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This article is about how to understand the application of the hierarchical relationship of GO in enrichment analysis. I think it is very practical, so I share it with you. I hope you can get something after reading this article.

For Gene ontology, there are currently more than 20, 000 Go trems. After enrichment analysis, we may get hundreds or even thousands of enriched GO terms. Such an amount of data is still an arduous task for manual retrieval. In order to make effective use of the results of GO enrichment analysis, we must filter the results again.

The hierarchical relationships of all GO are shown in the following figure

This kind of structure is called directed acyclic graph DAG. Although there is no strict hierarchical relationship between nodes in the data structure of graph, because there are ancestor nodes, that is, the top three nodes in GO graph, the other nodes can be regarded as their child nodes, thus referring to the concept of level in the tree structure. The number of nodes in the path from child nodes to ancestor nodes is defined as the level of that node. The level of ancestor node is 1. The schematic diagram is as follows

It is important to note that since there is more than one path from the child node to the ancestor node, a child node may have multiple level, which means that the level of the GO terms is not a value, so you need to be careful when using level to filter the GO Terms.

Imagine, for a GO term with multiple level, which value we use to characterize its level, whether it is the maximum value, the minimum value, or the mean value. Because of the uncertainty brought by different value algorithms, there is a certain risk to GO filtering by using level, especially when the level is very large. For example, we only select GO terms with level > 7, no matter which value is used as level, the filtering result is not consistent with what we expected.

GO level is also explained on GO's official website, refer to the following link

Http://www.geneontology.org/faq/how-can-i-calculate-level-go-term

Whether the traditional Fisher exact test or GSEA, these enrichment analysis algorithms are only for a single GO term analysis, and will not consider the hierarchical relationship of the GO term in the whole mesh results. For the results of these analyses, when using the above GO level for filtering, you can only use a smaller level. In the following R packet, such as goprofiler, the recommended minimum level is level 2.

The filtering effect of level is limited. In order to filter the results effectively, Gene Ontolgy network analysis appears. The schematic diagram is as follows.

According to all the enriched GO terms, a subgraph subgraph is extracted from the whole GO Graph. The color node in the graph is the enriched GO, the color depth is determined by P value, and the size of the node is determined by degree.

According to this network, the important GO term can be mined by using the algorithm of graph theory, so as to filter the enrichment results of GO.

The above is how to understand the application of GO hierarchical relationship in enrichment analysis. The editor believes that there are some knowledge points that we may see or use in our daily work. I hope you can learn more from this article. For more details, please follow the industry information channel.

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