# difflearn
This is a python tool packages for differential network inference (DNI).
This package mainly contains:
- Differential network inference models:
- Pinv;
- NetDiff;
- BDgraph;
- JGL;
- JGLCV;
- Expression profiles simulation algorithms:
- distributions:
- Gaussian;
- Exponential;
- Mixed;
- network structures:
- random;
- hub;
- block;
- scale-free;
- Visulization tools and some useful utilities.
## Requirments:
Before installation, you should:
1. install [pytorch](https://pytorch.org/) yourself according to your environment;
2. install [R language](https://www.r-project.org/) and R packages as follows:
- JGL
```r
install.packages( "JGL" )
```
- BDgraph:
```r
install.packages( "BDgraph" )
```
- NetDiff:
```r
library(devtools)
install_git("https://gitlab.com/tt104/NetDiff.git")
```
Please note:
If you have several different versions of `R`, you should specify the version installed with above packages with:
```python
import os
os.environ["R_HOME"] = "your path to R"
```
## Installation
Easily run:
```
pip install difflearn
```
## Quick Start
```python
from difflearn.simulation import *
from difflearn.models import Random,Pinv,NetDiff,BDGraph,JointGraphicalLasso,JointGraphicalLassoCV
from difflearn.utils import *
from difflearn.visualization import show_matrix
import matplotlib.pyplot as plt
data_params = {
'p': 10,
'n': 1000,
'sample_n': 100,
'repeats': 1,
'sparsity': [0.1, 0.1],
'diff_ratio': [0.5, 0.5],
'parallel_loops': 1,
'net_rand_mode': 'BA',
'diff_mode': 'hub',
'target_type': 'float',
'distribution': 'Gaussian',
'usage': 'comparison',
}
data = ExpressionProfilesParallel(**data_params)
modelrandom = Random()
modelPinv = Pinv()
modelBDgraph = BDGraph()
modelNetDiff = NetDiff()
modelJGL = JointGraphicalLasso()
modelJGLCV = JointGraphicalLassoCV()
(sigma, delta, *X) = data[0]
modelrandom.fit(X)
modelPinv.fit(X)
modelBDgraph.fit(X)
modelNetDiff.fit(X)
modelJGL.fit(X)
modelJGLCV.fit(X)
fig, axs = plt.subplots(4, 2, figsize=(7,7))
show_matrix(vec2mat(delta)[0], ax=axs[0][0], title = 'Ground Truth')
axs[0][1].set_visible(False)
show_matrix(modelrandom.delta, ax=axs[1][0], title = 'Random')
show_matrix(modelPinv.delta, ax=axs[1][1], title = 'Pinv')
show_matrix(modelBDgraph.delta, ax=axs[2][0], title = 'BDgraph')
show_matrix(modelNetDiff.delta, ax=axs[2][1], title = 'NetDiff')
show_matrix(modelJGL.delta, ax=axs[3][0], title = 'JGL')
show_matrix(modelJGLCV.delta, ax=axs[3][1], title = 'JGLCV')
plt.tight_layout()
fig.set_dpi(300)
plt.show()
```
