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# Ikarus
Ikarus is a stepwise machine learning pipeline that tries to cope with a task of distinguishing tumor cells from normal cells. Leveraging multiple annotated single cell datasets it can be used to define a gene set specific to tumor cells. First, the latter gene set is used to rank cells and then to train a logistic classifier for the robust classification of tumor and normal cells. Finally, sensitivity is increased by propagating the cell labels based on a custom cell-cell network. Ikarus is tested on multiple single cell datasets to ascertain that it achieves high sensitivity and specificity in multiple experimental contexts. 

![Chema](ikarus_schema.pdf)

## Installation
Make sure you are using python >= 3.8 before installing ikarus. If that requirement is fulfilled, ikarus can be installed from a gitthub repo:
```
git clone https://github.com/BIMSBbioinfo/ikarus.git
cd ikarus
pip install -e .
```
Alterantively, one can install ikarus' master branch directly from github:
```
python -m pip install git+https://github.com/BIMSBbioinfo/ikarus.git
```

## Usage
The easiest option to get started is to use the provided Tumor/Normal gene lists and the pretrained model.
```
from ikarus import classifier

model = classifier.Ikarus(signatures_gmt=signatures_path)
model.load_core_model(model_path)
predictions = model.predict(test_adata, 'test_name')
```

More information on how to train a model or how to create own gene lists is provided in the [tutorial notebook](tutorial.ipynb).