Further Applications of lineage tree matching

[stefanhahmann]

User Story

A biologist would like to learn how similar lineage trees of the same or event different species are.

Cells that have been found to be corresponding cells in 2 samples of the same species may be used to generate spatial landmarks. For this purpose, it would be interesting to find corresponding lineage tree patterns that are similar to a certain degree.

Additional information

https://docs.google.com/document/d/1hQSD3yBIlqfgqypUhRN11fEK6VEc8UVlL-aIqzTJ9YI/edit#heading=h.yspfsl4eri0i

• Look at one lineage and try to predict the name of the root cell from another lineage

  • https://iiif.elifesciences.org/lax/34410%2Felife-34410-fig7-v2.tif/full/1500,/0/default.jpg
  • Leo (contact him and ask, if the code is now available)

• Networkx / Christophe Godin Library

  • https://networkx.org/documentation/stable/reference/algorithms/similarity.html (“Graph Edit distance”)

• Michalis: top-down approach (predicting the root) may work for stereotypic cell developments (as in Platynereis), but bottom-up would be more general (would also work in cases that are less stereotypic, e.g. SOP lineages in flies and regeneration in Parhyale)

• Pavel: also involve the spatial arrangement of the progenitor cells and/or the terminal cells; explore with sublineages rather than entire trees

• Jean-Yves Tinevez:

  • There are some recent work that are related to this idea (but do not overlap nor preclude it). For instance Jan Funke proposed machine learning to create lineages from sparse annotations: https://www.nature.com/articles/s41587-022-01427-7
  • Of course it would be awesome to see this implemented in Mastodon, but maybe some components of this work would be useful. 
  • For instance, they use some metrics, maybe derived from Léo Guignard work, to assess a distance from one lineage to another. If we have a metric, then we can produce an optimizer for it or learn it.
  • Léo once proposed a simpler version of this metrics in the MaMuT paper: https://elifesciences.org/articles/34410 (figure 7). At the time he said he was working on a more formal definition of it but I could not find it.

• Robert: Consider graph neural networks for prediction

• Illustration: