Computational protocol: Impact of Prolonged Blood Incubation and Extended Serum Storage at Room Temperature on the Human Serum Metabolome

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Protocol publication

[…] Prior to statistical analysis, the transformation to base 10 logarithms (log10) was done to approximate a normal distribution of the data. The programs R (versions 2.8.1 and 2.14.1, packages nlme and glmnet), SIMCA (version 14.1) and Tibco Spotfire (version 6.0) were used for data analysis and visualization. Statistical analysis was done via a mixed linear model analysis of variance (ANOVA) with “subject” as the random intercept and “group”, “sex”, “age”, and “body mass index” as fixed effects. Restricted maximum likelihood was used to estimate consistent variance components for the linear mixed-effects models []. Significance level was set to 5%. The multiple test problem was addressed by calculating the false discovery rate (FDR) using the Benjamini–Hochberg procedure []. In addition, multivariate analysis was performed by OPLS-DA [,], which is a commonly used method for discovering relationships between metabolomics data and different sample groups such as pre-analytical confounders or controls. To ensure validation of the OPLS-DA model, the cross-validated cumulative Q2 value was used as a measure for the predictive value of this approach. […]

Pipeline specifications

Software tools nlme, lme4
Application Mathematical modeling
Chemicals Amino Acids, Edetic Acid, Taurine