EnrichGT: all-in-one enrichment analysis

Author

Zhiming Ye

Intro

Highlights

EnrichGT means enriching previously enriched results ($E^2_{\text{enrich}}$) into great tables and more …
Quick and fast enrichment analysis
Simple input format, empowering non-pro users
Re-clustering of enriched results provides clear insights
User-friendly HTML outputs / Dynamic Documents
LLM based result annotation
Do a series of things in a single package

Aim and Scope

Please note that the primary goal of EnrichGT is to provide researchers in wet labs or clincal doctors, who have been busy all day, with quick and insightful biological interpretations from dry lab data to support their experiments. So all things in EnrichGT is quick and dirty. Its purpose is to offer a “quick overview”. After this initial overview, you can use more widely recognized tools to generate statistically rigorous results (not necessary though). However, don’t misunderstand—this does not mean that the statistical processes in EnrichGT are incorrect. I’ve made effort to ensure their accuracy; they are just less refined. If you need a tool that handles every detail, please use clusterProfiler or Metascape instead. If you are using clusterProfiler to perform validation and do further research, please see acknowledgment page for citing clusterProfiler.

Install EnrichGT

install.packages("pak")
pak::pkg_install("ZhimingYe/EnrichGT")

The AnnotationDbi, fgsea, reactome.db and GO.db were from BioConductor and might be slow to install. If you can’t install, please re-check your web connections or update your R and BioConductor, or use Posit Package Manager to install when using old R.

Meet EnrichGT

Please see articles on the left! Especially the basic workflow introduction.

Tip

See package function page for further detail params of functions. (For example, how to use a function)

You can use ? function to get further help when installed.

?egt_recluster_analysis

In most examples of this document, we use a result table of DEG analysis DEG.csv. The gene symbols of DEG.csv is on the first column without column name. In default read_csv() convert the unamed column name to ...1. So DEGtable$...1 refers to gene symbols.

Overview

graph LR
    
    M[genes]
    N[genes with weights]
    
    subgraph Enrichment Analysis
        A[egt_enrichment_analysis]
        B[egt_gsea_analysis]
    end

    subgraph Pathway Databases
        D[database_* funcs]
    end

    subgraph Visualize results
        P1[egt_plot_results]
        P2[egt_plot_gsea]
    end

    subgraph egt_recluster_analysis
        K1[Pretty table]
        K2[AI Annotation]
        CC[cluster modules]
        MG[gene modules]
    end

    subgraph Pathway Act. and TF infer 
        
        I[egt_infer]
    end
    
    M --> A
    N --> B
    
    D --> A
    D --> B
    
    A --> C[Enriched Result]
    B --> C

    C --> CC
    C --> MG

    C --> P1

    CC --> K1
    MG --> K1

    CC --> K2
    MG --> K2

    CC --> P1
    C --> P2

    MG --> I

^{The functions from EnrichGT are starting with “egt_” or “database_”.}