In ccb-hms/BioPlexAnalysis: Applications and downstream analysis of BioPlex PPI data

Setup

library(BioPlex)
library(BioPlexAnalysis)
library(sparklyr)
library(graphframes)
library(dplyr)

Connecting to Spark

For working GraphFrames, we need a connection to Spark. For computation on a computer cluster, this can be achieved by connecting to the spark installation on the cluster.

However, for demonstration purposes we are using here a local connection, that will require a local Spark installation.

This can be achieved via (need to be carried out only once):

sparklyr::spark_install("3.0")

We can connect to both local instances of Spark as well as remote Spark clusters. Here we connect to our local installation of Spark via

sc <- sparklyr::spark_connect(master = "local", version = "3.0")

Store BioPlex PPIs in a GraphFrame

Get the latest version of the 293T PPI network:

bp.293t <- BioPlex::getBioPlex(cell.line = "293T", version = "3.0")
head(bp.293t)

and turn into a graph object:

bp.gr <- BioPlex::bioplex2graph(bp.293t)
bp.gr

Switch to a graphframes backend:

gf <- BioPlexAnalysis::graph2graphframe(bp.gr, sc)
gf

PageRank

PageRank is an algorithm used in Google Search for ranking websites in their results, but it has been adopted also for other purposes. According to Google, PageRank works by counting the number and quality of links to a page to determine a rough estimate of how important the website is. The underlying assumption is that more important websites are likely to receive more links from other websites.

For the analysis if PPI networks, personalized PageRank seems to be capable to robustly evaluate the importance of the vertices of a network, relatively to some already known relevant nodes (Ivan and Grolmusz, 2011).

As an example we look at the ACADVL gene. Mutations in ACADVL are associated with very long-chain acyl-coenzyme A dehydrogenase deficiency.

We first look up the corresponding node ID:

dplyr::filter(graphframes::gf_vertices(gf), symbol == "ACADVL")

And then apply the personalized PageRank as implemented in the graphframes package.

gf <- graphframes::gf_pagerank(gf, 
                               reset_prob = 0.15,
                               max_iter = 10L,
                               source_id = "P49748")
gf

Inspect the results:

dplyr::filter(graphframes::gf_vertices(gf), pagerank > 0)
dplyr::filter(graphframes::gf_edges(gf), src == "P49748")

Switch back to a graphNEL backend:

gr <- BioPlexAnalysis::graphframe2graph(gf)
gr
head(graph::nodeData(gr), n = 2)
head(graph::edgeData(gr), n = 2)

ccb-hms/BioPlexAnalysis documentation built on Jan. 29, 2023, 6:55 p.m.