MEMO OF THINGS TO DO

[dalessioluca]

0. produce one large segmentation of smFISH_OLEH and VISIUM
1. check batch_norm in UNET (currently is not there)
2. reflection padding in UNET (currently is not there) or nothing and then prediction on a smaller region?
3. the graph is not a K_NN graph. is that ok? Optimize the radius. It seems that larger is better (i.e. 5 is better than 2). To evaluate this systematically you need to make plots of N_OBJECTS vs RESOLUTION parameters. Hopefully for large radius we will see a plateau
4. is greedy modularity optimization the thing we are interested in? TIM suggests: If you aren’t committed to greedy modularity maximization, one of the fastest libraries that will get you community detection (using Stochastic Block Models) is graph tool (https://graph-tool.skewed.de/). It’s c++ underneath (using Boost I believe), so it is very fast. The tradeoff is that it can be a huge pain in the ass to install, though I have heard it has recently been simplified.
5. the graph is partitioned in disconnected components. Is there an advantage in treating each connected component separately. Is community detection faster? Can I use the same resolution parameters for all the different disconnected components
6. loss function optimization. It seems that the best loss function was the one in
   folder: /home/jupyter/REPOS/spacetx-research/NEW_ARCHIVE/merfish_june22_v2
   commit 39d6bf2
   Change master implementation back to that one. Try to understand the differences.
7. can i reduce operation for the creation of the graph to 1/4 by using roller2d on just one quadrant?

[dalessioluca]

1. I might have a bug in graph construction since the plot of sum_j e_ij does not look right.
2. I want a density clustering algorithm on a graph. Read: http://dm.uestc.edu.cn/wp-content/uploads/paper/Graph%20Clustering%20with%20Local%20Density-Cut.pdf
3. How do I build the right density cluster.
4. Try HDBSCAN

[dalessioluca]

Do a watershed based on d_i = sum_j e_ij
Read: https://en.wikipedia.org/wiki/Watershed_(image_processing)#:~:text=The%20algorithm%20works%20on%20a,should%20emerge%20along%20the%20edges.

[dalessioluca]

measure distance based || pi_i - pi_j ||^2 not e_ij = < pi_i | pi_j >
With max_distance = 1.0