Sampling

• ACHR
• CHRR

calcSampleDifference(sample1, sample2, nPts)

Selects randomly nPts flux vectors from sample1 and sample2 and calcutes the difference between the flux vectors

USAGE:

[sampleDiff, sampleRatio] = calcSampleDifference(sample1, sample2, nPts)

INPUTS:

sample1: First flux sample sample2: Second flux sample

OPTIONAL INPUTS:

nPts: Number of flux difference profiles desired (default:

10% of the samples)

OUTPUTS:

sampleDiff: Difference between the flux vectors sampleRatio: Ratio of the flux vectors

Example

example 1: [sampleDiff, sampleRatio] = calcSampleDifference(sample1, sample2) example 2: [sampleDiff, sampleRatio] = calcSampleDifference(sample1, sample2, 10)

calcSampleStats(samples)

Calculate sample modes, means, standard devs, and medians of the sample

USAGE:

sampleStats = calcSampleStats(samples)

INPUT:

samples: Samples to analyze

OUTPUT:

sampleStats: Structure with the following fields:

• mean

• std

• mode

• median

• skew

• kurt

Example

example 1: sampleStats = calcSampleStats(sample) example 2: sampleStats = calcSampleStats({sample1, sample2})

compareSampleTraj(rxnNames, samples, models, nBins)

Compares flux histograms for two or more samples for one or more reactions

USAGE:

compareSampleTraj(rxnNames, samples, models, nBins)

INPUTS:

rxnNames: List of reaction names to compare samples: Samples to compare models: Cell array containing COBRA model structures

OPTIONAL INPUTS:

nBins: Number of bins (Default = nSamples / 25)

compareTwoSamplesStat(sample1, sample2, tests)

Compares statistically the difference between two samples. Does the Kolmogorov-Smirnov, rank-sum, chi-square, and T-tests.

USAGE:

[stats, pVals] = compareTwoSamplesStat(sample1, sample2, tests)

INPUTS:

sample1, sample2: Samples to compare tests: {test1, test2,…} (Default = all tests)

• ‘ks’ - Kolmogorov-Smirnov test

• ‘rankSum’ - rank-sum test

• ‘chiSquare’ - chi-squre test

• ‘tTest’ - T-test

OUTPUTS:

stats: Struct array with statistics of the selected

tests.

pVals: Struct array with p values of the selected

tests.

Example

example 1: [stats, pVals] = compareTwoSamplesStat(sample1, sample2) example 2: [stats, pVals] = compareTwoSamplesStat(sample1, sample2, {‘ks’, ‘rankSum’})

Output will be in order that tests are inputed. i.e. {‘ks’,’rankSum’}

identifyCorrelSets(model, sample, corrThr, R)

Identifies correlated reaction sets from sampling data

USAGE:

[sets, setNumber, setSize] = identifyCorrelSets(model, sample, corrThr, R)

INPUTS:

model: COBRA model structure sample: Sample to be used to identify correlated sets

OPTIONAL INPUTS:

corrThr: Minimum correlation (\(R^2\)) threshold (Default = 1-1e-8) R: Correlation coefficient

OUTPUTS:

sets: Sorted cell array of sets (largest first) setNumber: List of set numbers for each reaction in model (0 indicates

that there is no set)

setSize: List of set sizes

loadSamples(filename, numFiles, pointsPerFile, numSkipped, randPts)

Loads a set of sampled data points

USAGE:

samples = loadSamples(filename, numFiles, pointsPerFile, numSkipped, randPts)

INPUTS:

filename: The name of the files containing the sample points. numFiles: The number of files containing the sample points. pointsPerFile: The number of points to be taken from each file.

OPTIONAL INPUTS:

numSkipped: Number of files skipped (default = 0) randPts: Select random points from each file (true/false, default = false)

OUTPUT:

samples: Sample flux distributions

plotHistConv(model, sample, rxnNames, nSubSamples)

Plots convergence of sample histograms

USAGE:

plotHistConv(model, sample, rxnNames, nSubSamples)

INPUTS:

model: COBRA model structure sample: Sampled fluxes rxnNames: List of reactions to plot nSubSamples: Number of sub samples

Example

example 1: rxnNames = {‘R1’, ‘R2’} plotHistConv(model, sample, rxnNames, nSubSamples)

plotSampleHist(rxnNames, samples, models, nBins, perScreen, modelNames, add2Plot)

Compares flux histograms for one or more samples for one or more reactions

USAGE:

plotSampleHist(rxnNames, samples, models, nBins, perScreen, modelNames, add2Plot)

INPUTS:

rxnNames: Cell array of reaction abbreviations samples: Cell array containing samples models: Cell array containing model structures or common model

structure

OPTIONAL INPUTS:

nBins: Number of bins to be used perScreen: Number of reactions to show per screen. Either a number or [nY, nX] vector.

