Figure 1.
Plaid decomposition of the E. coli data set (see section GC-MS metabolomics data set for a description) implementing aa additive simplivariate model as in Equation 3.
Figure reproduced from [5].
Figure 2.
IDR decomposition of the E. coli data set (see section GC-MS metabolomics data set for a description) implementing a multiplicative simplivariate model as in Equation 4.
Figure reproduced from [5].
Table 1.
Summary of mathematical notation and symbols.
Figure 3.
Dataset decomposition obtained by means of a multiplicative model implemented in the algorithm described in the Methods section.
Black squares indicate that a certain variable belongs to a given simplivariate component (SC). The algorithm is able to retrieve four simplivariate components (referred as SC 1, 2, 3, 4, 5) containing sets of correlated variables.
Table 2.
Summary of statistics parameters for the decomposition of the a simulated data set.
Figure 4.
First eight simplivariate components from the multiplicative simplivariate decomposition of the NMR human urine multiple collection data set.
Results are grouped as much as possible for clarity and non selected metabolites are not shown. See test for details on the biological interpretation.
Table 3.
Summary of statistics parameters for the decomposition of the NMR metabolomics data set.
Figure 5.
First eight simplivariate components from the multiplicative simplivariate decomposition of the GC-MS E. coli data set.
Results are grouped as much as possible for clarity and non selected metabolites are not shown. See text for details on the biological interpretation.
Table 4.
Summary of statistics parameters for the decomposition of the GC-MS metabolomic dat set.