with out methylation within the element - only for ID group
FreqPlusID = FreqPlusID[(FreqPlusID.T != 0).any()]
FreqMinusID = FreqMinusID[(FreqMinusID.T != 0).any()]
ranFreqPlusID = ranFreqPlusID[(ranFreqPlusID.T != 0).any()]
ranFreqMinusID = ranFreqMinusID[(ranFreqMinusID.T != 0).any()]
# Make heatmap for # methylation on pos strand (Frequency)
heatmap2 = sns.clustermap(FreqPlusTis,
cmap='RdPu',
xticklabels=50,
col_cluster=False)
ylabels2 = heatmap2.ax_heatmap.get_yticklabels()
plt.setp(heatmap2.ax_heatmap.set_yticklabels(ylabels2, rotation=0))
plt.setp(heatmap2.ax_heatmap.yaxis.tick_right())
plt.setp(heatmap2.ax_heatmap.set_ylabel('Sample', size=10))
plt.setp(heatmap2.ax_heatmap.set_xlabel('Position', size=10))
plt.setp(heatmap2.ax_heatmap.tick_params(labelsize=10))
plt.setp(
heatmap2.ax_heatmap.axvline(x=GlobalVariables.plotLineLocationOneFull,
linewidth=.05,
linestyle='dashed',
color='#96c85b',
alpha=0.5))
plt.setp(
heatmap2.ax_heatmap.axvline(x=GlobalVariables.plotLineLocationTwoFull,
linewidth=.05,
linestyle='dashed',
color='#5fc85b',
alpha=0.5))
plt.setp(
heatmap2.ax_heatmap.axvline(
x=GlobalVariables.plotLineLocationThreeFull,
linewidth=.05,
linestyle='dashed',
color='#5fc85b',
alpha=0.5))
plt.setp(
heatmap2.ax_heatmap.axvline(x=GlobalVariables.plotLineLocationFourFull,
linewidth=.05,
linestyle='dashed',
color='#96c85b',
alpha=0.5))
plt.setp(
heatmap2.ax_heatmap.set_title(
'Methylation Frequency on Plus Strand for Elements', size=12))
sns.despine()
pp.savefig()
# Make heatmap for # methylation on pos strand (Frequency)
heatmap3 = sns.clustermap(FreqMinusTis,
cmap='RdPu',
xticklabels=50,
col_cluster=False)
ylabels3 = heatmap3.ax_heatmap.get_yticklabels()
plt.setp(heatmap3.ax_heatmap.set_yticklabels(ylabels3, rotation=0))
plt.setp(heatmap3.ax_heatmap.yaxis.tick_right())
plt.setp(heatmap3.ax_heatmap.set_ylabel('Sample', size=10))
plt.setp(heatmap3.ax_heatmap.set_xlabel('Position', size=10))
plt.setp(heatmap3.ax_heatmap.tick_params(labelsize=10))
plt.setp(
heatmap3.ax_heatmap.axvline(x=GlobalVariables.plotLineLocationOneFull,
linewidth=.05,
linestyle='dashed',
color='#96c85b',
alpha=0.5))
plt.setp(
heatmap3.ax_heatmap.axvline(x=GlobalVariables.plotLineLocationTwoFull,
linewidth=.05,
linestyle='dashed',
color='#5fc85b',
alpha=0.5))
plt.setp(
heatmap3.ax_heatmap.axvline(
x=GlobalVariables.plotLineLocationThreeFull,
linewidth=.05,
linestyle='dashed',
color='#5fc85b',
alpha=0.5))
plt.setp(
heatmap3.ax_heatmap.axvline(x=GlobalVariables.plotLineLocationFourFull,
linewidth=.05,
linestyle='dashed',
color='#96c85b',
alpha=0.5))
plt.setp(
heatmap3.ax_heatmap.set_title(
'Methylation Frequency on Minus Strand for Elements', size=12))
sns.despine()
pp.savefig()
print 'Plotted methylation frequency for tissue types x position, for element'
# Make heatmap for # methylation on pos strand (Frequency)
heatmap4 = sns.clustermap(ranFreqPlusTis,
cmap='RdPu',
xticklabels=50,
col_cluster=False)
ylabels4 = heatmap4.ax_heatmap.get_yticklabels()
plt.setp(heatmap4.ax_heatmap.set_yticklabels(ylabels4, rotation=0))
plt.setp(heatmap4.ax_heatmap.yaxis.tick_right())
plt.setp(heatmap4.ax_heatmap.set_ylabel('Sample', size=10))
plt.setp(heatmap4.ax_heatmap.set_xlabel('Position', size=10))
plt.setp(heatmap4.ax_heatmap.tick_params(labelsize=10))
plt.setp(
heatmap4.ax_heatmap.axvline(x=GlobalVariables.plotLineLocationOneFull,
linewidth=.05,
