Reafect

Reafect uses Z-scores obtained from (untargeted) metabolomics data to calculate 'deficient reaction scores'. These scores indicate whether it is more or less likely that a considered reaction is deficient. Reafect uses KEGG modules and pathways as basis, but reactions can manually be added.

Our article is published in Molecular Genetics and Metabolism:
Michiel Bongaerts and Ramon Bonte and Serwet Demirdas and Hidde H. Huidekoper and Janneke Langendonk and Martina Wilke and Walter de Valk and Henk J. Blom and Marcel J.T. Reinders and George J.G. Ruijter. Integration of metabolomics with genomics: Metabolic gene prioritization using metabolomics data and genomic variant (CADD) scores Molecular Genetics and Metabolism, 2022, 1096-7192 https://doi.org/10.3390/metabo11010008

Content

Databases\

• kegg_enzyme_with_gene_symbol.csv contains (KEGG) associations of enzymes with genes.
• kegg_enzyme_with_reaction.csv contains (KEGG) associations of enzymes with reactions.
• kegg_enzyme_with_name.csv contains (KEGG) EC enzyme numbers with enzyme names.
• Z_scores_IEM_patients_Bongaerts.csv: Z-score data of 72 IEM patient samples used in the publication.
• Z_scores_IEM_patients_Miller.csv: Z-score data from Miller et al. (https://doi.org/10.1007/s10545-015-9843-7). Some adjustments were made to prepare the dataset for Reafect.

Reafect\ Contains all the code used to run Reafect.

Reafect_reproduce_results_publication.ipynb reproduces IEM ranking results as presented in our publication

Manual for running Reafect

First, import the Reafect module.

from Reafect import Reafect
ReafectObj = Reafect(path_to_store_maps = 'Maps/',
              path_to_abc_E = 'Abc_E/',
              path_to_store_results = 'Results/',
              path_to_databases ='Databases/')

Reafect uses metabolic modules and pathways from KEGG. These pathways/modules need to be downloaded first.

hsa_modules = ['M00001'] # etc
ReafectObj.get_modules_kegg(hsa_modules)
               
hsa_maps = ['00010'] # etc
ReafectObj.get_pathways_kegg(hsa_maps)

An investigator can manually add reactions to all pathways/modules as shown in Reafect_reproduce_results_publication.ipynb. After downloading all KEGG modules/pathways, we need to determine the effective Z-scores by searching (reaction) paths between metabolites and reactions in each module/pathway. These effective Z-scores are stored as an symbolic expression (using Sympy) such that this calculation is only done once.

ReafectObj.prepare_pathways()

After determining all effective Z-scores as an symbolic expression, we can substitute the hyper parameters a, b, c for real values. The following method replaces all a, b, c for real values.

ReafectObj.determine_pathways_for_abc_params(a_params= [0.85], b_params = [0.35] ,c_params = [0.75])

In order to determine the deficient reaction scores for a sample, the input needs to have the following format (Pandas dataframe):

network_ID	Z_score
C00026	2.2
C00037	-0.5
..	..
RM_mn	1.25

This dataframe can be passed to the process_patient() method.

SR_scores = ReafectObj.process_patient( patient_ID = 'S01', 
                                        Z_score_data = Z_scores_per_patient, 
                                        a_param= 0.85, b_param = 0.35, c_param = 0.75 # <-- make sure you ran determine_pathways_for_abc_params() first for these values
                                       )

The results are stored in 'Abc_E/'.