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PhD thesis unknown to me, which does Bayesian inference of a tree of cells, as well as many other things


I was following a reference chain and came across the PhD thesis of Jonathan Laserson, who was a Daphne Koller student and now works at Google.

The thesis is interesting. It’s quite ambitious: in one chapter he sets up a Bayesian inference algorithm for a tree of cells (cc @mathmomike) that explicitly models sequencing error. On the other hand, I can’t see how he got any of this to work efficiently for the sizes of data sets discussed, and his software, called ImmuniTree, is not to be found on the web.

@jeetsukumaran and others may know this fellow from his Genovo de novo assembly software for mixed populations.


The software ImmuniTree features prominently in the article by Sok et al., but the still I found no link to the software itself. Text S1 has a fairly detailed description of the Bayesian framework used by ImmuniTree. There are two modeling assumptions that I don’t fully understand:

  1. Mutations are modeled using a discrete-time Markov chain, while the prior over the tree appears to be a continuous-time birth-death process.
  2. Internal nodes of the tree are supposed to be observed, because reads are assigned to all nodes of the tree. It is possible that I simply don’t understand the experimental design used by the authors.