<i>Startle</i>: a star homoplasy approach for CRISPR-Cas9 lineage tracing

Sashittal, Palash; Schmidt, Henri; Chan, M.K.; Raphael, Benjamin J.

doi:10.1101/2022.12.18.520935

Cited by 7 publications

(22 citation statements)

References 69 publications

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“…4A. Interestingly, LAML produces a very different tree topology from the published trees inferred by Cassiopeia-Hybrid [50] (normalized RF 0.81) and Startle-NNI [38] (normalized RF 0.58). We discuss the biological implications of these different inferred topologies in this section and the next.…”

Section: Resultsmentioning

confidence: 87%

“…We compare LAML to four other algorithms for constructing trees from lineage tracing data: the Cassiopeia-Greedy method [24], distance-based Neighbor Joining (implemented in Cassiopeia [24]), DCLEAR [20] – another distance-based method, and Startle-NNI [38] – a maximum-parsimony method. We evaluate these methods on simulated trees with 1024 leaves (i.e.…”

Section: Resultsmentioning

confidence: 99%

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Maximum Likelihood Inference of Time-scaled Cell Lineage Trees with Mixed-type Missing Data

Mai,

Chu,

Raphael

2024

Preprint

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Recent dynamic lineage tracing technologies combine CRISPR-based genome editing with single-cell sequenc- ing to track cell divisions during development. A key computational problem in dynamic lineage tracing is to infer a cell lineage tree from the measured CRISPR-induced mutations. Three features of dynamic lineage tracing data distinguish this problem from standard phylogenetic tree inference. First, the CRISPR-editing process modifies a genomic location exactly once. This non-modifiable property is not well described by the time-reversible models commonly used in phylogenetics. Second, as a consequence of non-modifiability, the number of mutations per time unit decreases over time. Third, CRISPR-based genome-editing and single-cell sequencing results in high rates of both heritable and non-heritable (dropout) missing data. To model these features, we introduce the Probabilistic Mixed-type Missing (PMM) model. We describe an algorithm, LAML (Lineage Analysis via Maximum Likelihood), to search for the maximum likelihood (ML) tree under the PMM model. LAML combines an Expectation Maximiza- tion (EM) algorithm with a heuristic tree search to jointly estimate tree topology, branch lengths and missing data parameters. We derive a closed-form solution for the M-step in the case of no heritable missing data, and a block coordinate ascent approach in the general case which is more efficient than the standard General Time Reversible (GTR) phylogenetic model. On simulated data, LAML infers more accurate tree topologies and branch lengths than existing methods, with greater advantages on datasets with higher ratios of heritable to non-heritable missing data. We show that LAML provides unbiased time-scaled estimates of branch lengths. In contrast, we demonstrate that maximum parsimony methods for lineage tracing data not only underestimate branch lengths, but also yield branch lengths which are not proportional to time, due to the nonlinear decay in the number of mutations on branches further from the root. On lineage tracing data from a mouse model of lung adenocarcinoma, we show that LAML infers phy- logenetic distances that are more concordant with gene expression data compared to distances derived from maximum parsimony. The LAML tree topology is more plausible than existing published trees, with fewer total cell migrations between distant metastases and fewer reseeding events where cells migrate back to the primary tumor. Crucially, we identify three distinct time epochs of metastasis progression, which includes a burst of metastasis events to various anatomical sites during a single month.

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Section: Resultsmentioning

confidence: 87%

Section: Resultsmentioning

confidence: 99%

Maximum Likelihood Inference of Time-scaled Cell Lineage Trees with Mixed-type Missing Data

Mai,

Chu,

Raphael

2024

Preprint

Self Cite

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“…once a parental haplotype is lost in a lineage, it cannot be regained. We construct a tumor phylogeny among cancer clones using the inferred LOH events and the star homoplasy model in Startle [35]. Then, we project the phylogeny in space and infer the spatial location of ancestors using a diffusion model (Section 4.7).…”

Section: Copy Numbersmentioning

confidence: 99%

Inferring allele-specific copy number aberrations and tumor phylogeography from spatially resolved transcriptomics

