Analysis

compareModelsFeatures(models, param)[source]

Compare the information of different COBRA models including dimentions, essential pathways, main energy sources, etc.

USAGE

[comparisonResults, comparisonTable] = compareModelsFeatures (models, param)

INPUT

  • models – Struct file containing the models to compare

  • param – a structure containing the parameters for the function: * .comparisons -Cell array indicating the type of comparison that

    will be performed (Default: all):

    • ‘compartments’: Check the metabolites on each of the cellular compartments.

    • ‘uniqueElements’: Check the genes, metabolites and reactions present unique for each model.

    • ‘fluxAnalysis’: Uses the selected objective function to predict the flux in different metabolic pathways

    • ‘sparseFBA’: Identify the number of essential reactions per model

    • ‘predictiveCapacity’: Using the selected objective function, test the predictive capacity of the models

    • .objectives -The objective that will be used, includes the

      optimization of reactions or objectives (Default: unWeightedTCBMflux): - ‘unWeighted0norm’ min 0-norm unweighted. - ‘Weighted0normGE’ min 0-norm weighted by the gene expression. - ‘unWeighted1norm’ min 1-norm unweighted - ‘Weighted1normGE’ min 1-norm weighted by the gene expression - ‘unWeighted2norm’ min 2-norm unweighted - ‘Weighted2normGE’ min 2-norm weighted by the gene expression - {‘unWeightedTCBMflux’} unweighted thermodynamic constraint

      based modelling for fluxes

      • ‘unWeightedTCBMfluxConc’ unweighted thermodynamic constraint based modelling for fluxes and concentrations

    • .groups - Uses a common string to group the models together

      (e.g., ‘controlGroup’)

    • .printLevel - Verborese level

    • .trainingSet - Table with the training set. It includes the

      reaction identifier, the reaction name, the measured mean flux, standard deviation of the flux, the flux units, and the platform used to measure it (Required for test ‘flux’).

OUTPUTS

  • comparisonResults – Struct array with all data obtained

  • comparisonTable – Table summarising the analysis

compareXomicsModels(multiModels, printFlag)[source]

Compare generated models in mets, rxns, genes

USAGE

[overlapResults,statistic] = compareXomicsModels (multiModels, printFlag)

INPUT

  • multiModels – struct format with models that need to compare e.g.multimodels.model1

    multimodels.model2 …

  • printFlag – 1 if information should be printed to a table. Default = 0

OUTPUT

  • overlapResults – the overlapped met/rxn/gene numbers of each pair of models

  • statistic – the overlapped porportion matrix of each pair of models

Example

[overlapResults,statistic] = compareXomicsModels(multiModels)

Note

This function is used to compare generated models from xomicsToModel pipeline

Author(s): - Xi Luo

  • Hanneke Leegwater (2022)

coreMetRxnAnalysis(oldModel, model, coreMetAbbr, coreRxnAbbr, deletedMets, deletedRxns, param)[source]

compares the set of core metabolites and reactions with the current model report the core metabolites and reactions removed reduce the set of core metabolites and reactions

USAGE

[coreMetAbbrNew, coreRxnAbbrNew] = coreMetRxnAnalysis (oldModel,model, coreMetAbbr, coreRxnAbbr, deletedMets, deletedRxns, param)

INPUTS

  • oldModel – model prior to adjustment, with the following fields

  • * .mets

  • * .rxns

  • * .metNames

model: new model after adjustments, with the following fields

  • .mets:

  • .rxns:

coreMetAbbr: core metabolite identifiers coreRxnAbbr: core reaction identifiers

OPTIONAL INPUTS

  • deletedMets – set of metabolites removed from oldModel. If empty, given by setdiff(oldModel.mets,model.mets);

  • deletedRxns – set of reactions removed from oldModel. If empty, setdiff(oldModel.rxns,model.rxns);

  • param.message

  • param.printLevel

OUTPUTS

  • coreMetAbbrNew – set of core metabolites in the new model

  • coreRxnAbbrNew – set of core reactions in the new model

EXAMPLE:

NOTE:

Author(s):

debugXomicsToModel(genericModel, directory, modelGenerationReport, coreData)[source]

Examine the debug files generated by the XomicsToModel function for metabolites, reactions, and genes of interest to determine whether or not they were removed during the XomicsToModel process.

