CHRR¶
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chrrExpSampler
(model, numSkip, numSamples, lambda, toRound, roundedPolytope, useFastFVA)[source]¶ Generate random flux samples from an exponential distribution with CHRR Coordinate Hit-and-Run with Rounding. chrrExpSampler will generate numSamples samples from model, taking numSkip steps of a random walk between each sample. Rounding the polytope is a potentially expensive step. If you generate multiple rounds of samples from a single model, you can save roundedPolytope from the first round and input it for subsequent rounds. It is allowed to change lambda without recomputing roundedPolytope.
Usage
[samples, roundedPolytope, minFlux, maxFlux] = chrrExpSampler(model, numSkip, numSamples, lambda, toRound, roundedPolytope, useFastFVA)Inputs
- model –
COBRA model structure with fields:
- .S - The m x n stoichiometric matrix
- .lb - n x 1 lower bounds on fluxes
- .ub - n x 1 upper bounds on fluxes
- .c - n x 1 linear objective
- numSkip – Number of steps of coordinate hit-and-run between samples
- numSamples – Number of samples
Optional inputs
- lambda – the bias vector, i.e. generate samples from exp(-<lambda, x>) restricted to the feasible region. OPTIONAL ONLY IF model.c is nonzero, in which case we set lambda=model.c.
- toRound – {0, 1} Option to round the polytope before sampling.
- roundedPolytope – The rounded polytope from a previous round of sampling the same model.
- useFastFVA – Boolean to use fastFVA (default: false)
Outputs
- samples – n x numSamples matrix of random flux samples
- roundedPolytope – The rounded polytope. Save for use in subsequent rounds of sampling.
- minFlux, maxFlux – flux minima and maxima
- model –
COBRA model structure with fields:
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chrrParseModel
(model)[source]¶ Parse a COBRA model into the right format for the CHRR sampler
Usage
[P,model] = chrrParseModel(model);We are trying to sample uniformly at random from the points v that satisfy:
\[\begin{split}Sv = b\\ ~~ l_b \leq v \leq u_b\end{split}\]Input
- model –
COBRA model structure with fields:
- .S - The m x n stoichiometric matrix
- .lb - n x 1 lower bounds on fluxes
- .ub - n x 1 upper bounds on fluxes
Optional inputs
- * .C - ‘k x n’ matrix of additional inequality constraints
- * .d - ‘k x 1’ rhs of the above constraints
- * .dsense - ‘k x 1’ the sense of the above constraints (‘L’ or ‘G’)
Output
- P –
A structure with fields:
- .A_eq - Equality constraint matrix (model.S)
- .b_eq - Right hand side of equality constraints (model.b)
- .A - Inequality constraint matrix ([I_n 0; 0 -I_n])
- .b - Right hand side of inequality constraints ([lb; -ub])
- model –
COBRA model structure with fields:
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chrrSampler
(model, numSkip, numSamples, toRound, roundedPolytope, useFastFVA, optPercentage)[source]¶ Generate uniform random flux samples with CHRR Coordinate Hit-and-Run with Rounding. chrrSampler will generate numSamples samples from model, taking numSkip steps of a random walk between each sample. Rounding the polytope is a potentially expensive step. If you generate multiple rounds of samples from a single model, you can save roundedPolytope from the first round and input it for subsequent rounds.
Usage
[samples, roundedPolytope] = chrrSampler(model, numSkip, numSamples, toRound, roundedPolytope, useFastFVA)Inputs
- model –
COBRA model structure with fields:
- .S - The m x n stoichiometric matrix
- .lb - n x 1 lower bounds on fluxes
- .ub - n x 1 upper bounds on fluxes
- .c - n x 1 linear objective
- numSkip – Number of steps of coordinate hit-and-run between samples
- numSamples – Number of samples
Optional inputs
- toRound – {0,(1)} Option to round the polytope before sampling.
- roundedPolytope – The rounded polytope from a previous round of sampling the same model.
- useFastFVA – Boolean to use fastFVA (default: false)
- optPercentage – Only consider solutions that give you at least a certain percentage of the optimal solution (Default = 100) Can decrease optPercentage slightly if getting errors.
Outputs
- samples – n x numSamples matrix of random flux samples
- roundedPolytope – The rounded polytope. Save for use in subsequent rounds of sampling.
- minFlux, maxFlux – flux minima and maxima
- model –
COBRA model structure with fields: