Asymmetry tests for Bifurcating Auto-Regressive Processes with missing data

TL;DR

This paper develops symmetry tests for bifurcating autoregressive processes with missing data, focusing on detecting asymmetry between odd and even cells in cell lineage data, with applications to simulated and real datasets.

Contribution

It introduces novel asymmetry tests for BAR processes accounting for missing data and applies them to biological cell lineage data.

Findings

Tests successfully detect asymmetry in simulated data.

Application to real data reveals significant asymmetry.

Method handles missing data effectively.

Abstract

We present symmetry tests for bifurcating autoregressive processes (BAR) when some data are missing. BAR processes typically model cell division data. Each cell can be of one of two types \emph{odd} or \emph{even}. The goal of this paper is to study the possible asymmetry between odd and even cells in a single observed lineage. We first derive asymmetry tests for the lineage itself, modeled by a two-type Galton-Watson process, and then derive tests for the observed BAR process. We present applications on both simulated and real data.