linestyle='dashed',
color='#96c85b',
alpha=0.5))
plt.setp(
heatmap4.ax_heatmap.axvline(x=GlobalVariables.plotLineLocationTwoFull,
linewidth=.05,
linestyle='dashed',
color='#5fc85b',
alpha=0.5))
plt.setp(
heatmap4.ax_heatmap.axvline(
x=GlobalVariables.plotLineLocationThreeFull,
linewidth=.05,
linestyle='dashed',
color='#5fc85b',
alpha=0.5))
plt.setp(
heatmap4.ax_heatmap.axvline(x=GlobalVariables.plotLineLocationFourFull,
linewidth=.05,
linestyle='dashed',
color='#96c85b',
alpha=0.5))
plt.setp(
heatmap4.ax_heatmap.set_title(
'Methylation Frequency on Plus Strand for Random Regions',
size=12))
sns.despine()
pp.savefig()
# Make heatmap for # methylation on pos strand (Frequency)
heatmap5 = sns.clustermap(ranFreqMinusTis,
cmap='RdPu',
xticklabels=50,
col_cluster=False)
ylabels5 = heatmap5.ax_heatmap.get_yticklabels()
plt.setp(heatmap5.ax_heatmap.set_yticklabels(ylabels5, rotation=0))
plt.setp(heatmap5.ax_heatmap.yaxis.tick_right())
plt.setp(heatmap5.ax_heatmap.set_ylabel('Sample', size=10))
plt.setp(heatmap5.ax_heatmap.set_xlabel('Position', size=10))
plt.setp(heatmap5.ax_heatmap.tick_params(labelsize=10))
plt.setp(
heatmap5.ax_heatmap.axvline(x=GlobalVariables.plotLineLocationOneFull,
linewidth=.05,
linestyle='dashed',
color='#96c85b',
alpha=0.5))
plt.setp(
heatmap5.ax_heatmap.axvline(x=GlobalVariables.plotLineLocationTwoFull,
linewidth=.05,
linestyle='dashed',
color='#5fc85b',
alpha=0.5))
plt.setp(
heatmap5.ax_heatmap.axvline(
x=GlobalVariables.plotLineLocationThreeFull,
linewidth=.05,
linestyle='dashed',
color='#5fc85b',
alpha=0.5))
plt.setp(
heatmap5.ax_heatmap.axvline(x=GlobalVariables.plotLineLocationFourFull,
linewidth=.05,
linestyle='dashed',
color='#96c85b',
alpha=0.5))
plt.setp(
heatmap5.ax_heatmap.set_title(
'Methylation Frequency on Minus Strand for Random Regions',
size=12))
sns.despine()
pp.savefig()
print 'Plotted methylation frequency for tissue types x position, for random regions'
# Make heatmap for # methylation on pos strand (Frequency)
heatmap6 = sns.clustermap(FreqPlusID,
cmap='RdPu',
xticklabels=50,
col_cluster=False)
ylabels6 = heatmap6.ax_heatmap.get_yticklabels()
plt.setp(heatmap6.ax_heatmap.set_yticklabels(ylabels6, rotation=0))
plt.setp(heatmap6.ax_heatmap.yaxis.tick_right())
plt.setp(
heatmap6.ax_heatmap.set_ylabel('{0} Elements'.format(
len(FreqPlusID.index)),
size=10))
plt.setp(heatmap6.ax_heatmap.set_xlabel('Position', size=10))
plt.setp(heatmap6.ax_heatmap.tick_params(labelsize=10))
plt.setp(
heatmap6.ax_heatmap.axvline(x=GlobalVariables.plotLineLocationOneFull,
linewidth=.05,
linestyle='dashed',
color='#96c85b',
alpha=0.5))
plt.setp(
heatmap6.ax_heatmap.axvline(x=GlobalVariables.plotLineLocationTwoFull,
linewidth=.05,
linestyle='dashed',
color='#5fc85b',
alpha=0.5))
plt.setp(
heatmap6.ax_heatmap.axvline(
x=GlobalVariables.plotLineLocationThreeFull,
linewidth=.05,
linestyle='dashed',
color='#5fc85b',
alpha=0.5))
plt.setp(
heatmap6.ax_heatmap.axvline(x=GlobalVariables.plotLineLocationFourFull,
linewidth=.05,
linestyle='dashed',
color='#96c85b',
alpha=0.5))
plt.setp(
heatmap6.ax_heatmap.set_title(
'Methylation Frequency on Plus Strand for Elements', size=12))
sns.despine()
pp.savefig()
# Make heatmap for # methylation on neg strand (Frequency)
heatmap7 = sns.clustermap(FreqMinusID,
cmap='RdPu',
xticklabels=50,
col_cluster=False)
ylabels7 = heatmap7.ax_heatmap.get_yticklabels()
