Installation

Install Julia

To install Julia, follow instructions here.

Julia code is compiled just-in-time, meaning that the first time you run a function, it will be compiled at that moment. So, please be patient! Future calls to the function will be much faster. Trying out toy examples for the first calls is a good idea.

Install SNaQ

To install the package, type inside Julia:

using Pkg
Pkg.add("SNaQ")

If you already installed the package and want the latest registered version, do the following (which will update all of your packages):

Pkg.update()

It is important to update the package regularly as it is undergoing constant development.

Pkg.update() will install the latest registered version, but there could be other improvements in the main branch of the repository. If you want to update to the latest unregistered version of the package, you can do Pkg.add(PackageSpec(name="SNaQ", rev="main")) just beware that the latest changes could be not as robust. If you want to go back to the registered package, you can do Pkg.free("SNaQ").

Similarly, you can pin a version of the package Pkg.pin("SNaQ") so that Pkg.update() will not modify it. You can always free a pinned package with Pkg.free("SNaQ"). More on package management here.

The SNaQ package has dependencies like PhyloNetworks (see the Project.toml file for the full list), but everything is installed automatically.

PhyloPlots has utilities to visualize networks, and for interoperability, such as to export networks to R (which can then be plotted via R).

To install:

using Pkg
Pkg.add("PhyloNetworks")
Pkg.add("PhyloPlots")

PhyloPlots also depends on PhyloNetworks, and has further dependencies like RCall

To check that your installation worked, type this in Julia to load the package. This is something to type every time you start a Julia session:

using PhyloNetworks
using SNaQ

You can see a list of all the functions with

varinfo(SNaQ)

and press ? inside Julia to switch to help mode, followed by the name of a function (or type) to get more details about it.

Test example

We show here small examples on how to get more info on an object, what's its type, and how to manipulate objects.

For example, let's take an object raxmlCF created from reading in some data in the form of gene trees (see more on the data in Inputs for SNaQ):

julia> raxmltrees = joinpath(dirname(pathof(SNaQ)), "..","examples","raxmltrees.tre");
julia> raxmlCF = readtrees2CF(raxmltrees);will use all quartets on 6 taxa
calculating obsCF from 30 gene trees and for 15 quartets
Reading in quartets...
0+---------------+100%  
  ***************  
table of obsCF printed to file tableCF.txt
descriptive stat of input data printed to file summaryTreesQuartets.txt

Typing varinfo() will provide a list of objects and packages in memory, including raxmlCF that we just created. If we want to know the type of a particular object, we do:

julia> typeof(raxmlCF)DataCF

which shows us that raxmlCF is of type DataCF. If we want to know about the attributes the object has, we can type ? in Julia, followed by DataCF for a description. We can also ask for a list of all its attributes with

julia> fieldnames(typeof(raxmlCF))(:quartet, :numQuartets, :tree, :numTrees, :repSpecies)

For example, we see that one attribute is numQuartets: its the number of 4-taxon subsets in the data. To see what this number is:

julia> raxmlCF.numQuartets15

We also noticed an attribute quartet. It is a vector of Quartet objects inside raxmlCF, so

julia> raxmlCF.quartet[2].taxon4-element Vector{String}:
 "A"
 "B"
 "C"
 "E"

will provide the list of taxon names for the second 4-taxon subset in the data. To see the observed CF, we can type

julia> raxmlCF.quartet[2].obsCF3-element Vector{Float64}:
 0.8333333333333334
 0.03333333333333333
 0.13333333333333333

We can verify the type with

julia> typeof(raxmlCF.quartet[2])Quartet