Table 3.
Representative tools for deriving differential co-expression networks. Links to code/tools are provided in Supplementary Table 3.
| Tool | Input data | Algorithmic approach | Example application | Reference |
|---|---|---|---|---|
| DGCA | Two conditions | Correlation | Differential connectivity analysis in myelin dysregulation in a mouse model of Alzheimer’s disease[144] | [145] |
| MINDY | Two conditions | Information theory | Identify modulators of B cell signaling[146] | [147] |
| CINDy | Two conditions | Information theory | Infer candidate upstream modulators of master regulator proteins in various cancer states[148] | [135] |
| Joint Graphical LASSO | Multiple conditions |
Gaussian graphical model | Network analysis of gut microbiome data related to pediatric obesity[149] | [136] |
| Yuan et al. | Multiple conditions | Gaussian graphical model | Identification of genes involved in microsatellite stable colorectal cancers[137] | [137] |
| DICER | Multiple conditions | Correlation | Identification of differentially correlated gene clusters in Alzheimer’s disease[138] | [138], [142] |
| ALPACA | Two conditions | Differential modularity | Identification of network modules associated with glioblastoma survival[150] | [141] |
| Diffcoex | Multiple conditions | Correlation | Investigating altered co-expression patterns following influenza virus infection[151] | [140], [142] |