Neighbor-Joining algorithm with theoretical guarantees improves CRISPR-Cas9 lineage tracing tree reconstruction
By
Nir Yosef
If you only eat one bagel today, this is the bagel.
Summary
This paper presents a theoretical analysis of tree reconstruction from CRISPR-Cas9 lineage tracing data using Neighbor-Joining (NJ) algorithms. The authors develop a moment-matched distance estimation method and prove reconstruction guarantees even when model parameters are unknown and data is missing—a first for CRISPR-Cas9 lineage tracing. They demonstrate improved performance over traditional NJ approaches on both simulated data and real data from a mouse model of lung cancer.
Key quotes
· 4 pulledWe develop a series of tools to analyze this algorithm and prove its theoretical guarantees.
When the parameters of the data generating process are known and there is no missing data, our results align with established results from common evolutionary models, such as Cavender-Farris-Neyman and Jukes-Cantor.
We develop new theory that shows for the first time that it is still possible to obtain reconstruction guarantees in the CRISPR-Cas9 case and in other models of evolution.
Empirically, we show on both simulated lineage tracing data and on real data from a mouse model of lung cancer the improved performance of our method as compared to the traditional use of NJ.
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