7 New enrichment analysis

This portion of the system operates similarly to the ‘GO_KEGG_enrichment_analysis’ module, primarily designed to process custom data uploaded by users, which may not be present in the existing database.

7.1 Principles of over-representation enrichment analysis

  1. Let’s assume there are total m genes with Gene ontology annotation in Andrographis paniculata and there are n genes in pathway A.
  2. We have got a gene set G with k genes. Among them, l genes are located in pathway A.
  3. Question: Would genes in gene set G be enriched in pathway A?
User Genes Genome
In Pathway l n-l
Not In Pathway k-l m-k-n+l
  1. A statistical variable odds ratio would be computed:

\(Odds\ ratio = \frac{l * (m-k-n+l)}{(k-l) * (n-l)}\)

  1. Hypothesis: Odds ratio equals 1. A Hypergeometric test would be used to check if the Odds ratio > 1.

7.2 Choose options for each required parameters step by step

Just select each parameter sequentially as labeled in the figure (or simply just click the Demo1 button).

parameter selection

Figure 7.1: parameter selection

Note

1.This section requires you to create a background file on your own, with the file format referenced from the downloadable example. 2.It is recommended to use ‘eggNOG’ for annotation and ‘TBtools’ to generate the background file.

Clicking Submit, after several seconds, one bubble plot and one table are generated below.

7.3 Enrichment result

This part refers to ‘GO_KEGG_enrichment_analysis’ module.