4. Model extraction methods¶

PipeGEM inplements several model extraction methods for genomics/transcriptomics data integration, including:

MBA-like algorithms

• rFASTCORMICS
• CORDA
• FASTCORE
• mCADRE
• MBA

iMAT-like algorithms

• INIT
• iMAT

GIMME-like algorithms

• RIPTiDe
• GIMME (Becker, Scott A., and Bernhard O. Palsson. 2008)

EFlux-like algorithms

• E-Flux (Colijn, Caroline, et al. 2009)
• SPOT (in the next version)

In this session, we will showcase two of them, namely rFASTCORMICS and GIMME. Other methods and more details can be found in tutorial/data_integration.

In [2]:

import numpy as np
import seaborn as sns
import pipeGEM as pg

from pipeGEM.data import GeneData, get_syn_gene_data
from pipeGEM import load_remote_model

import numpy as np import seaborn as sns import pipeGEM as pg from pipeGEM.data import GeneData, get_syn_gene_data from pipeGEM import load_remote_model

In [6]:

# load the consistent template model saved in the previous session
human1 = pg.Model("human", 
                  load_remote_model("Human-GEM", format="mat"))

# load the consistent template model saved in the previous session human1 = pg.Model("human", load_remote_model("Human-GEM", format="mat"))

Model Human-GEM is already downloaded
Set parameter Username
Academic license - for non-commercial use only - expires 2024-02-28

In [ ]:

# load gene data
data = np.log2(get_syn_gene_data(human1, n_sample=3) + 1)

data_name = "sample_0"
gene_data = GeneData(data=data[data_name], 
                     data_transform=lambda x: np.log2(x), 
                     absent_expression=0)
human1.add_gene_data(data_name, gene_data)

# load gene data data = np.log2(get_syn_gene_data(human1, n_sample=3) + 1) data_name = "sample_0" gene_data = GeneData(data=data[data_name], data_transform=lambda x: np.log2(x), absent_expression=0) human1.add_gene_data(data_name, gene_data)

In [ ]:

# get thresholds using the data
rFASTCORMICS_th = gene_data.get_threshold("rFASTCORMICS")

exp_th, nexp_th = rFASTCORMICS_th.exp_th, rFASTCORMICS_th.non_exp_th

# get thresholds using the data rFASTCORMICS_th = gene_data.get_threshold("rFASTCORMICS") exp_th, nexp_th = rFASTCORMICS_th.exp_th, rFASTCORMICS_th.non_exp_th

In [ ]:

rFASTCORMICS_th.plot()

rFASTCORMICS_th.plot()

In [ ]:

# load task analysis result (optional)
from pipeGEM.analysis import TaskAnalysis

# task_analysis_result = TaskAnalysis.load("")

# get supporting reactions
# task_supps = human1.get_activated_task_sup_rxns(data_name=data_name, 
#                                                task_analysis=task_analysis_result, 
#                                                score_threshold=exp_th)

task_supps = []

spon_rxns = ['MAR04740', 'MAR04250', 'MAR06875', 'MAR06876', 'MAR04840', 'MAR04771', 
             'MAR06997', 'MAR07008', 'MAR07011', 'MAR07015', 'MAR07016', 'MAR05127', 'MAR08749', 'MAR08750']

# load task analysis result (optional) from pipeGEM.analysis import TaskAnalysis # task_analysis_result = TaskAnalysis.load("") # get supporting reactions # task_supps = human1.get_activated_task_sup_rxns(data_name=data_name, # task_analysis=task_analysis_result, # score_threshold=exp_th) task_supps = [] spon_rxns = ['MAR04740', 'MAR04250', 'MAR06875', 'MAR06876', 'MAR04840', 'MAR04771', 'MAR06997', 'MAR07008', 'MAR07011', 'MAR07015', 'MAR07016', 'MAR05127', 'MAR08749', 'MAR08750']

rFASTCORMICS¶

In [ ]:

result = human1.integrate_gene_data(data_name, 
                                    integrator="rFASTCORMICS", 
                                    protected_rxns=list(set(task_supps+spon_rxns)), 
                                    consistent_checking_method=None,
                                    predefined_threshold={"exp_th": exp_th, "non_exp_th": nexp_th})

# result model
print(result.result_model)

result = human1.integrate_gene_data(data_name, integrator="rFASTCORMICS", protected_rxns=list(set(task_supps+spon_rxns)), consistent_checking_method=None, predefined_threshold={"exp_th": exp_th, "non_exp_th": nexp_th}) # result model print(result.result_model)

And... that's it. You can also save the result with result.save("saved_folder").

Some of the parameters are reusable for other integration methods.

GIMME¶

In [ ]:

result = human1.integrate_gene_data(data_name, 
                                    integrator="GIMME", 
                                    remove_zero_fluxes=True,  # If True, create a result_model. default = False
                                    protected_rxns=list(set(task_supps+spon_rxns)), 
                                    high_exp=exp_th)

# print out the result model
print(result.result_model)

result = human1.integrate_gene_data(data_name, integrator="GIMME", remove_zero_fluxes=True, # If True, create a result_model. default = False protected_rxns=list(set(task_supps+spon_rxns)), high_exp=exp_th) # print out the result model print(result.result_model)