Network Learning

Simplified Example

This is the last step in learning networks. At this point, you should have one or more datasets to learn networks for, employed a gene selection method, and learned structural constraints for your networks.

library(shine)

data(toy)

dim(toy)

Features  Samples 
     150       30 

# Filter out non-varying genes
genes.filtered <- keep.var(toy, column="subtype", subtypes=c("A", "B", "C"))

# Select top genes by median absolute deviation
genes.selected <- rank.var(toy, column="subtype", subtypes=c("A", "B", "C"), genes=genes.filtered, limit=150)

# Subset toy dataset
eset <- toy[genes.selected,]

# Find constraints for structure with zero-order correlations
wgcna <- mods.detect(eset, min.size=5, cores=3, do.plot=FALSE)
fuzzy <- fuzzy.mods(wgcna$dat, wgcna$mods, p=0.9)
fuzzy$grey <- NULL

# Set constraints
blanket <- blanket.new(wgcna$genes)
blanket <- blanket.lift(blanket, mods=fuzzy)

# Learn network
bdg <- bdg.estimate(data=t(Biobase::exprs(eset)), 
                    mpl=TRUE,
                    g.prior=blanket, 
                    method="ggm", 
                    iter=5000, 
                    print=100, 
                    cores=3)

Network Workflows

Due to the heavy computational requirements for high-dimensional graphical modeling, we learn network hierarchies in parallel on high performance computing platforms. Because some networks are dependent on others (e.g. A network is used as a prior in learning another) we need a reactive workflow to handle all of the processes.

We have create a separate resources for learning networks called shine-nf. This is a collection of Nextflow modules (DSL2) for modeling hierarchical biological regulatory networks. It wraps methods developed in the shine R package in Nextflow modules for building hierarchical workflows.

Nextflow
Workflows are built using Nextflow. Nextflow can be used on any POSIX compatible system (Linux, OS X, etc) and requires BASH and Java 8 (or higher) to be installed. Download the latest version of Nextflow compatible with DSL2:

$ curl -s https://get.nextflow.io | bash

Hint

Once downloaded make the nextflow file accessible by your $PATH variable so you do not have to specify the full path to nextflow each time. e.g. nextflow run rather than path/to/nextflow run

Clone Directory

$ git clone https://github.com/montilab/shine-nf

Docker

$ docker pull montilab/shine:latest

Example Hierarchy

dim(toy)

Features  Samples 
     150       30 

table(toy$subtype)

 A  B  C 
10 10 10 

    ABC
    / \
   AB  \ 
  /  \  \
 A    B  C 

Individual / No Constraints

This most simple workflow includes building networks for groups A, B, and C without any prior information.

workflow A {
    main:
      eset = "data/esets/A.rds"
      LEARN( eset )
}
workflow B {
    main:
      eset = "data/esets/B.rds"
      LEARN( eset, prior )
}
workflow C {
    main:
      eset = "data/esets/C.rds"
      LEARN( eset, prior )
}
workflow {
    A()
    B()
    C()
}

Individual / Constraints

This most simple workflow includes building networks for groups A, B, and C without any prior information.

workflow A {
    main:
      eset = "data/esets/A.rds"
      modules = "data/modules.rds"
      SPLIT( eset, modules )
      LEARN( SPLIT.out.flatten() )
      RECONSTRUCT( eset, LEARN.out[0].collect() )
}
workflow B {
    main:
      eset = "data/esets/B.rds"
      modules = "data/modules.rds"
      SPLIT( eset, modules )
      LEARN( SPLIT.out.flatten() )
      RECONSTRUCT( eset, LEARN.out[0].collect() )
}
workflow C {
    main:
      eset = "data/esets/C.rds"
      modules = "data/modules.rds"
      SPLIT( eset, modules )
      LEARN( SPLIT.out.flatten() )
      RECONSTRUCT( eset, LEARN.out[0].collect() )
}
workflow {
    A()
    B()
    C()
}

Hierarchy / Constraints

workflow ABC {
    main:
      eset = "data/esets/ABC.rds"
      modules = "data/modules.rds"
      SPLIT( eset, modules )
      LEARN( SPLIT.out.flatten() )
      RECONSTRUCT( eset, LEARN.out[0].collect() )
    emit:
      LEARN.out[0]
}
workflow AB {
    take: 
      prior
    main:
      eset = "data/esets/AB.rds"
      LEARN_PRIOR( eset, prior )
      RECONSTRUCT( eset, LEARN_PRIOR.out[0].collect() )
     emit:
     LEARN_PRIOR.out[0]
}
workflow A {
    take: 
      prior
    main:
      eset = "data/esets/A.rds"
      LEARN_PRIOR( eset, prior )
      RECONSTRUCT( eset, LEARN_PRIOR.out[0].collect() )
}
workflow B {
    take: 
      prior
    main:
      eset = "data/esets/B.rds"
      LEARN_PRIOR( eset, prior )
      RECONSTRUCT( eset, LEARN_PRIOR.out[0].collect() )
}
workflow C {
    take: 
      prior
    main:
      eset = "data/esets/C.rds"
      LEARN_PRIOR( eset, prior )
      RECONSTRUCT( eset, LEARN_PRIOR.out[0].collect() )
}
workflow {
    ABC()
    AB(ABC.out)
    A(AB.out)
    B(AB.out)
    C(ABC.out)
}

Refer to shine-nf for detailed documentation.