plt.setp(heatmap7.ax_heatmap.set_yticklabels(ylabels7, rotation=0))
plt.setp(heatmap7.ax_heatmap.yaxis.tick_right())
plt.setp(
heatmap7.ax_heatmap.set_ylabel('{0} Elements'.format(
len(FreqMinusID.index)),
size=10))
plt.setp(heatmap7.ax_heatmap.set_xlabel('Position', size=10))
plt.setp(heatmap7.ax_heatmap.tick_params(labelsize=10))
plt.setp(
heatmap7.ax_heatmap.axvline(x=GlobalVariables.plotLineLocationOneFull,
linewidth=.05,
linestyle='dashed',
color='#96c85b',
alpha=0.5))
plt.setp(
heatmap7.ax_heatmap.axvline(x=GlobalVariables.plotLineLocationTwoFull,
linewidth=.05,
linestyle='dashed',
color='#5fc85b',
alpha=0.5))
plt.setp(
heatmap7.ax_heatmap.axvline(
x=GlobalVariables.plotLineLocationThreeFull,
linewidth=.05,
linestyle='dashed',
color='#5fc85b',
alpha=0.5))
plt.setp(
heatmap7.ax_heatmap.axvline(x=GlobalVariables.plotLineLocationFourFull,
linewidth=.05,
linestyle='dashed',
color='#96c85b',
alpha=0.5))
plt.setp(
heatmap7.ax_heatmap.set_title(
'Methylation Frequency on Minus Strand for Elements', size=12))
sns.despine()
pp.savefig()
print 'Plotted methylation frequency for element x position , element'
# Make heatmap for # methylation on pos strand (Frequency)
heatmap8 = sns.clustermap(ranFreqPlusID,
cmap='RdPu',
xticklabels=50,
col_cluster=False)
ylabels8 = heatmap8.ax_heatmap.get_yticklabels()
plt.setp(heatmap8.ax_heatmap.set_yticks([]))
plt.setp(heatmap8.ax_heatmap.yaxis.tick_right())
plt.setp(
heatmap8.ax_heatmap.set_ylabel('{0} Elements'.format(
len(ranFreqPlusID.index)),
size=10))
plt.setp(heatmap8.ax_heatmap.set_xlabel('Position', size=10))
plt.setp(heatmap8.ax_heatmap.tick_params(labelsize=10))
plt.setp(
heatmap8.ax_heatmap.axvline(x=GlobalVariables.plotLineLocationOneFull,
linewidth=.05,
linestyle='dashed',
color='#96c85b',
alpha=0.5))
plt.setp(
heatmap8.ax_heatmap.axvline(x=GlobalVariables.plotLineLocationTwoFull,
linewidth=.05,
linestyle='dashed',
color='#5fc85b',
alpha=0.5))
plt.setp(
heatmap8.ax_heatmap.axvline(
x=GlobalVariables.plotLineLocationThreeFull,
linewidth=.05,
linestyle='dashed',
color='#5fc85b',
alpha=0.5))
plt.setp(
heatmap8.ax_heatmap.axvline(x=GlobalVariables.plotLineLocationFourFull,
linewidth=.05,
linestyle='dashed',
color='#96c85b',
alpha=0.5))
plt.setp(
heatmap8.ax_heatmap.set_title(
'Methylation Frequency on Plus Strand for Random Regions',
size=12))
sns.despine()
pp.savefig()
# Make heatmap for # methylation on neg strand (Frequency)
heatmap9 = sns.clustermap(ranFreqMinusID,
cmap='RdPu',
xticklabels=50,
col_cluster=False)
ylabels9 = heatmap9.ax_heatmap.get_yticklabels()
plt.setp(heatmap9.ax_heatmap.set_yticks([]))
plt.setp(heatmap9.ax_heatmap.yaxis.tick_right())
plt.setp(
heatmap9.ax_heatmap.set_ylabel('{0} Elements'.format(
len(ranFreqMinusID.index)),
size=10))
plt.setp(heatmap9.ax_heatmap.set_xlabel('Position', size=10))
plt.setp(heatmap9.ax_heatmap.tick_params(labelsize=10))
plt.setp(
heatmap9.ax_heatmap.axvline(x=GlobalVariables.plotLineLocationOneFull,
linewidth=.05,
linestyle='dashed',
color='#96c85b',
alpha=0.5))
plt.setp(
heatmap9.ax_heatmap.axvline(x=GlobalVariables.plotLineLocationTwoFull,
linewidth=.05,
linestyle='dashed',
color='#5fc85b',
alpha=0.5))
plt.setp(
heatmap9.ax_heatmap.axvline(
x=GlobalVariables.plotLineLocationThreeFull,
linewidth=.05,
linestyle='dashed',
color='#5fc85b',
alpha=0.5))
plt.setp(
heatmap9.ax_heatmap.axvline(x=GlobalVariables.plotLineLocationFourFull,
