Inference of single-cell phylogenies from lineage tracing data using Cassiopeia

The pairing of CRISPR/Cas9-based gene editing with massively parallel single-cell readouts now enables large-scale lineage tracing. However, the rapid growth in complexity of data from these assays has outpaced our ability to accurately infer phylogenetic relationships. First, we introduce Cassiopeia - a suite of scalable maximum parsimony approaches for tree reconstruction. Second, we provide a simulation framework for evaluating algorithms and exploring lineage tracer design principles. Finally, we generate the most complex experimental lineage tracing dataset to date, 34,557 human cells continuously traced over 15 generations, and use it for benchmarking phylogenetic inference approaches. We show that Cassiopeia outperforms traditional methods by several metrics and under a wide variety of parameter regimes, and provide insight into the principles for the design of improved Cas9-enabled recorders. Together these should broadly enable large-scale mammalian lineage tracing efforts.Cassiopeia and its benchmarking resources are publicly available at https://www.github.com/YosefLab/Cassiopeia. scRNA-seq of 4 samples from an in vitro CRISPR/Cas9-based lineage experiment, resulting in 34,557 A549 cells across 11 clones. Please note that scRNAseq was performed solely to acquire the resulting lineage information, stored as insertions or delection (indels) on transcribed molecules, and that no other transcriptional data was used in this study. Included in this repository are: (1) Fastq reads for each of the four samples (each corresponding to a plate in the original experiment); (2) possorted_genome_bam's produced from cell ranger (3) a processed "alleleTable" describing the lineage information for all the cells, across the 11 clones; and (4) reconstructed trees for each clone, in newick format.