Tommaso Biancalani

Sorry for the super late response - since I changed job my time to respond messages is very small. The "density_prior" is your best estimate for the cell density within...

Woohoo - we love you enjoy the method. We have not implemented yet deconvolution in "an easy" fashion BUT, the Squidpy team wrote a beautiful tutorial which I recommend https://squidpy.readthedocs.io/en/stable/external_tutorials/tutorial_tangram.html...

Hi Enan, I was responding on GitHub but I cannot see the message anymore: did you delete it? For the histogram of cell types: if you do deconvolutions (ie you...

Ciao Carlo, In order to get the percentage of cell types per spot I would deconvolve. This means that you need to have prior information of how many cells you...

Ciao edward and apologizes for the delay in responding (holidays and such). `project_cell_annotation` simply transfers any annotation from the scRNAseq data to space. Most times, we use it to transfer...

The simplest way is to fetch the most probable cell for each voxel (which assumes one cell per voxel). To get a more accurate histogram, you can sample one cell...

Short answer: yes. Long answer: yes but be careful. Let's say that you have 4 voxels (containing 1 cell each), each of them is 75% prob. of being a cancer...

Yes it does work. Easy way to test, take some cells out from a cell type and remap. Also, you can map using cluster mode (which takes all cells of...

That's interesting. I would love to see a figure to understand how much this matters. Tangram look for a minimum of a loss function. If you change cell type ratio...

You can certainly integrate the data which will allow you to: - obtain cell types in space - obtain spatial data using scRNAseq which will resolve thousands of genes not...