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edkerk merged 1 commit into from
Jun 10, 2026
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Reformat function help blocks to NumPy-style docstrings #616

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59 changes: 34 additions & 25 deletions analysis/FSEOF.m
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@@ -1,33 +1,42 @@
function targets=FSEOF(model,biomassRxn,targetRxn,iterations,coefficient,outputFile)
% FSEOF
% Implements the Flux Scanning based on Enforced Objective Flux algorithm.
% FSEOF Flux Scanning based on Enforced Objective Flux.
%
% Input:
% model a model structure
% biomassRxn string with reaction ID of the biomass formation or
% growth reaction
% targetRxn string with reaction ID of target reaction
% iterations numeric indicating number of iterations (optional,
% default 10)
% coefficient numeric indicating ratio of optimal target reaction
% flux, must be less than 1 (optional, default 0.9)
% outputFile string with output filename (optional, default prints
% to command window)
% Implements the Flux Scanning based on Enforced Objective Flux algorithm.
% This function writes a tab-delimited file or prints to the command
% window. If an output has been specified (targets), it will also generate
% a structure indicating for each model reaction whether it is identified
% by FSEOF as a target and the slope of the reaction when switching from
% biomass formation to product formation.
%
% Output:
% targets structure with information for identified targets
% logical logical array indicating whether a model reaction was
% identified as target by FSEOF
% slope numeric array with FSEOF slopes for target reactions
% Parameters
% ----------
% model : struct
% a model structure.
% biomassRxn : char
% reaction ID of the biomass formation or growth reaction.
% targetRxn : char
% reaction ID of the target reaction.
% iterations : double, optional
% number of iterations (default 10).
% coefficient : double, optional
% ratio of optimal target reaction flux, must be less than 1
% (default 0.9).
% outputFile : char, optional
% output filename (default prints to command window).
%
% This function writes an tab-delimited file or prints to command window.
% If an output has been specified (targets), it will also generate a
% structure indicating for each model reaction whether it is identified by
% FSEOF as a target and the slope of the reaction when switching from
% biomass formation to product formation.
% Returns
% -------
% targets : struct
% structure with information for identified targets, with fields:
%
% - logical : logical array indicating whether a model reaction was
% identified as target by FSEOF
% - slope : numeric array with FSEOF slopes for target reactions
%
% Usage: targets = FSEOF(model, biomassRxn, targetRxn, iterations,...
% coefficient, outputFile)
% Examples
% --------
% targets = FSEOF(model, biomassRxn, targetRxn, iterations, ...
% coefficient, outputFile);

biomassRxn=char(biomassRxn);
targetRxn=char(targetRxn);
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66 changes: 40 additions & 26 deletions analysis/analyzeSampling.m
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function scores=analyzeSampling(Tex, df, solutionsA, solutionsB, printResults)
% analyzeSampling
% Compares the significance of change in flux between two conditions with
% the significance of change in gene expression
% analyzeSampling Compare flux change significance with expression change.
%
% Tex a vector of t-scores for the change in gene expression
% for each reaction. This score could be the Student t
% between the two conditions, or you can calculate it from
% a p-value (by computing the inverse of the so called error
% function). If you choose the second alternative you should
% be aware that the transcripts that increased in expression
% level should have positive values and those who decreased
% in expression level should have negative values (the
% p-values only tell you if the fluxes changed or not but
% not in which direction)
% df the degrees of freedom in the t-test
% solutionsA random solutions for the reference condition (as
% generated by randomSampling)
% solutionsB random solutions for the test condition (as generated
% by randomSampling)
% printResults prints the most significant reactions in each category
% (optional, default false)
% Compares the significance of change in flux between two conditions with
% the significance of change in gene expression.
%
% scores a Nx3 column matrix with the probabilities of a reaction:
% 1) changing both in flux and expression in the same direction
% 2) changing in expression but not in flux
% 3) changing in flux but not in expression or changing
% in opposed directions in flux and expression.
% Parameters
% ----------
% Tex : double
% a vector of t-scores for the change in gene expression for each
% reaction. This score could be the Student t between the two
% conditions, or you can calculate it from a p-value (by computing the
% inverse of the so called error function). If you choose the second
% alternative you should be aware that the transcripts that increased
% in expression level should have positive values and those who
% decreased in expression level should have negative values (the
% p-values only tell you if the fluxes changed or not but not in which
% direction).
% df : double
% the degrees of freedom in the t-test.
% solutionsA : double
% random solutions for the reference condition (as generated by
% randomSampling).
% solutionsB : double
% random solutions for the test condition (as generated by
% randomSampling).
% printResults : logical, optional
% prints the most significant reactions in each category
% (default false).
%
% Usage: scores=analyzeSampling(Tex, df, solutionsA, solutionsB, printResults)
% Returns
% -------
% scores : double
% a Nx3 column matrix with the probabilities of a reaction:
%
% 1. changing both in flux and expression in the same direction
% 2. changing in expression but not in flux
% 3. changing in flux but not in expression or changing in opposed
% directions in flux and expression
%
% Examples
% --------
% scores = analyzeSampling(Tex, df, solutionsA, solutionsB, ...
% printResults);

if nargin<5
printResults=false;
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109 changes: 62 additions & 47 deletions analysis/findGeneDeletions.m
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@@ -1,54 +1,69 @@
function [genes, fluxes, originalGenes, details, grRatioMuts]=findGeneDeletions(model,testType,analysisType,refModel,oeFactor)
% findGeneDeletions
% Deletes genes, optimizes the model, and keeps track of the resulting
% fluxes. This is used for identifying gene deletion targets.
% findGeneDeletions Delete genes and track the resulting fluxes.
%
% model a model structure
% testType single/double gene deletions/over expressions. Over
% expression only available if using MOMA
% 'sgd' single gene deletion
% 'dgd' double gene deletion
% 'sgo' single gene over expression
% 'dgo' double gene over expression
% (optional, default 'sgd')
% analysisType determines whether to use FBA ('fba') or MOMA ('moma')
% in the optimization. (optional, default 'fba')
% refModel MOMA works by fitting the flux distributions of two
% models to be as similar as possible. The most common
% application is where you have a reference model where
% some of the fluxes are constrained from experimental
% data. This model is required when using MOMA
% oeFactor a factor by which the fluxes should be increased if a
% gene is overexpressed (optional, default 10)
% Deletes genes, optimizes the model, and keeps track of the resulting
% fluxes. This is used for identifying gene deletion targets.
%
% genes a matrix with the genes that were deleted in each
% optimization (the gene indexes in originalGenes). Each
% row corresponds to a column in fluxes
% fluxes a matrix with the resulting fluxes. Double deletions
% that result in an unsolvable problem have all zero
% flux. Single deletions that result in an unsolvable
% problem are indicated in details instead
% originalGenes simply the genes in the input model. Included for
% simple presentation of the output
% details not all genes will be deleted in all analyses. It is
% for example not necessary to delete genes for dead end
% reactions. This is a vector with details about
% each gene in originalGenes and why or why not it was
% deleted
% 1: Was deleted/overexpressed
% 2: Proved lethal in sgd (single gene deletion)
% 3: - redundant, no longer used -
% 4: Involved in dead-end reaction
% grRatioMuts growth rate ratio between mutated strain and wild type,
% matches the originalGenes(genes) mutants. Note that
% this does not directly map to model.genes, as is the case
% for COBRA getEssentialGenes. However, this can be
% obtained by afterwards running:
% grRatio=zeros(1,numel(model.genes));
% grRatio(genes)=grRatioMuts;
% Parameters
% ----------
% model : struct
% a model structure.
% testType : char, optional
% single/double gene deletions/over expressions. Over expression is
% only available if using MOMA (default 'sgd'):
%
% Usage: [genes, fluxes, originalGenes, details, grRatioMuts]=findGeneDeletions(model,testType,analysisType,...
% refModel,oeFactor)
% - 'sgd' : single gene deletion
% - 'dgd' : double gene deletion
% - 'sgo' : single gene over expression
% - 'dgo' : double gene over expression
% analysisType : char, optional
% determines whether to use FBA ('fba') or MOMA ('moma') in the
% optimization (default 'fba').
% refModel : struct, optional
% MOMA works by fitting the flux distributions of two models to be as
% similar as possible. The most common application is where there is a
% reference model with some fluxes constrained from experimental data.
% This model is required when using MOMA.
% oeFactor : double, optional
% a factor by which the fluxes should be increased if a gene is
% overexpressed (default 10).
%
% Returns
% -------
% genes : double
% a matrix with the genes that were deleted in each optimization (the
% gene indexes in originalGenes). Each row corresponds to a column in
% fluxes.
% fluxes : double
% a matrix with the resulting fluxes. Double deletions that result in
% an unsolvable problem have all zero flux. Single deletions that
% result in an unsolvable problem are indicated in details instead.
% originalGenes : cell
% simply the genes in the input model. Included for simple
% presentation of the output.
% details : double
% not all genes will be deleted in all analyses. It is for example not
% necessary to delete genes for dead end reactions. This is a vector
% with details about each gene in originalGenes and why or why not it
% was deleted:
%
% - 1 : was deleted/overexpressed
% - 2 : proved lethal in sgd (single gene deletion)
% - 3 : redundant, no longer used
% - 4 : involved in dead-end reaction
% grRatioMuts : double
% growth rate ratio between mutated strain and wild type, matching the
% originalGenes(genes) mutants. Note that this does not directly map
% to model.genes, as is the case for COBRA getEssentialGenes. However,
% this can be obtained by afterwards running:
%
% grRatio=zeros(1,numel(model.genes));
% grRatio(genes)=grRatioMuts;
%
% Examples
% --------
% [genes, fluxes, originalGenes, details, grRatioMuts]=...
% findGeneDeletions(model,testType,analysisType,refModel,oeFactor);