Ma,

Balaban,

Liu

et al. 2024

Preprint

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A key challenge in cancer research is to reconstruct the somatic evolution within a tumor over time and across space. Spatially resolved transcriptomics (SRT) measures gene expression at thousands of spatial locations in a tumor, but does not directly reveal genetic aberrations. We introduce CalicoST, an algorithm to simultaneously infer allele-specific copy number aberrations (CNAs) and a spatial model of tumor evolution from SRT of tumor slices. By modeling CNA-induced perturbations in both total and allele-specific gene expression, CalicoST identifies important types of CNAs – including copy-neutral loss of heterozygosity (CNLOH) and mirrored subclonal CNAs– that are invisible to total copy number analysis. On SRT data from nine patients from the Human Tumor Atlas Network (HTAN) with matched whole exome sequencing (WES) data, CalicoST achieves an average accuracy of 86%, approximately 21% higher than existing methods. On two patients with SRT data from multiple adjacent slices, CalicoST reconstructs a tumor phylogeography that describes the spread of cancerous clones in three-dimensional space. CalicoST analysis of multiple SRT slices from a cancerous prostate organ reveals five spatially coherent clones, with mirrored subclonal CNAs distinguishing clones on the two sides of the prostate, forming a bifurcating phylogeography in both genetic and physical space.

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“…Our procedure searches the space of copy number trees for a given copy number matrix 𝐶 using sub-tree interchange operations [27] and relies heavily on the efficient algorithm we developed for the small parsimony problem (Problem 3) when the balancing condition (1) is dropped. The procedure is similar to the tree search procedure we developed for lineage tracing data [37]. Complete details on our tree search procedure are in Supplementary Methods A.1.…”

Section: Lazac Algorithm For Zcnt Large Parsimonymentioning

confidence: 99%

“…We assess the accuracy of the inferred trees compared to the ground truth simulated trees by employing two distinct tree dissimilarity metrics. These metrics are implemented in the TreeCmp tool [5] and the comparisons are done in a similar manner to the comparisons in our Startle [37] paper. Our metrics take a ground truth tree, T * , and an inferred tree, T , both in Newick format [6].…”

Section: A5 Comparison To Simulated Treesmentioning

confidence: 99%

A zero-agnostic model for copy number evolution in cancer

Schmidt

Sashittal

Raphael

2023

Preprint

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Motivation: New low-coverage single-cell DNA sequencing technologies enable the measurement of copy number profiles from thousands of individual cells within tumors. From this data, one can infer the evolutionary history of the tumor by modeling transformations of the genome via copy number aberrations. A widely used model to infer such copy number phylogenies is the copy number transformation (CNT) model in which a genome is represented by an integer vector and a copy number aberration is an event that either increases or decreases the number of copies of a contiguous segment of the genome. The CNT distance between a pair of copy number profiles is the minimum number of events required to transform one profile to another. While this distance can be computed efficiently, no efficient algorithm has been developed to find the most parsimonious phylogeny under the CNT model. Results: We introduce the zero-agnostic copy number transformation (ZCNT) model, a simplification of the CNT model that allows the amplification or deletion of regions with zero copies. We derive a closed form expression for the ZCNT distance between two copy number profiles and show that, unlike the CNT distance, the ZCNT distance forms a metric. We leverage the closed-form expression for the ZCNT distance and an alternative characterization of copy number profiles to derive polynomial time algorithms for two natural relaxations of the small parsimony problem on copy number profiles. While the alteration of zero copy number regions allowed under the ZCNT model is not biologically realistic, we show on both simulated and real datasets that the ZCNT distance is a close approximation to the CNT distance. Extending our polynomial time algorithm for the ZCNT small parsimony problem, we develop an algorithm, Lazac, for solving the large parsimony problem on copy number profiles. We demonstrate that Lazac outperforms existing methods for inferring copy number phylogenies on both simulated and real data.

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Startle: a star homoplasy approach for CRISPR-Cas9 lineage tracing

Cited by 7 publications

References 69 publications

Maximum Likelihood Inference of Time-scaled Cell Lineage Trees with Mixed-type Missing Data

Maximum Likelihood Inference of Time-scaled Cell Lineage Trees with Mixed-type Missing Data

Inferring allele-specific copy number aberrations and tumor phylogeography from spatially resolved transcriptomics

A zero-agnostic model for copy number evolution in cancer

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