Mycorrhiza algorithm

Published in -Mycorrhiza: Combining phylogenetic networks and Random Forests for prediction of ancestry from multilocus genotype data

Installing Mycorrhiza on Ubuntu 16.04

Make sure you have the latest version of Python 3.x
```
python3 --version
```

Install pip3, Java and the tkinter library

sudo apt-get install python3-pip python3-tk default-jre

Install Mycorrhiza
```
pip3 install --upgrade mycorrhiza
```
Install SplitsTree

Follow the instructions in the GUI installer, leaving all settings to default.
```
wget http://ab.inf.uni-tuebingen.de/data/software/splitstree4/download/splitstree4_unix_4_14_6.sh
chmod +x splitstree4_unix_4_14_6.sh
./splitstree4_unix_4_14_6.sh
```
If the link above is not available - find the most recent version of the SplitsTree: http://ab.inf.uni-tuebingen.de/data/software/splitstree4/download

Installing Mycorrhiza on Mac OS X Sierra 10.12

If you don't already have the package manager HomeBrew, install it before proceeding.

ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"

Install Python 3.x
```
brew install python
```

Install Mycorrhiza

sudo -H pip3 install --upgrade mycorrhiza

Install SplitsTree

The package can be found here. Follow the installer instructions, leaving all settings to default.

If the link above is not available - find the most recent version of the SplitsTree: http://ab.inf.uni-tuebingen.de/data/software/splitstree4/download

Running an analysis from command line

Run an analysis.

Run a 5-fold crossvalidated analysis.
```
crossvalidate -i gipsy.myc -o out/ -s 5
```
Run a analysis with a training set and a prediction set. Samples with a learing flag = 1 will be used for training and predictions will be made on samples with a learning flag = 0.
```
supervised -i gipsy.myc -o out/
```
To see all available parameters:
```
crossvalidate -h
```

Running an analysis in a script

Import the necessary modules.

from mycorrhiza.dataset import Myco
from mycorrhiza.analysis import CrossValidate
from mycorrhiza.plotting.plotting import mixture_plot

(Optional) By default Mycorrhiza will look for SplitStree in your home folder. I you wish to specify a different path for the SplitsTree executable you can do so in the settings module.
```
from mycorrhiza.settings import const
const['__SPLITSTREE_PATH__'] = '~/splitstree4/SplitsTree'
```
Load some data. Here data is loaded in the Mycorrhiza format from the Gipsy moth sample data file. Example data can be found here.
```
myco = Myco(file_path='data/gipsy.myc')
myco.load()
```

Run an analysis. Here a simple 5-fold cross-validation analysis is executed on all available loci, without partitioning.

cv = CrossValidate(dataset=myco, out_path='data/')
cv.run(n_partitions=1, n_loci=0, n_splits=5, n_estimators=60, n_cores=1)

Plot the results.
```
mixture_plot(cv)
```

Documentation

https://jgeofil.github.io/mycorrhiza/

File formats

For microsatellite loci set the is_str flag to True.

```python
data = Myco(file_path='data/myco.myc', is_str=True)
data = Structure(file_path='data/myco.str', is_str=True)
```

Myco

Diploid genotypes occupy 2 rows (the sample identifier must be identical).

Column(s)	Content	Type
1	Sample identifier	string
2	Population	string or integer
3	Learning flag	{0,1}
4 to M+3	SNP Loci	{A, T, G, C, N}
4 to M+3	STR Loci	any or 000

STRUCTURE

Diploid genotypes occupy 2 rows (the sample identifier must be identical).

Column(s)	Content	Type
1	Sample identifier	string
2	Population	integer
3	Learning flag	{0,1}
4 to O+3	Optional (Ignored)
O+3 to M+O+3	SNP Loci	integer or -9
O+3 to M+O+3	STR Loci	any or -9

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