# Functional analysis of mouse mammary gland RNA-Seq using fgsea instead of topGO

Aurelien Brionne1, Amelie Juanchich1 and Christelle Hennequet-Antier1

1Institut national de recherche pour l'agriculture, l'alimentation et l'environnement (INRAE)

#### 30 October, 2025

# Contents

# Introduction

In this vignette, we perform a functional Gene Set Enrichment Analysis (GSEA) from differential Expression analysis from genes of luminal cells in the mammary gland. (see `utils::vignette("mouse_bioconducor", package ="ViSEAGO")`)

# 1 Genes of interest

We load examples files from ViSEAGO package using `system.file` from the locally installed package. We read gene identifiers (GeneID) and corresponding statistical values (BH padj) for all results.

in this example, gene identifiers were ranked based on the BH padj from Differential expression analysis.

    # load gene identifiants and padj test results from Differential Analysis complete tables
    PregnantvsLactate<-data.table::fread(
        system.file(
            "extdata/data/input",
            "pregnantvslactate.complete.txt",
            package = "ViSEAGO"
        ),
        select = c("Id","padj")
    )

    VirginvsLactate<-data.table::fread(
         system.file(
            "extdata/data/input",
            "virginvslactate.complete.txt",
            package = "ViSEAGO"
        ),
        select = c("Id","padj")
    )

    VirginvsPregnant<-data.table::fread(
        system.file(
            "extdata/data/input",
            "virginvspregnant.complete.txt",
            package = "ViSEAGO"
        ),
        select = c("Id","padj")
    )

    # rank Id based on statistical value (BH padj in this example)
    data.table::setorder(PregnantvsLactate,padj)
    data.table::setorder(VirginvsLactate,padj)
    data.table::setorder(VirginvsPregnant,padj)

Here, we display the header from the PregnantvsLactate ranked data.table.

              Id         padj
           <int>        <num>
        1: 11941 1.722774e-09
        2: 12992 1.722774e-09
        3: 13358 1.722774e-09
        4: 13645 1.722774e-09
        5: 69219 1.836763e-09
       ---
    15800: 79221 1.000000e+00
    15801: 80979 1.000000e+00
    15802: 83558 1.000000e+00
    15803: 84111 1.000000e+00
    15804: 97476 1.000000e+00

# 2 GO annotation of genes

In this study, we build a `myGENE2GO` object using the Bioconductor org.Mm.eg.db database package for the mouse species. This object contains all available GO annotations for categories Molecular Function (MF), Biological Process (BP), and Cellular Component (CC).

NB: Don’t forget to check if the last current annotation database version is installed in your R session! See `ViSEAGO::available_organisms(Bioconductor)`.

    # connect to Bioconductor
    Bioconductor<-ViSEAGO::Bioconductor2GO()

    # load GO annotations from Bioconductor
    myGENE2GO<-ViSEAGO::annotate(
        "org.Mm.eg.db",
        Bioconductor
    )

    # display summary
    myGENE2GO

    - object class: gene2GO
    - database: Bioconductor
    - stamp/version: 2019-Jul10
    - organism id: org.Mm.eg.db

    GO annotations:
    - Molecular Function (MF): 22707 annotated genes with 91986 terms (4121 unique terms)
    - Biological Process (BP): 23210 annotated genes with 164825 terms (12224 unique terms)
    - Cellular Component (CC): 23436 annotated genes with 107852 terms (1723 unique terms)

# 3 Functional GO enrichment

## 3.1 GO enrichment tests

We perform a functional Gene Set Enrichment Analysis (GSEA) from differential Expression analysis from genes of luminal cells in the mammary gland. Here, gene list were ranked based on the BH padj from Differential expression analysis. The enriched Biological process (BP) are obtained using a GSEA test with `ViSEAGO::runfgsea`, which is a wrapper from algorithms developped in fgsea package [1]. we perform the GO enrichment tests for BP category with `fgseaMultilevel`algorithm.

    # perform fgseaMultilevel tests
    BP_PregnantvsLactate<-ViSEAGO::runfgsea(
        geneSel=PregnantvsLactate,
        ont="BP",
        gene2GO=myGENE2GO,
        method ="fgseaMultilevel",
        params = list(
            scoreType = "pos",
             minSize=5
        )
    )

    BP_VirginvsLactate<-ViSEAGO::runfgsea(
        geneSel=VirginvsLactate,
        gene2GO=myGENE2GO,
        ont="BP",
        method ="fgseaMultilevel",
        params = list(
            scoreType = "pos",
             minSize=5
        )
    )

    BP_VirginvsPregnant<-ViSEAGO::runfgsea(
        geneSel=VirginvsPregnant,
        gene2GO=myGENE2GO,
        ont="BP",
        method ="fgseaMultilevel",
        params = list(
            scoreType = "pos",
             minSize=5
        )
    )

## 3.2 Combine enriched GO terms

We combine the results of the three GSEA tests into an object using `ViSEAGO::merge_enrich_terms` method.

    # merge fgsea results
    BP_sResults<-ViSEAGO::merge_enrich_terms(
        cutoff=0.01,
        Input=list(
            PregnantvsLactate="BP_PregnantvsLactate",
            VirginvsLactate="BP_VirginvsLactate",
            VirginvsPregnant="BP_VirginvsPregnant"
        )
    )

    # display a summary
    BP_sResults

    - object class: enrich_GO_terms
    - ontology: BP
    - method: fgsea
    - summary:
     PregnantvsLactate
        method : fgseaMultilevel
        sampleSize : 101
        minSize : 5
        maxSize : Inf
        eps : 0
        scoreType : pos
        nproc : 0
        gseaParam : 1
        BPPARAM : fgseaMultilevel
        absEps : 101
     VirginvsLactate
        method : fgseaMultilevel
        sampleSize : 101
        minSize : 5
        maxSize : Inf
        eps : 0
        scoreType : pos
        nproc : 0
        gseaParam : 1
        BPPARAM : fgseaMultilevel
        absEps : 101
     VirginvsPregnant
        method : fgseaMultilevel
        sampleSize : 101
        minSize : 5
        maxSize : Inf
        eps : 0
        scoreType : pos
        nproc : 0
        gseaParam : 1
        BPPARAM : fgseaMultilevel
        absEps : 101- enrichment pvalue cutoff:
            PregnantvsLactate : 0.01
            VirginvsLactate : 0.01
            VirginvsPregnant : 0.01
    - enrich GOs (in at least one list): 184 GO terms of 3 conditions.
            PregnantvsLactate : 67 terms
            VirginvsLactate : 58 terms
            VirginvsPregnant : 64 terms

Now you can follow mouse bioconductor vignette for next steps beginning with 3.3 Graphs of GO enrichment tests section (`utils::vignette("mouse_bioconducor", package ="ViSEAGO")`).

# References

1. Korotkevich G, Sukhov V, Sergushichev A. Fast gene set enrichment analysis. bioRxiv [Internet]. Cold Spring Harbor Labs Journals; 2019; https://doi.org/10.1101/060012