xomx-tutorials

Tutorials for xomx (https://github.com/perrin-isir/xomx)

List of colab notebooks:

• xomx_kidney_classif: a tutorial in two phases:
  xomx_kidney_classif_1.ipynb (phase 1) and
  xomx_kidney_classif_2.ipynb (phase 2).
  Remark: the phase 1, about importation and basic preprocessing of the data, can be skipped.
  Goal of the tutorial: use a recursive feature elimination method on RNA-seq data to identify gene biomarkers for the differential diagnosis of three types of kidney cancer.

• xomx_pbmc.ipynb
  Goal of the tutorial: follow the single cell RNA-seq Scanpy tutorial on 3k PBMCs, except for the computation of biomarkers for which recursive feature elimination is used.

• xomx_tcr.ipynb
  Goal of the tutorial: train an extra-trees classifier to predict whether a TCR beta-chain CDR3 sequence is associated with a given epitope.

• xomx_hla.ipynb
  Goal of the tutorial: try to predict the tissue type based on HLA-presented peptides that have been found in it.

To run tutorials as python scripts instead of notebooks:

You can run locally the convert_notebooks_to_scripts.sh shell script. It will convert the tutorials to python scripts and put them in the tuto_scripts folder:

bash convert_notebooks_to_scripts.sh
cd tuto_scripts

You can now run the tutorials with python, for instance:

python xomx_pbmc.py

You can also pass "bokeh" or "matplotlib" as an argument to the scripts to force the plots to be generated by either bokeh or matplotlib. For example:

python xomx_pbmc.py matplotlib

For large plots, it may be interesting to use matplotlib as it can improve responsiveness compared to the JavaScript-powered stand-alone bokeh plots.