GSoC’23 @ NRNB : Week 5 (Over-Representation Analysis)

AIM : Unleashing Insights with Over-Representation Analysis: enrichPC

Mentors : Guangchuang Yu, Augustin Luna

Week — 5 : Jun 26 — Jul 2

Introduction:

Welcome to the fifth week of my GSOC journey! Over the past weeks, we’ve embarked on a captivating exploration of Pathway Commons data. This week, we’re delving into the realm of Over-Representation Analysis (ORA) using the powerful enrichPC function.

Progress Made:

This week’s focus was on implementing the enrichPC function, a key player in ORA. This function empowers us to perform Over-Representation Analysis using inputs such as gene vectors, organisms, and additional parameters. The result? An enrichResult instance brimming with insights into significant gene-pathway associations.

enrichPC <- function(gene, source, keyType = "hgnc", ...) {
    keyType <- match.arg(keyType, c("hgnc", "uniprot"))
    source <- match.arg(source, get_pc_source())

    pcdata <- get_pc_data(source, keyType, output = 'gson')
    res <- enricher(gene, 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 venture into the realm of Gene Set Enrichment Analysis (GSEA) using the gsePC function.

Conclusion:

Unleashing insights from data requires powerful tools, and enrichPC fits the bill perfectly. Stay with me as we journey forward to explore GSEA and its applications in Pathway Commons analysis.

Repository :

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