PyAGH

KO | EN

gitlite — search

#javascript #python #ai #typescript #golang #deep-learning #linux #docker #java #windows #kubernetes #vue #security #open-source

(★ 13)

A package for calculating kinship matrix

PyAGH Latest Version Download

Download Latest Version (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# PyAGH

## Introduction

`PyAGH` is a Python package developed for calculating relationship matrix using pedigree, genotype or microbiology data as well as  processing, analysis and visualization for data. `PyAGH` provides fast and concise methods for calculating Amatrix (based on pedigree), Gmatrix (based on genotype), Mmatrix(based on OTU) and Hmatrix (based on pedigree and genotype) used in breeding. `PyAGH` supports  high marker density typing data, large pedigree data, microbiome data, additive, dominant and epistatic effects relationship matrix, relationship matrix construction across population. With the obtained relationship matrix, you can use `PyAGH` for fast visualization, including PCA analysis, Cluster analysis and drawing Heatmaps.

To make it easier to use pedigree information, `PyAGH ` also provides targeted tools for specific needs, such as detecting pedigree errors,  selection individuals, pedigree visualization, sorting pedigree, calculating inbreeding coefficients and calculating  relationship coefficients . The latest stable release of the software can be installed conveniently via pip .

### Target Audience

The target audience of `PyAGH` includes:

- Students and researchers in the field of breeding and genetics, particularly those who want to perform genomic prediction.
- Government, enterprises, or other entities who expect efficient processing of pedigree and genomic information.

### Technical Features

- Provide a variety of  methods for calculating kinship matrix including combined reference population.
- Fast and support tens of millions of genotypes.

### Main Functions

Currently, `PyAGH` mainly provides the following methods:

- **Pedigree sorting:** Provides methods to quickly obtain the correct pedigree in chronological order of birth clean multiple types of data error.
- **Pedigree selection:** Provides methods to select pedigree based on specific individuals and generations.
- **Calculation of coefficients:** Provides methods to easily obtain the inbreeding and relationship coefficients of specific individuals.
- **Kinship matrix:** Provides different methods to calculating Amatix, Gmatrix and Hmatrix.
- **Visualization:** Provides methods to display results.

---

## Installation

It is recommended to use `Python 3.9`

### Using pypi

`PyAGH`can be installed by using `pip install`.

```shell
pip install PyAGH
```

## Examples

#### Load the example data

`loadEgPed()` and `loadEgGeno()` functions can load the example data.

```python
import PyAGH
ped = PyAGH.loadEgPed()
genofile = PyAGH.loadEgGeno() #is a filename
ped
```

| index | id | sire | dam |
| :---: | -: | ---: | --: |
|   0   |  9 |    1 |   2 |
|   1   | 10 |    3 |   4 |
|   2   | 11 |    5 |   6 |
|   3   | 12 |    7 |   8 |
|   4   | 13 |    9 |  10 |
|   5   | 14 |   11 |  12 |
|   6   | 15 |   11 |   4 |
|   7   | 16 |   13 |  15 |
|   8   | 17 |   13 |  14 |

#### Sort pedigree

`sortPed()` is a function that can sort the pedigree according to the correct birth date of individuals and check for various errors in the  pedigree.

```python
ped_sorted = PyAGH.sortPed(ped)
ped_sorted
```

| index | id | sire | dam |
| :---: | :-: | :--: | :-: |
|   0   | 1 |  0  |  0  |
|   1   | 3 |  0  |  0  |
|   2   | 5 |  0  |  0  |
|   3   | 7 |  0  |  0  |
|   4   | 2 |  0  |  0  |
|   5   | 4 |  0  |  0  |
|   6   | 6 |  0  |  0  |
|   7   | 8 |  0  |  0  |
|   8   | 9 |  1  |  2  |
|   9   | 10 |  3  |  4  |
|  10  | 11 |  5  |  6  |
|  11  | 12 |  7  |  8  |
|  12  | 15 |  11  |  4  |
|  13  | 13 |  9  | 10 |
|  14  | 14 |  11  | 12 |
|  15  | 16 |  13  | 15 |
|  16  | 17 |  13  | 14 |

