2.3 Co‐Expression Network Analysis
Complex traits are influenced by hundreds of SNPs/genes which individually are only able to explain a small amount of variation in the trait. Genes with same or similar biological functions or involved in the same pathway are likely to be co-expressed.
This analysis allows users to identify genes that may be acting collectively to exert a trait. RicePilaf searches rice co-expression networks for modules/communities/clusters genes that are statistically enriched in the GWAS/QTL genes. Functional characterization of the modules are done via enrichment analysis against several ontology and pathway databases from: agriGO, KEGG, Oryzabase.
The screenshot below shows the user interface for the co-expression network analysis.
The intervals provided as input are shown in Box 1. Genes contained in these intervals are automatically included in the co-expression analysis.
Box 2 shows an input box where you can manually add genes (MSU IDs only). These could be genes that you found for example from the pan-genomic lift-over or from text-mining. You can opt to leave this input box empty.
The radio buttons in Box 3 let you can choose between the networks to be used for analysis. Current choices are the co-expression component of RiceNet v2 or RCRN (Box 3).
The radio buttons in Box 4 let you choose among 4 different module-finding algorithms. The slider in Box 5 lets you choose the cluster density parameter of the algorithm above. At the extreme right position, you are likely to find densely connected but smaller-sized modules, whereas at the extreme left position, you are likely to find larger-sized but less dense modules. If you are unsure, we recommend that you choose ClusterONE with clustering parameter = 2.
Click Run Analysis to start the analysis.
Box 1: The result section begins with information on how many of the pre-computed modules (36 in the example in the screenshot) are enriched in your query gene set containing the genes in the Nipponbare intervals and those manually added. The dropdown box contains these modules sorted by ascending p-value. Select an enriched module to explore it.
Box 2: When you select a module from the dropdown, you will see a set of tabs containing its functional characterization via ontology or pathway enrichment. The tab containing Gene Ontology enrichment information is open by default.
Box 3: For every tab selection, you will see a table showing ontology terms or pathways that the module is enriched in along with their statistical significance.
Boxes 4 and 5: The module is displayed in Box 5, depending on the choice on the layout in Box 4. The larger-sized nodes genes are your GWAS/QTL gene. The module display allow user interaction: click nodes for more information, dragging nodes, zooming in/out etc.