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shine

Structure Learning for Hierarchical Networks
A package to aid in structure learning for hierarchical biological regulatory networks

Documentation

Please visit https://montilab.github.io/shine/ for comprehensive documentation.

Requirements

We suggest R 3.6.0 but R (>= 3.5.0) is required to install directly from Github. For workflows, you will need Python (>= 2.7.0) and dependencies for 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. Alternatively, check out usage with Docker.

Installation

Install the development version of the package from Github.

devtools::install_github("montilab/shine")
library(shine)

Quick Example

data(toy)
    ABC
    / \
   AB  \ 
  /  \  \
 A    B  C 

dim(toy)
#> Features  Samples 
#>      150       30

table(toy$subtype)
#> 
#>  A  B  C 
#> 10 10 10

Variable Selection

# 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=75)

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

Structure Constraints

# Detect modules
wgcna <- mods.detect(eset, min.size=5, cor.fn="cor", do.plot=FALSE)
# Module membership
mods.plot(wgcna$dat, wgcna$mods, wgcna$colors, ncol=3, size=2.5)

# Module extension
mods.extended <- fuzzy.mods(wgcna$dat, wgcna$mods, p=0.75)
mods.extended$grey <- NULL
mods <- sort(sapply(wgcna$mods, length), decreasing=TRUE)
print(mods)
#> turquoise      blue     brown    yellow     green       red 
#>        25        17        11         9         7         6

sapply(mods.extended, length)[names(mods)]
#> turquoise      blue     brown    yellow     green       red 
#>        26        19        15        11         8         7

Network Estimation

$ 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 See full documentation for shine-nf.

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

Docker

$ docker pull montilab/shine:latest

Hierarchy

Define Workflow

#!/usr/bin/env nextflow
# wf.nf

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)
}

Run

$ nextflow run wf.nf -with-docker montilab/shine
N E X T F L O W  ~  version 20.07.1
Launching `wf.nf` [happy_koch] - revision: c2526aec9e
executor >  local (36)
[f4/c715fd] process > ABC:SPLIT          [100%] 1 of 1 ✔
[1b/37cc11] process > ABC:LEARN (6)      [100%] 6 of 6 ✔
[99/23dcba] process > ABC:RECONSTRUCT    [100%] 1 of 1 ✔
[1b/4e3537] process > AB:LEARN_PRIOR (6) [100%] 6 of 6 ✔
[2a/42f4df] process > AB:RECONSTRUCT     [100%] 1 of 1 ✔
[da/17bb2f] process > A:LEARN_PRIOR (6)  [100%] 6 of 6 ✔
[59/d209b2] process > A:RECONSTRUCT      [100%] 1 of 1 ✔
[26/65e7a0] process > B:LEARN_PRIOR (6)  [100%] 6 of 6 ✔
[7e/b65391] process > B:RECONSTRUCT      [100%] 1 of 1 ✔
[58/721e46] process > C:LEARN_PRIOR (6)  [100%] 6 of 6 ✔
[99/517354] process > C:RECONSTRUCT      [100%] 1 of 1 ✔
Completed at: 21-Nov-2020 16:00:29
Duration    : 2m 16s
CPU hours   : 0.1
Succeeded   : 36