mycorrhiza 0.0.15

Mycorrhiza population assignment tools.

# Mycorrhiza
Combining phylogenetic networks and Random Forests for prediction of ancestry from multilocus genotype data.

## Running an analysis from command line

1. Install Docker

Instructions can be found [here](https://docs.docker.com/install/).
1. (On linux, optional) [Give Docker root access](https://docs.docker.com/install/linux/linux-postinstall/#manage-docker-as-a-non-root-user).

2. Get the Mycorrhiza image.

```bash
docker pull jgeofil/mycorrhiza:latest
```

3. Run an analysis.

Example data can be found [here](https://github.com/jgeofil/mycorrhiza/tree/master/examples/data).

```bash
docker run -v [WORKING DIRECTORY]:/temp/ mycorrhiza crossvalidate -i /temp/[INPUT FILE] -o /temp
```

For example, in a folder containing the input file gipsy.myc.

```bash
docker run -v $PWD:/temp/ mycorrhiza crossvalidate -i /temp/gipsy.myc -o /temp
```

## Running an analysis in a script

### Installing Mycorrhiza with pip

1. Make sure you have the latest version of Python 3.x

```bash
python --version
```

2. Install pip

https://pip.pypa.io/en/stable/installing/

3. Install Mycorrhiza

```bash
pip3 install --upgrade mycorrhiza
```

4. Install SplitsTree

Installation executables for SplitsTree4 can be
found [here](http://ab.inf.uni-tuebingen.de/data/software/splitstree4/download/welcome.html).

5. Install matplotlib

Instructions can be found [here](https://matplotlib.org/users/installing.html).

### Running an analysis in a script

1. Import the necessary modules.

```python
from mycorrhiza.dataset import Myco
from mycorrhiza.analysis import CrossValidate
from mycorrhiza.plotting.plotting import mixture_plot
```
2. (Optional) By default Mycorrhiza will look for SplitStree in your PATH.
I you wish to specify a different path for the SplitsTree executable you can do so in the settings module.

```python
from mycorrhiza.settings import const
const['__SPLITSTREE_PATH__'] = 'SplitsTree'

```
3. Load some data. Here data is loaded in the Mycorrhiza format from the Gipsy moth sample data file.
Example data can be found [here](https://github.com/jgeofil/mycorrhiza/tree/master/examples/data).

```python
myco = Myco(file_path='data/gipsy.myc')
myco.load()
```

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

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

5. Plot the results.

```python
mixture_plot(cv)
```

## Documentation

[https://jgeofil.github.io/mycorrhiza/](https://jgeofil.github.io/mycorrhiza/)


## File formats

### 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 | Loci | {A, T, G, C, N} |

### 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 | Loci | integer or -9 |