#### Select pedigree

`selectPed()` function can select pedigree based on specific individuals and generations.

```python
ped_selected = PyAGH.selectPed(data=ped,id=['9','10'],generation=3)
ped_selected
```

| index | id | sire | dam |
| :---: | :-: | :--: | :-: |
|   0   | 1 |  0  |  0  |
|   1   | 3 |  0  |  0  |
|   2   | 2 |  0  |  0  |
|   3   | 4 |  0  |  0  |
|   4   | 9 |  1  |  2  |
|   5   | 10 |  3  |  4  |

#### Calculate kinship matrix

`makeA()`, `makeG()` and `makeH()` functions can calculate the kinship matrix using various methods based on pedigree , genotype and both together, respectively.

```python
A = PyAGH.makeA(ped_sorted)
G = PyAGH.makeG(File=genofile,method=1,File_list=False)
G_inter = PyAGH.makeG_inter(geno,method="dd")
M = PyAGH.makeM(OTU)
H = PyAGH.makeH(G,A,w=0.05)
####A,G and H are lists with 2 elements. The first one is kinship matrix in numpy.ndarray type and the other one is id labels in pandas.Series type. 
```

#### Calculate coefficients

```python
coef_inbreeding = PyAGH.coefInbreeding(A)
coef_inbreeding
```

| index | ID |    F    |
| :---: | :-: | :-----: |
|   0   |  1  | 0.0000 |
|   1   |  3  | 0.0000 |
|   2   |  5  | 0.0000 |
|   3   |  7  | 0.0000 |
|   5   |  4  | 0.0000 |
|  11  | 12 | -0.0000 |
|  ...  | ... |   ...   |
|  12  | 15 | -0.0000 |
|  13  | 13 | -0.0000 |
|  14  | 14 | -0.0000 |
|  15  | 16 | 0.0625 |
|  16  | 17 | -0.0000 |

```python
coef_kinship = PyAGH.coefKinship(A)
coef_kinship
```

|     | ID1 | ID2 |        r |
| --: | --: | --: | -------: |
|   0 |   1 |   1 | 1.000000 |
|   1 |   1 |   3 | 0.000000 |
|   2 |   1 |   5 | 0.000000 |
|   3 |   1 |   7 | 0.000000 |
|   4 |   1 |   2 | 0.000000 |
| ... | ... | ... |      ... |
| 148 |  14 |  16 | 0.121268 |
| 149 |  14 |  17 | 0.500000 |
| 150 |  16 |  16 | 1.000000 |
| 151 |  16 |  17 | 0.333486 |
| 152 |  17 |  17 | 1.000000 |

153 rows × 3 columns

#### Visualization

```python
cluster_example = PyAGH.cluster(A)
plt.savefig('cluster_example.png', facecolor='w',dpi=300)
```

```python
group=['1','1','1','1','1','1','1','2','2','2','2','2','2','2','2','2','2']
pca_example = PyAGH.pca(A,group=group)
pca_example.savefig('pca_example.png', facecolor='w',dpi=300)
```

![pca](https://raw.githubusercontent.com/zhaow-01/PyAGH/main/picture/pca_example.png)

```python
heatmap_example = PyAGH.heat(A)
plt.savefig('heatmap_example.png', facecolor='w',dpi=300)
```

```python
import graphviz
ped_selected = PyAGH.selectPed(data=ped,id=['17'],generation=3)
p = PyAGH.gragh(ped_selected)
graphviz.Source(p)
```

![output](https://raw.githubusercontent.com/zhaow-01/PyAGH/main/picture/ped_example.png)

## License

`PyAGH` is MIT licensed.

PyAGH

PyAGH Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

PyAGH

PyAGH Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Download README