Details

Autor(en) / Beteiligte

• Toubiana, David
• Maruenda, Helena

Titel

Guidelines for correlation coefficient threshold settings in metabolite correlation networks exemplified on a potato association panel

Ist Teil von

• BMC bioinformatics, 2021-03, Vol.22 (1), p.116-116, Article 116

Ort / Verlag

England: BioMed Central Ltd

Erscheinungsjahr

2021

Link zum Volltext

Quelle

Electronic Journals Library

Beschreibungen/Notizen

• Correlation network analysis has become an integral tool to study metabolite datasets. Networks are constructed by omitting correlations between metabolites based on two thresholds-namely the r and the associated p-values. While p-value threshold settings follow the rules of multiple hypotheses testing correction, guidelines for r-value threshold settings have not been defined. Here, we introduce a method that allows determining the r-value threshold based on an iterative approach, where different networks are constructed and their network topology is monitored. Once the network topology changes significantly, the threshold is set to the corresponding correlation coefficient value. The approach was exemplified on: (i) a metabolite and morphological trait dataset from a potato association panel, which was grown under normal irrigation and water recovery conditions; and validated (ii) on a metabolite dataset of hearts of fed and fasted mice. For the potato normal irrigation correlation network a threshold of Pearson's |r|≥ 0.23 was suggested, while for the water recovery correlation network a threshold of Pearson's |r|≥ 0.41 was estimated. For both mice networks the threshold was calculated with Pearson's |r|≥ 0.84. Our analysis corrected the previously stated Pearson's correlation coefficient threshold from 0.4 to 0.41 in the water recovery network and from 0.4 to 0.23 for the normal irrigation network. Furthermore, the proposed method suggested a correlation threshold of 0.84 for both mice networks rather than a threshold of 0.7 as applied earlier. We demonstrate that the proposed approach is a valuable tool for constructing biological meaningful networks.