def transformDFByRFunction(self, df, function, library="", *args):
r_df= pandas_df_to_r_df(df)
if library != "":
import_=r.r("library(%s)"%library)
function_=r.r(function)
r_updated_df= function_(r_df, *args)
return r_matrix_to_dataframe(r_updated_df)
def plotCommunityResponse(self, diversity_data, metadata):
title= self.r.initRVariable('title', self.analysis_type)
metadata= metadata.ix[diversity_data.index]
r_diversity_data= pandas_df_to_r_df(diversity_data)
r_dis_matrix= self.r.fPlotCommunityResponse(r_diversity_data)
dis_matrix= r_matrix_to_dataframe(r_dis_matrix)
### build a new dataframe with triclosan values
A={int(b):a for a,b in zip(diversity_data.T ,set(list(dis_matrix['Var1'])))}
new_df= DataFrame(columns= ["Var1","Var2","value","Triclosan1","Triclosan2"], index= dis_matrix.index)
for index in dis_matrix.index:
try:
new_df['Var1'][index]= A[dis_matrix['Var1'][index]]
new_df['Var2'][index]= A[dis_matrix['Var2'][index]]
new_df['Triclosan1'][index]= self.r.df_metadata.ix[new_df['Var1'][index]]['Triclosan']
new_df['Triclosan2'][index]= self.r.df_metadata.ix[new_df['Var2'][index]]['Triclosan']
new_df['value'].loc[index]= dis_matrix['value'].loc[index]
except Exception,e:
print e
continue
def plotNMDS(self, nmds_data, metadata, analysis_type):
r_nmds_data= pandas_df_to_r_df(nmds_data)
r_analysis_type= self.r.initRVariable('analysis_type', analysis_type)
r_nmds= self.r.fMetaMDS(r_nmds_data, r_analysis_type)
r_scores= self.r.fMDSScores(r_nmds)
stress= list(r_nmds.rx('stress')[0])[0]
scores= r_matrix_to_dataframe(r_scores)
plt.figure()
dim1= scores["NMDS1"]
dim2= scores["NMDS2"]
metadata= metadata.ix[nmds_data.index]
c_data = plt.cm.jet(np.log10(metadata))
cm = plt.cm.get_cmap('Greys')
# adding a negligible value to the controls so that logs can be meaningful
metadata[metadata == 0.0] = metadata[metadata == 0.0] +0.1
sc = plt.scatter(dim1,dim2, c= np.log10(metadata), vmin=min(np.log10(metadata)), vmax=max(np.log10(metadata)),s=100, cmap=cm)
cb= plt.colorbar(sc, shrink= 0.6, aspect= 10, pad= 0.1, norm=matplotlib.colors.LogNorm())
cb.ax.tick_params(labelsize='x-small',top=False)
cb.set_label('Triclosan concentration [nM]', labelpad=-70)
labels= np.sort(np.array(list(set(metadata))))
ind = np.log10(labels)
ind_labels= map(str, labels)
ind_labels[0] = "0.0"
cb.set_ticks(ind)
cb.set_ticklabels(ind_labels)
ax= plt.gca()
ax.set_xlabel('NMDS1')
ax.set_ylabel('NMDS2')
ax.set_title('NMDS Ordination of %s OTU Abundance' %self.analysis_type)
left, width = 1.05, .5
bottom, height = 0.05, .5
ax.grid(True, color="gray", alpha= 0.5)
ax.text(left, bottom, 'Stress: %.2g'% stress, fontsize= 11,
horizontalalignment='left', verticalalignment='bottom',
transform= ax.transAxes)# bbox={ 'facecolor':'white','pad':10})
plt.savefig( os.path.join(self.analysis_dir,'nmds.png'))
annotations= map(str, list(metadata))
for label, x, y in zip(annotations, dim1, dim2):
plt.annotate(
label,
xy = (x, y), xytext = (random.randint(-50,-1), random.randint(1,50)),
textcoords = 'offset points', ha = 'right', va = 'bottom',
bbox = dict(boxstyle = 'round,pad=0.8', fc = 'yellow', alpha = 0.2),
arrowprops = dict(arrowstyle = '->', connectionstyle = 'arc3,rad=0'),
fontsize= 10)
#"""
plt.savefig( os.path.join(self.analysis_dir, 'nmds_with_sample_names.png'))
plt.close()
