Comments (8)
What is the difference between scDeepCluster.py,scDeepCluster_latent.py and scDeepCluster_estimate_k.py?
"scDeepCluster.py" is the code of scDeepCluster model.
"scDeepCluster_latent.py" is the code used to extract latent embedding after the model training.
"scDeepCluster_estimate_k.py" is the code to use an intuitive metric generalizability to estimate the number of clusters from data.
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Sorry I can't see your attachment, could you send it again? Thanks.
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I'm sorry. I know it was a little abrupt. I found that github can upload pictures. The following are the network structure you gave on github and the network structure I ran out with your code. The main difference seems to be the way Gaussian noise is added. Does that make a difference? I am looking forward to your reply.
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Yes, there is difference. The main difference is I didn't add noise to the clustering layer, which will make the model difficult to converge.
Why don't you use the PyTorch version? This code is based on Keras 1.0 which is not supported any more currently.
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I'm sorry, I don't understand what you mean. You said that you did not add noise to the clustering layer, but because I modified the network to remove the clustering layer. The way I'm talking about noise being added differently is during encoding . As shown in the following figure. Also thanks for your advice, I will try the PyTorch version. Can you answer the questions I have raised about this code? What causes these two different ways of adding noise? Is it the structure of the network or the keras version? Do different noise adding methods have a big impact on the overall network?
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Related Issues (10)
- jupyter notebook cannot be loaded HOT 2
- how to get the optimal value of cluster ? HOT 2
- some question about "code" HOT 6
- why does your model run on cpu definitely? HOT 1
- ImportError: cannot import name 'extension_dict' HOT 1
- repeat results HOT 1
- scDeepCluster in HPC HOT 4
- how can I get clustering information of every cell? HOT 1
- reproduce tutorial.ipynb HOT 3
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