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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. 
+
+.. image:: ikarus_scheme.pdf
+  :width: 600
+
+
+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 notebooks                                 |
++====================================================+
+| `Data preparation and basic prediction`_           |
++----------------------------------------------------+
+
+.. _`Data preparation and basic prediction`: https://github.com/BIMSBbioinfo/ikarus/blob/master/tutorial.ipynb
+
+
+
+