FIGURE 2: Python Code
class figure2a:
@staticmethod
def execute(**kwargs):
folder = kwargs.get('folder','PATH')
output_folder = kwargs.get('output_folder','PATH')
print('FIGURE2A: main_figure2a_rocMatrix')
figure2a.main_figure2a_rocMatrix(folder = folder, output_folder = output_folder)
@staticmethod
def main_figure2a_rocMatrix(**kwargs):
folder = kwargs.get('folder','PATH')
output_folder = kwargs.get('output_folder','PATH')
print('get_auc_matrix')
auc_matrix = figure2a.get_auc_matrix(folder = folder)
print("plot_auc_matrix")
figure2a.plot_auc_matrix(auc_matrix, output_folder = output_folder)
print('export_underlying_data')
figure2a.export_underlying_data(auc_matrix, folder = folder, output_folder = output_folder)
@staticmethod
def get_auc_matrix(**kwargs):
folder = kwargs.get('folder','PATH')
auc_matrix = DataFrameAnalyzer.open_in_chunks(folder, 'fig2a_auc_matrix.tsv.gz', sep = '\t')
return auc_matrix
@staticmethod
def get_specific_color_gradient(colormap,inputList, **kwargs):
vmin = kwargs.get("vmin", False)
vmax = kwargs.get("vmax", False)
cm = plt.get_cmap(colormap)
if type(inputList)==list:
if vmin == False and vmax == False:
cNorm = mpl.colors.Normalize(vmin=min(inputList), vmax=max(inputList))
else:
cNorm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)
else:
if vmin == False and vmax == False:
cNorm = mpl.colors.Normalize(vmin=inputList.min(), vmax=inputList.max())
else:
cNorm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)
scalarMap = mpl.cm.ScalarMappable(norm=cNorm, cmap=cm)
scalarMap.set_array(inputList)
colorList = scalarMap.to_rgba(inputList)
return scalarMap,colorList
@staticmethod
def plot_auc_matrix(auc_df, output_folder):
datasets = ["tcga_ovarian","battle_protein","colo_cancer","gygi3",
"tcga_breast","gygi1","mann_all_log2","primatePRO","wu",
"battle_ribo","battle_rna","gygi2",'bxd_protein',
"primateRNA","tiannan"]
categories = ["chromosome","housekeeping","essential","pathway",
"compartment","string_700","complex"]
data = auc_df.T
data = data[categories]
data = data.T[datasets].T
data.index = ["TCGA Ovarian Cancer(P)",'Human Individuals(Battle,P)',
'TCGA Colorectal Cancer(P)','DO mouse strains(P)',
"TCGA Breast Cancer(P)",'Founder mouse strains (P)',
'Human cell lines(P)','Primate cells(P)',
'Human Individuals(Wu,P)','Human Individuals(RP)',
'Human Individuals(RS)','DO mouse strains(RS)',
'BXD80 mouse strains(P)','Primate cells(RS)',
'Kidney cancer cells(P)']
data.columns = ['chromosome','housekeeping','essential',
'pathway','compartment','STRING','complex']
sns.set(context='notebook', style='white',
palette='deep', font='Liberation Sans', font_scale=1,
color_codes=False, rc=None)
plt.rcParams["axes.grid"] = False
plt.clf()
x_mean_list = list()
for col in data.columns:
x_mean_list.append(np.mean(utilsFacade.finite(list(data[col])))-0.5)
y_mean_list = list()
for col in data.T.columns:
y_mean_list.append(max(utilsFacade.finite(list(data.T[col])))-0.5)
plt.clf()
fig = plt.figure(figsize = (5,8))
gs = gridspec.GridSpec(16,11)
ax1_density = plt.subplot(gs[0:2,0:8])
ax1_density.set_ylim(0.5,0.8)
ax1_density.axhline(0.55, alpha = 0.6, color="grey", linestyle='--', linewidth = 0.2, zorder=1)
ax1_density.axhline(0.6, alpha = 0.6, color="grey", linestyle='--', linewidth = 0.2, zorder=1)
ax1_density.axhline(0.65, alpha = 0.6, color="grey", linestyle='--', linewidth = 0.2, zorder=1)
ax1_density.axhline(0.7, alpha = 0.6, color="grey", linestyle='--', linewidth = 0.2, zorder=1)
