ConvexML: Scalable and accurate inference of single-cell chronograms from CRISPR/Cas9 lineage tracing data

CRISPR/Cas9 gene editing technology has enabled lineage tracing for thousands of cells in vivo. However, most of the analysis of CRISPR/Cas9 lineage tracing data have so far been limited to the reconstruction of single-cell tree topologies, which depict lineage relationships between cells, but not the amount of time that has passed between ancestral cell states and the present. Time-resolved trees, known as chronograms, would allow one to study the evolutionary dynamics of cell populations at an unprecedented level of resolution. Indeed, time-resolved trees would reveal the timing of events on the tree, the relative fitness of subclones, and the dynamics underlying phenotypic changes in the cell population – among other important applications. In this work, we introduce the first scalable and accurate method to refine any given single-cell tree topology into a single-cell chronogram by estimating its branch lengths. To do this, we leverage a statistical model of CRISPR/Cas9 cutting with mi..., Trees and lineage tracing data were simulated as described in detail in our manuscript., , # ConvexML: Scalable and accurate inference of single-cell chronograms from CRISPR/Cas9 lineage tracing data

[https://doi.org/10.5061/dryad.qrfj6q5nz](https://doi.org/10.5061/dryad.qrfj6q5nz)

Our simulated trees paired with lineage tracing data encompass a large number of lineage tracing regimes, which are used to assess the performance of our proposed branch length estimator.

## Description of the data and file structure

For each lineage tracing regime, 50 simulations are performed. All trees have exactly 400 leaves, and were simulated as described in the manuscript. The `default' regime consists of:

13 barcodes.

3 target sites per barcode.

mutation rate adjusted to obtain an expected 50% mutated entries in the character matrix.

100 indel states.

20% missing data, with 10% coming from heritable epigenetic silencing and 10% coming from sequencing dropouts. (This does not include missing data further introduced by double-resection events, which we also simulate.)

Each lineage t...,