### Introduction
*Network Coherence Calculator* is an app designed to calculate the network coherence of a set of differentially expressed genes within a biological network. As of version 2.0, the app is also able to accept a file containing expression data for multiple samples as an alternative to a list of genes. The app then offers a rudimentary functionality of computing sample-level gene sets with high expression levels and returns a network coherence for each such set. Network Coherence Calculator is able to use any network loaded into Cytoscape as the biological reference network, provided the nodes and gene sets follow a common naming scheme.
### Use of The Concept
**Network coherence computations have been used in the following publications:**
Sonnenschein, N., Geertz, M., Muskhelishvili, G., & Hütt, M. T. (2011). Analog regulation of metabolic demand. BMC Systems Biology, 5(1), 1-13.
Knecht, C., Fretter, C., Rosenstiel, P., Krawczak, M., & Hütt, M. T. (2016). Distinct metabolic network states manifest in the gene expression profiles of pediatric inflammatory bowel disease patients and controls. Scientific Reports, 6(1), 1-11.
Schlicht, K., Nyczka, P., Caliebe, A., Freitag-Wolf, S., Claringbould, A., Franke, L., ... & Krawczak, M. (2019). The metabolic network coherence of human transcriptomes is associated with genetic variation at the cadherin 18 locus. Human Genetics, 138(4), 375-388.
Kosmidis, K., Jablonski, K. P., Muskhelishvili, G., & Hütt, M. T. (2020). Chromosomal origin of replication coordinates logically distinct types of bacterial genetic regulation. NPJ Systems Biology and Applications, 6(1), 1-9.
Perrin-Cocon, L., Vidalain, P. O., Jacquemin, C., Aublin-Gex, A., Olmstead, K., Panthu, B., ... & Diaz, O. (2021). A hexokinase isoenzyme switch in human liver cancer cells promotes lipogenesis and enhances innate immunity. Communications Biology, 4(1), 1-15.
**Some theoretical work around network coherences is found in:**
Nyczka, P., & Hütt, M. T. (2020). Generative network model of transcriptome patterns in disease cohorts with tunable signal strength. Physical Review Research, 2(3), 033130.
Nyczka, P., Hütt, M. T., & Lesne, A. (2021). Inferring pattern generators on networks. Physica A: Statistical Mechanics and its Applications, 566, 125631.