ax1_density.axhline(0.75, alpha = 0.6, color="grey", linestyle='--', linewidth = 0.2, zorder=1)
scalarmap_x, colorList_x = figure2a.get_specific_color_gradient(plt.cm.Greys,
np.array(xrange(len(x_mean_list))))
ax1_density.bar(np.arange(len(x_mean_list)),
x_mean_list, 0.95, color = colorList_x, bottom = 0.5,
edgecolor = "white", linewidth = 2, zorder = 3)
plt.xticks(list(xrange(len(data.columns))))
ax1_density.set_xticklabels([])
ax1_density.set_xlim(0,len(data.columns))
ax = plt.subplot(gs[2:10,0:8])
scalarmap, colorList = figure2a.get_specific_color_gradient(plt.cm.RdBu,
np.array(data), vmin = 0.4, vmax = 0.7)
sns.heatmap(data, cmap = plt.cm.RdBu, vmin = 0.4,vmax = 0.7,
linecolor = "white", linewidth = 2, cbar = False)
y_mean_list = y_mean_list[::-1]
ax2_density = plt.subplot(gs[2:10,8:10])
plt.yticks(list(xrange(len(data.index))))
ax2_density.set_ylim(0,len(data.index))
ax2_density.set_xlim(0.5,0.85)
scalarmap_y, colorList_y = figure2a.get_specific_color_gradient(plt.cm.Greys,
np.array(xrange(len(y_mean_list))))
plt.setp(ax2_density.get_xticklabels(), rotation = 90)
ax2_density.set_yticklabels([])
ax2_density.axvline(0.7, color = "red", linestyle = "--", linewidth = 0.5)
ax2_density.axvline(0.55, alpha = 0.6, color="grey", linestyle='--', linewidth = 0.2, zorder=1)
ax2_density.axvline(0.6, alpha = 0.6, color="grey", linestyle='--', linewidth = 0.2, zorder=1)
ax2_density.axvline(0.65, alpha = 0.6, color="grey", linestyle='--', linewidth = 0.2, zorder=1)
ax2_density.axvline(0.75, alpha = 0.6, color="grey", linestyle='--', linewidth = 0.2, zorder=1)
ax2_density.axvline(0.8, alpha = 0.6, color="grey", linestyle='--', linewidth = 0.2, zorder=1)
ax2_density.barh(np.arange(len(y_mean_list)), y_mean_list, 0.95,
color = colorList_y, left = 0.5, edgecolor = "white", linewidth = 2, zorder = 3)
ax_category = plt.subplot(gs[2:10,10:11])
df = pd.DataFrame({"color":["green"]*7+["lightgreen"]+["magenta"]*2+["green","magenta","green"]})
df = pd.DataFrame({'color':14*[1]})
sns.heatmap(df, cbar = False, linewidth = 2, linecolor = "white")
ax_category.axis("off")
ax_cbar = plt.subplot(gs[13:14,0:8])
cbar = fig.colorbar(scalarmap, cax = ax_cbar, orientation = "horizontal")
cbar.set_label("Area under curve (AUC)")
plt.savefig(output_folder + "fig2a_auc_matrix.pdf", bbox_inches = "tight", dpi=400)
@staticmethod
def export_underlying_data(auc_matrix, **kwargs):
folder = kwargs.get('folder','PATH')
output_folder = kwargs.get('folder','PATH')
category_list = ['chromosome', 'compartment', 'complex',
'housekeeping', 'jiyoye_essentiality',
'k562_essentiality','kbm7_essentiality',
'pathway', 'raji_essentiality',
'string_700']
name_list = ["battle_protein","tiannan","battle_ribo", "battle_rna",
"primateRNA", "primatePRO", "wu","mann_all_log2","yibo",
"gygi1","gygi2","gygi3","tcga_ovarian",'tcga_breast',
'bxd_protein','colo_cancer']
concat_list = list()
names_columns = list()
category_columns = list()
for name in name_list:
for category in category_list:
print(name,category)
file_name = name + '_figure1_data_' + category.upper() + '.tsv.gz'
data = DataFrameAnalyzer.open_in_chunks(folder, file_name)
concat_list.append(data)
for i in xrange(len(data)):
names_columns.append(name)
category_columns.append(category)
data = pd.concat(concat_list)
data['name'] = pd.Series(names_columns, index = data.index)
data['category'] = pd.Series(category_columns, index = data.index)
data.to_csv(output_folder + 'suppTable2_fig2a_underlying_data_ROCanalysis_' + time.strftime('%Y%m%d') +'.tsv',
