17.1.1.6.1.4. cobra.sampling.sampling
¶
Module implementing flux sampling for cobra models.
17.1.1.6.1.4.1. Module Contents¶
17.1.1.6.1.4.1.1. Functions¶
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Sample valid flux distributions from a cobra model. |
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cobra.sampling.sampling.
sample
(model, n, method='optgp', thinning=100, processes=1, seed=None)[source]¶ Sample valid flux distributions from a cobra model.
The function samples valid flux distributions from a cobra model. Currently we support two methods:
- ‘optgp’ (default) which uses the OptGPSampler that supports parallel
sampling 1. Requires large numbers of samples to be performant (n < 1000). For smaller samples ‘achr’ might be better suited.
or
‘achr’ which uses artificial centering hit-and-run. This is a single process method with good convergence 2.
- Parameters
model (cobra.Model) – The model from which to sample flux distributions.
n (int) – The number of samples to obtain. When using ‘optgp’ this must be a multiple of processes, otherwise a larger number of samples will be returned.
method (str, optional) – The sampling algorithm to use.
thinning (int, optional) – The thinning factor of the generated sampling chain. A thinning of 10 means samples are returned every 10 steps. Defaults to 100 which in benchmarks gives approximately uncorrelated samples. If set to one will return all iterates.
processes (int, optional) – Only used for ‘optgp’. The number of processes used to generate samples.
seed (int > 0, optional) – The random number seed to be used. Initialized to current time stamp if None.
- Returns
The generated flux samples. Each row corresponds to a sample of the fluxes and the columns are the reactions.
- Return type
pandas.DataFrame
Notes
The samplers have a correction method to ensure equality feasibility for long-running chains, however this will only work for homogeneous models, meaning models with no non-zero fixed variables or constraints ( right-hand side of the equalities are zero).
References
- 1
Megchelenbrink W, Huynen M, Marchiori E (2014) optGpSampler: An Improved Tool for Uniformly Sampling the Solution-Space of Genome-Scale Metabolic Networks. PLoS ONE 9(2): e86587.
- 2
Direction Choice for Accelerated Convergence in Hit-and-Run Sampling David E. Kaufman Robert L. Smith Operations Research 199846:1 , 84-95