Created on Tue Jul 12 17:18:19 2016
@author: dan
"""
import pandas as pd
from capacity_usage import CAPACITY_USAGE
import matplotlib.pyplot as plt
import math
import numpy as np
from scipy.stats import pearsonr, spearmanr
flux = pd.DataFrame.from_csv("../data/mmol_gCDW_h.csv")
abundance = pd.DataFrame.from_csv("../data/g_gCDW.csv")
cu = CAPACITY_USAGE(flux, abundance)
def configure_plot(ax, x_label='', y_label='', fontsize=15):
ax.tick_params(right=0, top=0, direction='out', labelsize=fontsize)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.set_xlabel(ax.get_xlabel(), size=15)
ax.set_ylabel(ax.get_ylabel(), size=15)
ax.xaxis.tick_bottom()
ax.yaxis.tick_left()
ax.set_xlabel(x_label, size=fontsize*1.3)
ax.set_ylabel(y_label, size=fontsize*1.3)
ax.set_xscale('log')
ax.set_yscale('log')
def add_labels(x, y, labels, ax, fig, fontsize=10, hide_overlap=True):
import pandas as pd
from capacity_usage import CAPACITY_USAGE
import matplotlib.pyplot as plt
from scipy.stats import pearsonr, spearmanr
import seaborn as sns
from cobra.manipulation.modify import revert_to_reversible
from itertools import product
flux = pd.DataFrame.from_csv("../data/mmol_gCDW_h.csv")
copies_fL = pd.read_csv("../data/abundance[copies_fl].csv")
copies_fL = copies_fL[['bnumber', 'GLC_BATCH_mu=0.58_S']]
abundance = pd.DataFrame.from_csv("../data/g_gCDW.csv")
cu = CAPACITY_USAGE(flux, abundance)
uni_to_b = {row[48:54]:row[0:5].split(';')[0].strip()
for row in open("../data/all_ecoli_genes.txt", 'r')}
id_mapper = pd.DataFrame.from_dict(uni_to_b.items())
id_mapper.columns = ["uniprot", "bnumber"]
TS = pd.read_csv("../data/thermoal_stability_ecoli.csv")
df = TS.merge(id_mapper, on=["uniprot"])
#%%
model = cu.model.copy()
revert_to_reversible(model)
def one2one_mapping():
l = []
for b in cu.model.genes:
l+=(list(product([b.id], list(b.reactions))))
df = pd.DataFrame(l)
df.columns = ['bnumber', 'reaction']
from scipy.stats import ranksums
def despine(ax, fontsize=15):
ax.tick_params(right=0, top=0, direction="out", labelsize=fontsize)
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.set_xlabel(ax.get_xlabel(), size=15)
ax.set_ylabel(ax.get_ylabel(), size=15)
ax.xaxis.tick_bottom()
ax.yaxis.tick_left()
flux = pd.DataFrame.from_csv("../data/mmol_gCDW_h.csv")
abundance = pd.DataFrame.from_csv("../data/g_gCDW.csv")
cu = CAPACITY_USAGE(flux, abundance, shared_reactions=False)
biosyn_mean = []
ccm_mean = []
ranksum_pvalues = {}
dists = []
for c in cu.cs:
x = cu.CU[c]
x.replace(np.inf, np.nan, inplace=True)
x.dropna(inplace=True)
subsystems = pd.Series(index=x.index, data=[cu.rxns[r].subsystem for r in x.index])
for k, v in cu.get_master_groups().iteritems():
subsystems.replace({i: k for i in v}, inplace=True)
s = set(subsystems.values)
l = []
"""
Created on Mon Sep 26 16:21:32 2016
@author: dan
"""
from capacity_usage import CAPACITY_USAGE
import pandas as pd
from itertools import product
import numpy as np
gene_info = pd.DataFrame.from_csv('../data/ecoli_genome_info.csv', sep='\t')
copies_fL = pd.DataFrame.from_csv('../data/copies_fL.csv')
flux = pd.DataFrame.from_csv("../data/mmol_gCDW_h.csv")
abundance = pd.DataFrame.from_csv("../data/g_gCDW.csv")
cu = CAPACITY_USAGE(flux, abundance)
x = pd.DataFrame({r:r.metabolites for r in cu.model.reactions}).T.stack()
r2stoich = x.reset_index()
r2stoich.columns = ['reaction', 'metabolite', 'coefficient']
#%%
l = []
for r,v in cu.reactions_to_isozymes().iteritems():
l+=(list(product([r], v, cu.cs)))
r2isozymes = pd.DataFrame(l)
r2isozymes.columns = ['reaction', 'enzyme', 'condition']
#%%
# include abudance of enzyme complexes - take the minimum abudance
