GRMT: Generative Reconstruction of Mutation Tree From Scratch Using Single-Cell Sequencing Data.

Area of Science:

• Genomics
• Computational Biology
• Cancer Research

Background:

• Single-cell sequencing (SCS) reveals genetic diversity crucial for understanding tumor evolution.
• SCS data is prone to noise, complicating accurate tumor tree reconstruction.
• Current methods for tumor phylogeny are computationally intensive or lack resolution.

Purpose of the Study:

• To develop an efficient and accurate method for reconstructing tumor mutation trees from SCS data.
• To address limitations of existing tumor phylogeny inference techniques.

Main Methods:

• Introduced GRMT (Generative Reconstruction of Mutation Tree), a novel method for inferring tumor mutation trees.
• Utilized the k-Dollo parsimony model, allowing mutations to be gained once and lost up to k times.
• Employed an iterative tree generation process, adding one mutation at a time to build the complete mutation tree.

Main Results:

• GRMT efficiently recovers the chronological order of mutations.
• The method scales well to large datasets.
• Evaluations on simulated and real data demonstrate GRMT's superior performance compared to state-of-the-art methods.

Conclusions:

• GRMT provides an efficient and accurate approach for tumor phylogeny reconstruction from SCS data.
• The method overcomes computational and resolution limitations of existing techniques.
• GRMT software is publicly available for broader research application.