James KitchensExcited to announce the release of version 0.0.2 of the `tskit_arg_visualizer` Python package on PyPI! This update brings functionality for handling larger ARGs, including major performance improvements (thanks to @yan) and a new Node View that shows local connections around a focal node, useful when your ARG is too large to display fully. Styling has also been overhauled, allowing for customizability in the colors and symbols used in your plots. Check it out at https://pypi.org/project/tskit-arg-visualizer/!
Matt OsmondAfter solving the issue of non-independence mathematically, we developed an algorithm to quickly compute the quantities we need (using #tskit), allowing us to infer spatial histories from large ARGs.
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Claudia Solis-Lemus@petrelharp I want a #tskit sticker 👋
Peter RalphUnfortunately, I've got to announce a bug in pyslim - if you've been using it, update and read on. #tskit
It's possibly a bad one: pyslim.recapitate( ) has been introducing a short bottleneck to size Ne=1 (!) in most cases.
EDIT: I accidentally pushed out the bugfix release without the fix to the bug.It is now fixed FOR REALS in 1.0.3, now on pip and soon on conda. :facepalm:
Heading off to #Evol2023! If you're looking for a postdoc, come talk to me! Or, if you want a #tskit sticker.
jeromekelleherWe can analyse these ARGs using the well-established and feature-rich #tskit library (and surrounding ecosystem). All of the analyses for the preprint were done using Jupyter notebooks, and most run in seconds on a standard laptop.
@kitchensjn does log time (as in #tskit) work as a nice intermediate between these two extremes (visibility vs info)?
It's been a little while since I last updated the #tskit ARG visualizer. Today, I added an optional scale bar to the left of the ARG that gives the timing of nodes. You can now also change the y-axis scaling from "rank" (default, equal spacing between nodes) to "time" (proportional to the true timing of the node). Scaling by time often leads to a jumbled mess, so it should probably only be useful with very simple ARGs in its current form. If you have ideas on how to improve that, let me know!
Ultra-realistic simulations of 1.4 million human genomes generated by using a detailed pedigree of French Canadians as input to #msprime! These simulations are hugely useful for large-scale genomics methods development, because they are freely available, easy to download (2.8G for chr1), and efficient to process using #tskit. I hope they will become a standard benchmark across all sorts of methods.
Added the ability to highlight trees within the #tskit ARG visualizer. If the optional parameter tree_highlighting is set to True (default), the visualizer now pairs the ARG with a "chromosome" along the bottom of the figure that is segmented according to the tree sequence breakpoints. You can highlight the individual trees by hovering over their respective segment of the chromosome.