TY  - JOUR
A1  - Schwahn, Kevin
A1  - Nikoloski, Zoran
T1  - Data reduction approaches for dissecting transcriptional effects on metabolism
JF  - Frontiers in plant science
N2  - The availability of high-throughput data from transcriptomics and metabolomics technologies provides the opportunity to characterize the transcriptional effects on metabolism. Here we propose and evaluate two computational approaches rooted in data reduction techniques to identify and categorize transcriptional effects on metabolism by combining data on gene expression and metabolite levels. The approaches determine the partial correlation between two metabolite data profiles upon control of given principal components extracted from transcriptomics data profiles. Therefore, they allow us to investigate both data types with all features simultaneously without doing preselection of genes. The proposed approaches allow us to categorize the relation between pairs of metabolites as being under transcriptional or post-transcriptional regulation. The resulting classification is compared to existing literature and accumulated evidence about regulatory mechanism of reactions and pathways in the cases of Escherichia coil, Saccharomycies cerevisiae, and Arabidopsis thaliana.
KW  - E. coil
KW  - S. cerevisiae
KW  - A. thaliana
KW  - partial correlation
KW  - principal component analysis
KW  - metabolomics
KW  - data reduction
KW  - regulation
Y1  - 2018
U6  - https://doi.org/10.3389/fpls.2018.00538
SN  - 1664-462X
VL  - 9
PB  - Frontiers Research Foundation
CY  - Lausanne
ER  -