USAGE

debugXomicsToModel (model, directory, contextSpecificData)

INPUT

genericModel – The generic input COBRA model used in XomicsToModel

  • .S - Stoichiometric matrix

  • .mets - Metabolite ID vector

  • .rxns - Reaction ID vector

  • .lb - Lower bound vector

  • .ub - Upper bound vector

  • .genes - Upper bound vector

directory: Folder where the debug files are located

metUtilisation(model, met, flux_v, printFig, param)[source]

metUtilisation - a graph analysis of reactions producing and consuming given metabolite

This function can be used to visualise the fluxes of the reactions that either produce or consume certain metabolite. User can choose whether to only study a generic topography or add flux estimation to the graph to visualise the major producers and consumers of a metabolite. Flux can be given either as a single vector, as a pair of vector for a comparison between two different predictions, or as a sampling matrix, in which case a meana and standard deviation will be used to estimate idividual contributions of each reaction to the total metabolite balance

USAGE:

[graph, summary] = metUtilisation(model,mets, flux_v, printFig, param)

INPUT:

model: A generic COBRA model

  • .S - Stoichiometric matrix

  • .mets - Metabolite ID vector

  • .rxns - Reaction ID vector

  • .lb - Lower bound vector

  • .ub - Upper bound vector

met: A string with metabolite ID with or without compartment

information (e.g. ‘atp[c]’ or ‘atp’) as found in model.mets; if no compartment information provided, function will include all compartments for the analysis

OPTIONAL INPUTS:
flux_v: A single flux distribution vector (as obtained from optimisation

algorithm, e.g. FBA) or a matrix (if obtained from flux sampling). Or an array [control_v perturbation_v] containing two flux vectors provided to perform a comparison between two conditions. The first vector is then used as a control, and second as a perturbation to be plotted. Or a matrix containing the results of the flux sampling algorithm. (default = {})

printFig: Logical, whether figure should be printed out (default = 1) param: a structure containg additional parameters for the function

and plotting:

*.treshold_v - flux value below which reaction is

considered active (default = 1e-6)

*.NodeLabels - what value should be plotted as a

node edge: ‘rxns’ for reaction abbreviation or ‘rxnNames’ for a full reaction name (default = ‘rxns’)

*.EdgeLabel.rxns - cell array containg reaction IDs

(in the format of model.rxns)

*.EdgeLabel.text - cell array containg labels to be

ploted on the graph edges (same length as EdgeLabel.rxns)

*.saveFig -logical value, whether figure should be save *.modelName -character, which will be used when save the figure

OUTPUTS:

graph: matlab graph with metabolite as a central node and reactions

producing (left) and consuming (right) metabolite as nodes, if v is provided, the tickness of edges (weight) represents a relative contribution of each reaction to the metabolite utilisation

graph_data: additional data used for plotting (can be used to modify
final graph aesthetics)

*.edgeLabels - labels used to describe edges *.LWidths - scaled widths of edges based on the weights *.graph - matlab graph with metabolite as a central

node and reactions producing (left) and consuming (right) metabolite as nodes, if v is provided, the tickness of edges (weight) represents a relative contribution of each reaction to the metabolite utilisation

*.eColour - colours used for the edges *.nLabels - labels used to describe nodes

summary: table consisting of all reactions (rxns) identified as nodes,

together with their full reaction name (rxnNames), ID in the model (rxnsIDs), stoichimeric coefficient (scoff) representing if metabolite is consumed (-1) or produced (1) by the reaction, reaction formulas, flux values, either as a single flux vector, a pair of vectors used for comparison (flux_v1, flux_v2) or summary statistics from flux sampling data (mean, median, mode, and standard deviation), and in case of the comparison between two flux vectors, labels showing the relative increase, decrease or no change between the two vectors

Author(s): Agnieszka Wegrzyn

Y

plotOverlapResults(overlapresults, statistic, savepath)[source]
USAGE

plot the overlapped heatmap for each model with proportion text labels

Input:

overlapresults: from compareXomicsModels.m statistic: from compareXomicsModels.m savepath (optional): the path to save the plot

Output:

a heat map plot

Author(s):

Xi Luo, 2023/02

use proportion to create map

quanQualAcc(validationData, predictedFlux, param)[source]

param.boundPrecisionLimit