knitr::opts_chunk$set(
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GSEAdv

Travis build status AppVeyor Build Status Coverage status lifecycle Project Status: WIP – Initial development is in progress, but there has not yet been a stable, usable release suitable for the public.

The goal of GSEAdv is to provide methods to work with gene sets collections.

GSEAdv is based on the relationship between genes and gene sets under this schema: Schema of gene sets

And provides methods to understand the relationships between each property of the schema and as a whole.

Installation

install.packages(".", repos = NULL)

It is an R package you will be able to install it from the Bioconductor project with:

## install.packages("BiocManager") 
BiocManager::install("GSEAdv")

You can install this version of GSEAdv with:

## install.packages("devtools")
devtools::install_github("llrs/GSEAdv")

How does it work?

It is simple, load the package and learn from your data!

# Load some data
library("GSEAdv")
fl <- system.file("extdata", "Broad.xml", package = "GSEABase")
gss <- getBroadSets(fl)
gss
summary(gss)

Which tells us that each gene in the GeneSetCollection is only on one gene set.

We can try with a bigger dataset, one derived from human genes pathways in KEGG:

summary(genesKegg)

Knowing that it has so much pathways and genes we can learn how do they relate. The number of genes per pathway in the collection is:

gpp <- genesPerPathway(genesKegg)
plot(table(gpp))

We can see that most gene sets have low number of genes but one has 1130 genes in a single gene set (It is the gene set r names(gpp)[gpp > 400]). The genes might be associated too with many gene sets, it is so extreme? Let's see:

ppg <- pathwaysPerGene(genesKegg)
plot(table(ppg))

Not so extreme, one gene (r names(ppg)[which.max(ppg)]) appears in r max(ppg) gene sets.

To see which gene sets are included in other gene sets we can use nested:

nested(genesKegg)[1:10, 80:90]

As expected the pathway with more than 1100 genes has other pathways inside it.

You can see the vignettes for more examples.

Who will use this repo or project?

It is intended for bioinformaticians, both people interested in comparing databases and people developing analysis using the information provided by GSEAdv.

What is the goal of this project?

The goal of this project is to be able to understand the gene sets collections available.

What can be GSEAdv used for?

Contributing

Please read how to contribute for details on the code of conduct, and the process for submitting pull requests.

You can also look at the tests and add more tests to increase the quality of the package.

Acknowledgments

The ideas of this package were developed after a colleague asked a question in a poster presentation of my other package BioCor. To know the whole history you can read this blogpost.



llrs/GSEAdv documentation built on May 29, 2019, 6 p.m.