genomics training example about deeplift HOT 1 OPEN

Hi @saralinker, sorry for the slow response - I was on medical leave last quarter.

In terms of a tutorial for training genomics models, I think this notebook by Ziga Avsec is a good place to start; it trains a very simple model with 1 convolutional layer, but hopefully it's enough to give you a grounding: https://colab.research.google.com/github/Avsecz/DL-genomics-exercise/blob/master/Simulated.ipynb. Note that colab notebooks currently default to tensorflow version 2, and if you want to force an earlier version of tensorflow you need to execute the command %tensorflow_version 1.x at the beginning of the notebook.

When you say your "weights are not correct", can you be more specific? In case you were running into an hdf5 error with reading the model weights, this was because the model weights were saved with an earlier version of the h5py library; you have to use h5py < 3.0.0 for reading the weights to work. I have updated the example colab notebook in the deeplift repo to reflect this: https://colab.research.google.com/github/kundajelab/deeplift/blob/master/examples/genomics/genomics_simulation.ipynb

In terms of interpretation, if you have trouble using this particular deeplift repository, then you might have more luck using the DeepSHAP implementation (DeepSHAP is an extension of deeplift, and the implementation is done in a more flexible way such that it works with a wider array of models). I have an example notebook using DeepSHAP here: https://colab.research.google.com/github/AvantiShri/shap/blob/5fdad0651176cdbf1acd6c697604a71529695211/notebooks/deep_explainer/Tensorflow%20DeepExplainer%20Genomics%20Example%20With%20Hypothetical%20Importance%20Scores.ipynb. I also have detailed slides from a lab meeting I gave on using DeepSHAP, in case those are helpful: https://docs.google.com/presentation/d/1JCLMTW7ppA3Oaz9YA2ldDgx8ItW9XHASXM1B3regxPw/edit?usp=sharing

from deeplift.