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comparison COBRAxy/flux_simulation.xml @ 10:3788bd8c278b draft

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author luca_milaz
date Wed, 18 Sep 2024 12:30:54 +0000
parents 7c01dab3e961
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9:7c01dab3e961 10:3788bd8c278b
97 <help> 97 <help>
98 <![CDATA[ 98 <![CDATA[
99 What it does 99 What it does
100 ------------- 100 -------------
101 101
102 This tool generates flux samples starting from a model in JSON or XML format by using CBS (Corner-based sampling) or OPTGP (mproved Artificial Centering Hit-and-Run sampler) sampling algorithms. 102 This tool generates flux samples starting from a model in JSON or XML format by using CBS (Corner-based sampling) or OPTGP (Improved Artificial Centering Hit-and-Run sampler) sampling algorithms.
103 103
104 It can return sampled fluxes by appliying summary statistics: 104 It can return sampled fluxes by appliying summary statistics:
105 - mean 105 - mean
106 - median 106 - median
107 - quantiles (0.25, 0.50, 0.75). 107 - quantiles (0.25, 0.50, 0.75).
121 The tool generates: 121 The tool generates:
122 - Samples: reporting the sampled fluxes for each reaction (reaction names on the rows and sample names on the columns). Format: csv. 122 - Samples: reporting the sampled fluxes for each reaction (reaction names on the rows and sample names on the columns). Format: csv.
123 - a log file (.txt). 123 - a log file (.txt).
124 124
125 **TIP**: The Batches parameter is useful to mantain in memory just a batch of samples at time. For example, if you wish to sample 10.000 points, than it is suggested to select n_samples = 1.000 and n_batches=10. 125 **TIP**: The Batches parameter is useful to mantain in memory just a batch of samples at time. For example, if you wish to sample 10.000 points, than it is suggested to select n_samples = 1.000 and n_batches=10.
126 126 **TIP**: The Thinning parameter of the OPTGP algorithm is useful to converge to a stationary distribution (see cited articles by Galuzzi, Milazzo and Damiani).
127 127
128 ]]> 128 ]]>
129 </help> 129 </help>
130 <expand macro="citations" /> 130
131 <xml name="citations">
132 <citations>
133 <citation type="bibtex">
134 @article{galuzzi2024adjusting,
135 title={Adjusting for false discoveries in constraint-based differential metabolic flux analysis},
136 author={Galuzzi, Bruno G and Milazzo, Luca and Damiani, Chiara},
137 journal={Journal of Biomedical Informatics},
138 volume={150},
139 pages={104597},
140 year={2024},
141 publisher={Elsevier}
142 }
143 </citation>
144 <citation type="bibtex">
145 @inproceedings{galuzzi2022best,
146 title={Best practices in flux sampling of constrained-based models},
147 author={Galuzzi, Bruno G and Milazzo, Luca and Damiani, Chiara},
148 booktitle={International Conference on Machine Learning, Optimization, and Data Science},
149 pages={234--248},
150 year={2022},
151 organization={Springer}
152 }
153 </citation>
154 <citation type="bibtex">
155 @article{ebrahim2013cobrapy,
156 title={COBRApy: constraints-based reconstruction and analysis for python},
157 author={Ebrahim, Ali and Lerman, Joshua A and Palsson, Bernhard O and Hyduke, Daniel R},
158 journal={BMC systems biology},
159 volume={7},
160 pages={1--6},
161 year={2013},
162 publisher={Springer}
163 }
164 </citation>
165 </citations>
166 </xml>
131 </tool> 167 </tool>