EnrichGT: all-in-one enrichment analysis

Intro

Highlights

EnrichGT means enriching previously enriched results (\(E^2_{\text{enrich}}\)) into great tables and more …
Efficient C++-based functions for rapid enrichment analysis
Simple input format, empowering non-pro users
Re-clustering of enriched results provides clear and actionable insights
User-friendly HTML output that is easy to read and interpret
Do a series of things just in ONE package

Aim and Scope

Please note that the primary goal of EnrichGT is to provide researchers in wet labs, who have been busy all day, with quick and insightful biological interpretations from dry lab data to support their experiments. Therefore, all computational methods employed are relatively straightforward and pragmatic. For example, C++ based ORA enrichment function, GSEA only using fgsea output without more analysis, use only vocabulary frequency matrix for re-enrichment instead of term’s similarity, and more… 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 every effort to ensure their accuracy; they are just less refined. Additionally, the tool has been tested in most typical scenarios, but extreme cases cannot be entirely ruled out.

This package was developed under the inspiration of clusterProfiler (a wonderful and powerful package! I have learned a lot. Without it I won’t develop EnrichGT.), but none of EnrichGT’s internal functions rely on any clusterProfiler functions. Some database-loading code references clusterProfiler‘s implementation, but it has been extensively modified to provide lightweight experience. In other words, this is not a case of rebranding clusterProfiler under a different name. It’s a lightweight implementation designed to wet-labs. If you need a tool that handles every detail, please use clusterProfiler instead. 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_umap]
    end

    subgraph egt_recluster_analysis
        K1[Pretty table]
        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 --> P1
    CC --> P2

    MG --> I

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