sep = '\t')
class figure2b:
@staticmethod
def execute(**kwargs):
folder = kwargs.get('folder','PATH')
output_folder = kwargs.get('output_folder','PATH')
print('FIGURE2B: main_figure2b_vignette_complexEffect')
figure2b.main_figure2b_vignette_complexEffect(folder = folder, output_folder = output_folder)
@staticmethod
def main_figure2b_vignette_complexEffect(**kwargs):
folder = kwargs.get('folder','PATH')
output_folder = kwargs.get('output_folder','PATH')
print('plot_complex_effect:tcga_ovarian')
figure2b.plot_complex_effect('tcga_ovarian')
print('plot_complex_effect:battle_protein')
figure2b.plot_complex_effect('battle_protein')
print('plot_complex_effect:gygi1')
figure2b.plot_complex_effect('gygi1')
print('plot_complex_effect:gygi3')
figure2b.plot_complex_effect('gygi3')
print('plot_complex_effect:tcga_breast')
figure2b.plot_complex_effect('tcga_breast')
print('plot_complex_effect:colo_cancer')
figure2b.plot_complex_effect('colo_cancer')
@staticmethod
def get_data(name, **kwargs):
folder = kwargs.get('folder','PATH')
complex_data = DataFrameAnalyzer.open_in_chunks(folder, name + '_figure1_data_COMPLEX.tsv.gz')
return complex_data
@staticmethod
def plot_complex_effect(name, **kwargs):
folder = kwargs.get('folder','PATH')
output_folder = kwargs.get('output_folder','PATH')
complex_data = figure2b.get_data(name, folder = folder)
other_correlations = utilsFacade.finite(list(complex_data[complex_data.label==False]["correlations"]))
complex_correlations = utilsFacade.finite(list(complex_data[complex_data.label==True]["correlations"]))
pval_list1 = list()
for i in xrange(1,1000):
corr1 = random.sample(complex_correlations,100)
corr2 = random.sample(other_correlations,100)
odds1, pval1 = scipy.stats.mannwhitneyu(corr1, corr2)
pval_list1.append(pval1)
print(np.mean(pval_list1))
sns.set(context='notebook', style='white',
palette='deep', font='Liberation Sans', font_scale=1,
color_codes=False, rc=None)
plt.rcParams["axes.grid"] = True
plt.clf()
fig = plt.figure(figsize = (7,5))
ax = fig.add_subplot(111)
ax.set_ylabel('Density', fontsize=12)
ax.set_xlabel('correlation coefficient (pearson)', fontsize=12)
ax.hist(other_correlations, bins = 50, color='grey', alpha =0.3, normed = 1)
plottingFacade.func_plotDensities_border(ax, other_correlations,
linewidth = 2, alpha = 1, facecolor = 'grey')
ax.hist(complex_correlations, bins = 50, color='#AF2D2D', alpha =0.3, normed =1)
plottingFacade.func_plotDensities_border(ax, complex_correlations,
linewidth = 2, alpha = 1, facecolor = '#AF2D2D')
plottingFacade.make_full_legend(ax,['n(other)='+str(len(other_correlations)),
'n(complex)='+str(len(complex_correlations)),
'pvalComplex(Mann-Whitney U)='+str(np.mean(pval_list1))],['grey']*3, loc = 'upper left')
ax.set_xlim(-1,1)
plt.savefig(output_folder + 'fig2b_' + name + '_complex_density_effect.pdf',
bbox_inches = 'tight', dpi = 400)
if __name__ == "__main__":
## EXECUTE FIGURE2
figure2a.execute(folder = sys.argv[1], output_folder = sys.argv[2])
figure2b.execute(folder = sys.argv[1], output_folder = sys.argv[2])
All scripts were developed by Natalie Romanov (Bork group, EMBL). The source code used in the analysis of protein complex variability across individuals is released under the GNU General Public License v3.0. All scripts on this website/web resource is Copyright (C) 2019 Natalie Romanov, Michael Kuhn, Ruedi Aebersold, Alessandro Ori, Martin Beck, Peer Bork and EMBL.