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

In ViSEAGO: ViSEAGO: a Bioconductor package for clustering biological functions using Gene Ontology and semantic similarity

# load ViSEAGO and mouse db package
library(ViSEAGO)

# knitr document options
knitr::opts_chunk$set(
    eval=FALSE,echo=TRUE,fig.pos = 'H',
    fig.width=6,message=FALSE,comment=NA,warning=FALSE
)

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"))

Genes of interest

We load examples files from r BiocStyle::Biocpkg("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.

# show the ten first lines of genes_DE (same as genes_ref)
PregnantvsLactate

GO annotation of genes

In this study, we build a myGENE2GO object using the Bioconductor r BiocStyle::Biocpkg("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

cat(
"- 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)"
)

Functional GO enrichment

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 r BiocStyle::Biocpkg("fgsea") package @fgsea. we perform the GO enrichment tests for BP category with fgseaMultilevelalgorithm.

# 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
    )
)

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

cat(
"- 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{-}

ViSEAGO documentation built on Nov. 8, 2020, 6:51 p.m.