gpps

gpps reconstructs tumor phylogenies from single-cell DNA sequencing data to infer evolutionary histories while modeling mutation losses and intra-tumor heterogeneity.

Key Features:

• Phylogeny Reconstruction: gpps reconstructs phylogenetic trees representing the evolutionary history of tumor cells and subclonal relationships.
• Handling Mutation Losses: gpps allows each mutation to be lost at most a fixed number of times, enabling modeling of back mutations.
• Single-Cell Data Specificity: gpps analyzes single-cell DNA sequencing data while accounting for moderate false negative rates and missing values.
• Maximum Likelihood Search: gpps employs a maximum likelihood search algorithm to identify the phylogenetic tree that best explains the input data.

Scientific Applications:

• Intra-Tumor Heterogeneity Analysis: gpps supports analysis of intra-tumor heterogeneity and subclonal composition by reconstructing tumor phylogenies.
• Cancer Phylogeny Reconstruction: gpps enables reconstruction of cancer progression models from single-cell sequencing to map evolutionary pathways of cancer cells.

Methodology:

gpps integrates principles from phylogenetics with computational algorithms, allows for mutation losses, and employs a maximum likelihood approach while accounting for moderate false negative rates and missing values in single-cell DNA sequencing data.