Comprehensive understanding of finite-data errors in lineage-based growth rate inference

Develop a comprehensive understanding of the systematic errors introduced when estimating the long-term population growth rate Λ from finite single-cell lineage data, particularly those arising from sampling large deviations of generation times and division counts in lineage-based methods that map single-cell statistics to population growth.

Background

The paper studies how population growth rate Λ can be inferred from single-cell lineage statistics, noting that growth selects for exponentially rare phenotypes and inference necessarily relies on sampling large deviations from finite datasets. This introduces systematic errors whose full characterization has been difficult.

The authors analyze bias and variance in two lineage-based estimators and decompose bias into finite-time and nonlinear averaging components, providing tools to mitigate and understand these errors. The abstract frames the need for a comprehensive understanding of finite-data errors as an open issue motivating the work.