prideRcompare

An R package for comparing and clustering PRIDE Archive projects.

This package makes use of the prideR package.

Installation

First, we need to install devtools:

install.packages("devtools")
library(devtools)

Then we just call

install_github(username="Bioanalytics", repo="prideRcompare")

Usage

Archive distance calculators

The following code

archive.accession.distance.retriever("PXD001034", project.count=100)

Returns the Jaccard similarity index between the PRIDE Archive project with accession PXD001034 and the first 100 projects returned by the PRIDE Archive web service (that currently are the 100 most recent ones).

We can use ProjectDetail objects in a similar way.

Individual distances

Individual distances can be calculated using distance.ProteinDetail like in

p1034.protein.details <- get.list.ProteinDetail("PXD001034")
p1156.protein.details <- get.list.ProteinDetail("PXD001156")
distance.ProteinDetail(p1034.protein.details, p1156.protein.details)

Also applicable to lists using the function distance.list.ProteinDetail and distance.df.ProteinDetail.

Clustering

We can cluster lists of ProteinDetail and ProjectSummary instances, although in the end the clustering is always done on the protein details and therefore the later method uses the former one through the PRIDE Archive web service. In order to be a good citizen, the recommended usage is through ProteinDetail obtaining first the list of lists of protein details for each project.

cancer.projects.100 <- search.list.ProjectSummary("cancer", 100)
cancer.projects.100.protein.details.100 <- lapply(cancer.projects.100, function(x) {get.list.ProteinDetail(accession(x), 100)})
cancer.clusters.100.100 <- cluster.ProteinDetails(cancer.projects.100.protein.details.100)

That will give us a hierarchical cluster objects (as generated by hclust) that we can use to find out clusters (e.g. 5 clusters) using:

cancer.projects.100.accessions <- sapply(cancer.projects.100, accession)
cutree.labels(cancer.clusters.100.100, 5, cancer.projects.100.accessions)

Or just plot:

plot(cancer.clusters.100.100, cancer.projects.100.accessions, main="Clustering of latest 100 cancer projects")

Examples

Some clustering examples can be found here.