In maxconway/metabex: Metabolic Model Explorer

The metabex package is for Cytoscape-based analysis of populations of metabolic models, and their properties. This vignette introduces the package and provides some usage examples.

Overview

Package usage consists of:

1. Initializing cytoscape, and the cytoscape connection.
2. Loading a Sybil metabolic model.
3. Loading a population dataset.
4. Exploring.

Initialization

First, open cytoscape, and install the CytoscapeRPC plugin, if necessary. Then connect to cytoscape:

library(RCytoscape)
CytoscapeConnection()

Loading a model

Two sybil models are included with the package (from Sun et al, 2009):

library(metabex, warn.conflicts=FALSE)
data('geo_m_model', 'geo_s_model', package='metabex')

Once these are loaded into R, we can push them to Cytoscape like so:

sulwin <- cytoscape_load(geo_s_model)
metwin <- cytoscape_load(geo_m_model)

You'll see a progress report as the models are loaded, but after this you might have to interact with cytoscape a little to force it to update and show the models.

Loading a population

At this stage, you can interact with Cytoscape to explore the models visually, but you if you want to visualize a population, now is the time to load the population data. Included in this package are two datasets, whos creation is described in the accompanying paper.

data('geo_s', 'geo_m', package='metabex')

This point requires a bit more explanation. These population datasets are in the core format of this package: a data.frame, with some columns prefixed with genotype. and some with phenotype.. These mean exactly what one would expect: we have one genotype. column per gene, which can be boolean (i.e. presence) or numeric (i.e. expression level), and phenotype. columns represent properties of individuals (like biomass or synthetic objective levels).

Exploration

Let's plot the G. Sulfurreducens pareto front:

library(ggplot2)
ggplot(geo_s, aes(phenotype.biomass, phenotype.fe2)) + geom_point()

There is a noticeable jump between synthesis rates of ~0.21 and ~0.24 for a small decrease in biomass production. We can investigate this further by comparing these points in cytoscape.

Pushing individuals from R to Cytoscape

To see points from the pareto front on the graph, we first select the points from the graph. point_selector brings up a graph from which points can be clicked, to return their index.

index1 <- point_selector(dataset=geo_s, x='phenotype.biomass', y='phenotype.fe2')
index2 <- point_selector(dataset=geo_s, x='phenotype.biomass', y='phenotype.fe2')

To assign these points to the graph, we can call

show_gene_expression(model=geo_s_model, expressions=geo_s[index1,], cw=sulwin, attribute='activity1')
show_gene_expression(model=geo_s_model, expressions=geo_s[index2,], cw=sulwin, attribute='activity2')

We can then assign the attributes 'activity1' and 'activity2' to colours in cytoscape. However, more interesting is the activty difference. We can see this as follows:

show_gene_expression(
    model=geo_s_model, 
    expressions=(geo_s[index1,] - geo_s[index2,]), 
    cw=sulwin, 
    attribute='activityDiff'
    )

This creates a new attribute showing the activity difference.

Pulling reactions from Cytoscape to R

In the G. Sulfurreducens metabolic network, there are three connected components: the large, main one, and two small ones of one and two reactions each. If we want to find out what they do, we can select them in Cytoscape and view their attributes. However, if we want to examine them in more detail, we can select them, then pull them back into R:

rxns = get_selected_rxns(sulwin, geo_s_model)

maxconway/metabex documentation built on May 21, 2019, 1:39 p.m.