Detecting branching rate heterogeneity with tree balance statistics in lineage tracing trees

Area of Science:

• * Cell biology
• * Bioinformatics
• * Evolutionary biology

Background:

• * Cellular birth rate heterogeneity is key for cancer progression and tumor genetic analysis.
• * Lineage tracing reconstructs cellular genealogies to measure branching rate heterogeneity.
• * Lineage tracing introduces tree complexities, like multifurcations, complicating analysis.

Purpose of the Study:

• * To extend tree topology metrics for analyzing branching rate heterogeneity in lineage tracing data.
• * To evaluate methods for detecting rate heterogeneity despite lineage tracing-induced distortions.
• * To apply validated methods to experimental xenograft data.

Main Methods:

• * Examination of tree characteristics in lineage tracing-derived genealogies.
• * Development and extension of tree topology-based metrics.
• * Simulation of lineage tracing data to test statistical methods.
• * Application of validated methods to xenograft experimental data.

Main Results:

• * Editing window placement in lineage tracing can cause root or tip multifurcations.
• * The $$ statistic effectively detects branching rate heterogeneity in simulated data.
• * Widespread rate heterogeneity was observed across multiple xenograft study systems.

Conclusions:

• * Tree topology statistics can be adapted to analyze lineage tracing data.
• * Methods are proposed to overcome lineage tracing distortions in phylogenetic analysis.
• * The study highlights the utility of $$ statistic for analyzing cell division rates.