(press ‘enter’ to advance screens)

modelNames: Cell array containing the name of the models (used for the

plot’s legend).

add2Plot: Struct array with additional data to show more

detaled information (real measuremets, FVA resuts, statistics results, etc).

Example

sampleStructOut1 = gpSampler(model1, 2150); sampleStructOut2 = gpSampler(model2, 2150); %Plot for model 1 plotSampleHist(model1.rxns,{samplePoints1},{model1})

%Plot reactions reactions in model 1 that also exist in model 2 using 10 %bins and plotting 12 reactions per screen. plotSampleHist(model1.rxns,{samplePoints1,samplePoints2},{model1,model2},10,12)

CONTROLS: To advance to next screen hit ‘enter/return’ or type ‘f’ and hit ‘enter/return’ To rewind to previous screen type ‘r’ or ‘b’ and hit ‘enter/return’ To quit script type ‘q’ and hit ‘enter/return’

printSampleStats(sampledModel, commonModel, sampleNames, fileName)

Prints out sample statistics for multiple samples

USAGE:

printSampleStats(samples, commonModel, sampleNames, fileName)

INPUTS:

sampledModel: Samples to plot commonModel: COBRA model structure sampleNames: Names of the models

OPTIONAL INPUT:

fileName: Name of tab delimited CSV file to generate

(Default = print to command window)

sampleCbModel(model, sampleFile, samplerName, options, modelSampling)

Samples the solution-space of a constraint-based model

USAGE:

[modelSampling, samples] = sampleCbModel(model, sampleFile, samplerName, options, modelSampling)

INPUTS:

model: COBRA model structure with fields

• .S - Stoichiometric matrix

• .b - Right hand side vector

• .lb - ‘n x 1’ vector: Lower bounds

• .ub - ‘n x 1’ vector: Upper bounds

• .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’)

• .vMean - ‘n x 1’ vector: the mean for Gaussian sampling (RHMC only)

• .vCov - ‘n x 1’ vector: the diagonal for the covariance for Gaussian sampling (RHMC only)

OPTIONAL INPUTS:

sampleFile: File names for sampling output files (only implemented for ACHR) samplerName: {(‘CHRR’), ‘ACHR’, ‘RHMC’} Name of the sampler to be used to

sample the solution.

options: Options for sampling and pre/postprocessing (default values

in parenthesis).

• .nStepsPerPoint - Number of sampler steps per point saved (200)

• .nPointsReturned - Number of points loaded for analysis (2000)

• .nWarmupPoints - Number of warmup points (5000). ACHR only.

• .nFiles - Number of output files (10). ACHR only.

• .nPointsPerFile - Number of points per file (1000). ACHR only.

• .nFilesSkipped - Number of output files skipped when loading points to avoid potentially biased initial samples (2) loops (true). ACHR only.

• .maxTime - Maximum time limit (Default = 36000 s). ACHR only.

• .toRound - Option to round the model before sampling (true). CHRR only.

• .lambda - the bias vector for exponential sampling. CHRR_EXP only.

• .nWorkers - Number of parallel workers. RHMC only.

modelSampling: From a previous round of sampling the same

model. Input to avoid repeated preprocessing.

OUTPUTS:

modelSampling: Cleaned up model used in sampling samples: n x numSamples matrix of flux vectors

Examples

%1) Sample a model called ‘superModel’ using default settings and save the % results in files with the common beginning ‘superModelSamples’

[modelSampling,samples] = sampleCbModel(superModel,’superModelSamples’);

%2) Sample a model called ‘hyperModel’ using default settings except with a total of 50 sample files % saved and with 5000 sample points returned.

options.nFiles = 50; options.nPointsReturned = 5000; [modelSampling,samples] = sampleCbModel(hyperModel,’options’,options);

sampleScatterMatrix(rxnNames, model, sample, nPoints, fontSize, dispRFlag, rxnNames2)

Draws a scatterplot matrix with pairwise scatterplots for multiple reactions

USAGE:

sampleScatterMatrix(rxnNames, model, sample, nPoints, fontSize, dispRFlag, rxnNames2)

INPUTS:

rxnNames: Cell array of reaction names to be plotted model: Model structure sample: Samples to be analyzed (nRxns x nSamples)

OPTIONAL INPUTS:

nPoints: How many sample points to plot (Default nSamples) fontSize: Font size for labels (Default calculated based on

number of reactions)

dispRFlag: Display correlation coefficients (Default false) rxnNames2: Optional second set of reaction names

Example

%Plots the scatterplots only between the three reactions listed - %histograms for each reaction will be on the diagonal sampleScatterMatrix({‘PFK’, ‘PYK’, ‘PGL’}, model, sample); %Plots the scatterplots between each of the first set of reactions and %each of the second set of reactions. No histograms will be shown. sampleScatterMatrix({‘PFK’, ‘PYK’, ‘PGL’}, model, sample, 100, 10, true, {‘ENO’,’TPI’});