GSoC’23 @ NRNB : Week 6 (Gene Set Enrichment Analysis)

AIM : Exploring Pathways through Gene Set Enrichment Analysis : gsePC

Mentors : Guangchuang Yu, Augustin Luna

Week — 6 : Jul 3 — Jul 9

Introduction:

Greetings from the fifth week of my GSOC journey! Last week, we explored Over-Representation Analysis using the enrichPC function. This week, we're taking our analysis a step further with Gene Set Enrichment Analysis (GSEA) using the gsePC function.

Progress Made:

This week’s focus was on implementing the gsePC function, which is the gateway to Gene Set Enrichment Analysis. Armed with ranked gene lists, organisms, and more, this function enables us to delve into the complex world of pathway-level gene expression changes and enrichment.

gsePC <- function(geneList, source, keyType, ...) {
    keyType <- match.arg(keyType, c("hgnc", "uniprot"))
    source <- match.arg(source, get_pc_source())

    pcdata <- get_pc_data(source, keyType, output = 'gson')
    res <- GSEA(geneList, gson = pcdata, ...)

    if (is.null(res)) return(res)

    res@ontology <- pcdata@gsname
    res@organism <- pcdata@species
    res@keytype <-  keyType

    return(res)
}

Next Week Plan:

In the upcoming week, we’ll consolidate our insights by integrating GSEA results with other analyses.

Conclusion:

Exploring pathways through GSEA is akin to deciphering biological stories within genes. As we move forward, join me in weaving together diverse analyses for a comprehensive understanding.

The integration of insights and the amalgamation of diverse analyses showcase the potential of Pathway Commons within clusterProfiler.

As we conclude, I’m immensely grateful for the opportunity to learn, collaborate, and contribute. Thank you for joining me on this remarkable adventure !

Repository :

https://github.com/YuLab-SMU/clusterProfiler/blob/devel/R/pathwayCommons.R