Effects of Sample Size and Dimensionality on the Performance of Four Algorithms for Inference of Association Networks in Metabonomics.
We investigated the effect of sample size and dimensionality on the performance of four algorithms (ARACNE, CLR, CORR, and PCLRC) when they are used for the inference of metabolite association networks. We report that as many as 100-400 samples may be necessary to obtain stable network estimations, depending on the algorithm and the number of measured metabolites. The CLR and PCLRC methods produce similar results, whereas network inference based on correlations provides sparse networks; we found ARACNE to be unsuitable for this application, being unable to recover the underlying metabolite association network. We recommend the PCLRC algorithm for the inference on metabolite association networks.
Laboratory of Systems and Synthetic Biology, Wageningen University and Research Center , Dreijenplein 10, 6703 HB Wageningen, The Netherlands.
Maria
M
Suarez-Diez
Laboratory of Systems and Synthetic Biology, Wageningen University and Research Center , Dreijenplein 10, 6703 HB Wageningen, The Netherlands.
Edoardo
E
Saccenti
D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D006801:Humans; D053858:Metabolic Networks and Pathways; D055432:Metabolomics / Q000379:methods*; D018401:Sample Size; D013548:Sweden; D014427:Twins / Q000706:statistics & numerical data
Low-molecular-weight metabolites; correlations; mutual information; network inference; network topology