lkmklsmn

Thanks for your questions! _1. Do you intend the PCA to be run on the counts data or transformed data?_ In our analysis we ran PCA on the transformed data...

I see your point. If the missing cells are scattered across batches, you would have to assign them into a specific batch called 'missing'. We realize this may not be...

One `missing` will do. Valid point, I appreciate your advice. I do want to point out that you could use the integrated two dimensional embedding for downstream analysis. (clustering etc).

As a quick solution (which I havent tested myself). You could try running `import tensorflow as tf` `import keras` `config = tf.ConfigProto(device_count={"CPU": 3})` `keras.backend.tensorflow_backend.set_session(tf.Session(config=config))` before you train the model.