Dominik J. Otto

Hi, I looked at this by chance because I made the pymc transformation that may has inspired this simplex transformation. From my perspective I also not like to see `int64`...

Please let me know if I need to make any further changes.

I changed the license back to GPL-2.0-only to avoid any concerns when merging. Please let me know if there are any blockers.

Hi @minghao622! Thanks for your inquiry! We are currently working on establishing a differential abundance framework that will hopefully make this use case a lot easier. However, to answer your...

Thank you for your patience. There’s no update on this issue yet, but you might find the `normalize` option for predicted values useful. You can find details in the [documentation](https://mellon.readthedocs.io/en/latest/predictor.html#mellon.Predictor)....

Yes, using `estimator.predict(X, normalize=True)` adjusts for differences based on the total number of cells, provided that `estimator = mellon.DensityEstimator(d_method="fractal", ...)`. Keep in mind that this normalization is approximate; the integral...

Using this code enables a method to make an estimation of the intrinsic dimensionality of the dataset. This impacts the unit of the resulting log-density and, therefore, its range. It...

Hi @solivehong, Thank you for reaching out! I’d be happy to help clarify. Could you specify whether you’re aiming to use RNA counts from your spatial RNA-seq data to infer...

The left scale is for the trend line and the right scale is for the cell-specific gene expression values. The two can be very different, especially, when the non-imputed log-count...

Hi @yitengfei120011, thank you for this interesting question! The cells are selected based on the fate probability computed by Palantir. The tool simply selects all cells for a specific fate...