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Parse enrichment results and further clustering and visualizing

EnrichGT.Rd

Cluster enrichment results based on hit genes for ORA (e.g, typical GO enrichment) or core enrichment from GSEA using term frequency analysis. This provides a clearer view of biological relevance by focusing on the genes that matter most.

Gene enrichment analysis can often be misleading due to the redundancy within gene set databases and the limitations of most enrichment tools. Many tools, by default, only display a few top results and fail to filter out redundancy. This can result in both biological misinterpretation and valuable information being overlooked.

For instance, high expression of certain immune genes can cause many immune-related gene sets to appear overrepresented. However, a closer look often reveals that these gene sets are derived from the same group of genes, which might represent only a small fraction. Less than 1/10 of the differentially expressed genes (DEGs). What about the other 9/10? Do they hold no biological significance?

The main purpose of developing this package is to provide a lightweight and practical solution to the problems mentioned above.

Usage

EnrichGT(
  x,
  ClusterNum = 17,
  P.adj = 0.05,
  force = F,
  nTop = 10,
  method = "ward.D2",
  ...
)

Arguments

x

an enrichment result from `clusterProfiler`, or a `data.frame` containing result from `clusterProfier`. To perform fusing multi-database enrichment results, please give a `list` object.

ClusterNum

how many cluster will be clustered

P.adj

p.adjust cut-off. To avoid slow visualization, you can make stricter p-cut off.

force

ignore all auto-self-checks, which is useful

nTop

keep n top items according to p-adj in each cluster.

method

the agglomeration method to be used. This should be (an unambiguous abbreviation of) one of "ward.D", "ward.D2", "single", "complete", "average" (= UPGMA), "mcquitty" (= WPGMA), "median" (= WPGMC) or "centroid" (= UPGMC).

...

Others options.

Value

an `EnrichGT_obj` object.

slot `enriched_result` contains a data.frame with enriched results. `gt_object` contains `gt` object.

you can use `obj@gt_object` to get it and use functions from `gt` like `gtsave`.

`gene_modules` is a list containing meta-gene modules of each cluster.

`pathway_clusters` contains pathways names in each cluster.

`clustering_tree` contains the clustering tree object from `hclust()`, you can use other packages like `ggtree` for further visualization and analysis.

`raw_enriched_result` contains raw table without selecting `nTop`.

Details

For an ORA result, c("ID","Description","GeneRatio","pvalue","p.adjust","geneID","Count") should be contained;

For GSEA, c("ID","Description","NES","pvalue","p.adjust","core_enrichment") should be contain.

For `compareClusterResult`, a `compareClusterResult` object or a data-frame with additional `Cluster` column should be contained, others similar to ORA result.

To perform fusing multi-database enrichment results, please give a `list` object.

Author

Zhiming Ye