Hi Adam and everyone,
This is quite cool!
I want to ask you these 3 things for a while now (any thoughts would be appreciated):
For expression data we typically just log1p the data before encoding as a first layer, and for most of our models, 1 hidden layer is good. I think if you take a look at our basic Encoder class, it's probably useful for your situation
- Do you have any tips about making the encoder NN work well? For example, which data transformations for input to the encoder NN work best? how many layers is a good starting point (e.g. for complex datasets with >50 cell populations)? Anything else relevant?
What is the purpose of KL warmup step? What function is this trying to achieve?
https://github.com/YosefLab/scvi-tools/issues/735#issuecomment-681201770
If you have any experience with this, which transformation works best for transforming the output of encoder NN positive variables (exp, softplus, softplus with a different beta, ...)? E.g. variance of the posterior distribution, positive latent variables (thinking of cell2location and other models I am working on)
We typically just use exp, though I've heard good things about softplus.
We typically just use exp, though I've heard good things about softplus.
Would it be fair to say that KL warmup is essentially training the encoder NN to predict the distribution prior?
I'm not sure I would characterize it this way, but I'm not 100% sure what you mean.
pip install -e .[dev]
method
Hi folks, just wanted to show the live deploy of a simngle page app for scvi DE I made using flask: https://cengen-de.textpressolab.com/
I made this deploy for wormbase.org to allow people to explore a new dataset with 100k cells and 65k C. elegans neurons that came out, but you can deploy it with your own data easily if you have the trained model