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


#1

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.


#2

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.