linewidth=.05,
linestyle='dashed',
color='#96c85b',
alpha=0.5))
plt.setp(
heatmap9.ax_heatmap.set_title(
'Methylation Frequency on Minus StrandStrand for Random Regions',
size=12))
sns.despine()
pp.savefig()
print 'Plotted methylation frequency for element x position , random regions'
# Make heatmap for # methylation on pos strand (Frequency)
heatmap10 = sns.clustermap(XPlus, cmap='RdPu')
ylabels10 = heatmap10.ax_heatmap.get_yticklabels()
plt.setp(heatmap10.ax_heatmap.set_yticklabels(ylabels10, rotation=0))
plt.setp(heatmap10.ax_heatmap.yaxis.tick_right())
plt.setp(
heatmap10.ax_heatmap.set_ylabel('{0} Elements'.format(
len(FreqPlusID.index)),
size=10))
plt.setp(heatmap10.ax_heatmap.set_xlabel('Sample', size=10))
plt.setp(heatmap10.ax_heatmap.tick_params(labelsize=10))
plt.setp(
heatmap10.ax_heatmap.set_title(
'Methylation Frequency on Plus Strand for Elements', size=12))
sns.despine()
pp.savefig()
# Make heatmap for # methylation on neg strand (Frequency)
heatmap11 = sns.clustermap(XMinus, cmap='RdPu')
ylabels11 = heatmap11.ax_heatmap.get_yticklabels()
plt.setp(heatmap11.ax_heatmap.set_yticklabels(ylabels11, rotation=0))
plt.setp(heatmap11.ax_heatmap.yaxis.tick_right())
plt.setp(
heatmap11.ax_heatmap.set_ylabel('{0} Elements'.format(
len(FreqMinusID.index)),
size=10))
plt.setp(heatmap11.ax_heatmap.set_xlabel('Sample', size=10))
plt.setp(heatmap11.ax_heatmap.tick_params(labelsize=10))
plt.setp(
heatmap11.ax_heatmap.set_title(
'Methylation Frequency on Minus Strand for Elements', size=12))
sns.despine()
pp.savefig()
print 'Plotted methylation frequency for element x tissue type , element'
# Make heatmap for # methylation on pos strand (Frequency)
heatmap12 = sns.clustermap(ranXPlus, cmap='RdPu')
ylabels12 = heatmap12.ax_heatmap.get_yticklabels()
plt.setp(heatmap12.ax_heatmap.set_yticks([]))
plt.setp(heatmap12.ax_heatmap.yaxis.tick_right())
plt.setp(
heatmap12.ax_heatmap.set_ylabel('{0} Elements'.format(
len(ranFreqPlusID.index)),
size=10))
plt.setp(heatmap12.ax_heatmap.set_xlabel('Sample', size=10))
plt.setp(heatmap12.ax_heatmap.tick_params(labelsize=10))
plt.setp(
heatmap12.ax_heatmap.set_title(
'Methylation Frequency on Plus Strand for Random Regions',
size=12))
sns.despine()
pp.savefig()
# Make heatmap for # methylation on neg strand (Frequency)
heatmap13 = sns.clustermap(ranXMinus, cmap='RdPu')
ylabels13 = heatmap13.ax_heatmap.get_yticklabels()
plt.setp(heatmap13.ax_heatmap.set_yticks([]))
plt.setp(heatmap13.ax_heatmap.yaxis.tick_right())
plt.setp(
heatmap13.ax_heatmap.set_ylabel('{0} Elements'.format(
len(ranFreqMinusID.index)),
size=10))
plt.setp(heatmap13.ax_heatmap.set_xlabel('Sample', size=10))
plt.setp(heatmap13.ax_heatmap.tick_params(labelsize=10))
plt.setp(
heatmap13.ax_heatmap.set_title(
'Methylation Frequency on Minus Strand for Random Regions',
size=12))
sns.despine()
pp.savefig()
print 'Plotted methylation frequency for element x position , random regions'
# #put the index in a list
# UCEindex = ATelement.T.index.tolist()
# RANindex = ranATelement.T.index.tolist()
#
# reorder index based on clustering
# ATsorted = [UCEindex[i] for i in ATOrdered]
# RANsorted = [RANindex[i] for i in ranATOrdered]
#
# GraphTableLibrary.main(ATOrdered,ranATOrdered,'Cluster_{0}'.format(fileName))
# print 'Created table for re-ordered mean AT cluster data'
sns.despine()
pp.savefig()
pp.close()