SCITE

SCITE reconstructs tumor phylogenies and mutational histories from noisy and incomplete single-cell somatic mutation profiles to infer tumor evolutionary trajectories.

Key Features:

• Noisy and incomplete data handling: Operates on noisy and incomplete single-cell somatic mutation profiles.
• Stochastic search algorithm: Employs a stochastic search algorithm to explore possible mutation histories.
• Flexible MCMC sampling: Uses a flexible Markov chain Monte Carlo (MCMC) sampling scheme for probabilistic inference.
• Maximum-likelihood estimation: Computes maximum-likelihood mutation histories from input mutation profiles.
• Posterior sampling: Samples from the posterior probability distribution of possible evolutionary paths.
• Error-rate estimation: Provides estimates of error rates associated with underlying sequencing experiments.
• Scalability and accuracy: Demonstrated scalability to contemporary single-cell sequencing technologies and superior reconstruction accuracy on real cancer datasets and simulation studies.

Scientific Applications:

• Tumor evolution reconstruction: Infers mutational histories and evolutionary trajectories of somatic (tumor) cells.
• Single-cell sequencing analysis: Analyzes single-cell somatic mutation profiles to resolve intratumoral heterogeneity.
• Method benchmarking: Enables performance evaluation and comparison on real cancer datasets and simulation studies.
• Cancer progression and target identification: Provides information relevant to studies of cancer progression and identification of candidate therapeutic targets.

Methodology:

SCITE applies a stochastic search algorithm with a flexible MCMC sampling scheme to compute maximum-likelihood mutation histories, sample from the posterior distribution of evolutionary paths, and estimate sequencing error rates.