originalModel=model;
if nargin<5
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46 changes: 28 additions & 18 deletions analysis/followChanged.m
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function followChanged(model,fluxesA,fluxesB, cutOffChange, cutOffFlux, cutOffDiff, metaboliteList)
% followChanged
% Prints fluxes and reactions for each of the reactions that results in
% different fluxes compared to the reference case.
% followChanged Print reactions whose fluxes differ from a reference case.
%
% model a model structure
% fluxesA flux vector for the test case
% fluxesB flux vector for the reference test
% cutOffChange reactions where the fluxes differ by less than
% this many percent won't be printed (optional, default 10^-8)
% cutOffFlux reactions where the absolute value of both fluxes
% are below this value won't be printed (optional,
% default 10^-8)
% cutOffDiff reactions where the fluxes differ by less than
% cutOffDiff won't be printed (optional, default 10^-8)
% metaboliteList cell array of metabolite names. Only reactions
% involving any of these metabolites will be
% printed (optional)
% Prints fluxes and reactions for each of the reactions that result in
% different fluxes compared to the reference case.
%
% Usage: followChanged(model,fluxesA,fluxesB, cutOffChange, cutOffFlux,
% cutOffDiff, metaboliteList)
% Parameters
% ----------
% model : struct
% a model structure.
% fluxesA : double
% flux vector for the test case.
% fluxesB : double
% flux vector for the reference test.
% cutOffChange : double, optional
% reactions where the fluxes differ by less than this many percent
% won't be printed (default 10^-8).
% cutOffFlux : double, optional
% reactions where the absolute value of both fluxes are below this
% value won't be printed (default 10^-8).
% cutOffDiff : double, optional
% reactions where the fluxes differ by less than cutOffDiff won't be
% printed (default 10^-8).
% metaboliteList : cell, optional
% cell array of metabolite names. Only reactions involving any of
% these metabolites will be printed.
%
% Examples
% --------
% followChanged(model,fluxesA,fluxesB,cutOffChange,cutOffFlux,...
% cutOffDiff,metaboliteList);

%Checks if a cut off flux has been set
if nargin<4
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37 changes: 25 additions & 12 deletions analysis/followFluxes.m
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function errorFlag=followFluxes(model, fluxesA, lowerFlux, upperFlux, fluxesB)
% followFluxes
% Prints fluxes and reactions for each of the reactions that results in
% fluxes in the specified interval.
% followFluxes Print reactions with fluxes in a specified interval.
%
% model a model structure
% fluxesA flux vector for the test case
% lowerFlux only reactions with fluxes above this cutoff
% value are displayed
% upperFlux only reactions with fluxes below this cutoff
% value are displayed (optional, default Inf)
% fluxesB flux vector for the reference case(optional)
% Prints fluxes and reactions for each of the reactions that result in
% fluxes within the specified interval.
%
% Usage: errorFlag=followFluxes(model, fluxesA, lowerFlux, upperFlux,
% fluxesB)
% Parameters
% ----------
% model : struct
% a model structure.
% fluxesA : double
% flux vector for the test case.
% lowerFlux : double
% only reactions with fluxes above this cutoff value are displayed.
% upperFlux : double, optional
% only reactions with fluxes below this cutoff value are displayed
% (default Inf).
% fluxesB : double, optional
% flux vector for the reference case.
%
% Returns
% -------
% errorFlag : double
% set to 1 if upperFlux is not larger than lowerFlux, otherwise empty.
%
% Examples
% --------
% errorFlag=followFluxes(model,fluxesA,lowerFlux,upperFlux,fluxesB);

%Checks that the upper flux is larger than the lower flux
if nargin>3
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26 changes: 16 additions & 10 deletions analysis/getAllSubGraphs.m
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function subGraphs=getAllSubGraphs(model)
% getAllSubGraphs
% Get all metabolic subgraphs in a model. Two metabolites
% are connected if they share a reaction.
% getAllSubGraphs Get all metabolic subgraphs in a model.
%
% Input:
% model a model structure
% Two metabolites are connected if they share a reaction.
%
% Output:
% subGraphs a boolean matrix where the rows correspond to the metabolites
% and the columns to which subgraph they are assigned to. The
% columns are ordered so that larger subgraphs come first
% Parameters
% ----------
% model : struct
% a model structure.
%
% Usage: subGraphs=getAllSubGraphs(model)
% Returns
% -------
% subGraphs : logical
% a boolean matrix where the rows correspond to the metabolites and
% the columns to which subgraph they are assigned to. The columns are
% ordered so that larger subgraphs come first.
%
% Examples
% --------
% subGraphs = getAllSubGraphs(model);

%Generate the connectivity graph. Metabolites are connected through
%reactions. This is not a bipartite graph with the reactions.
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