BiTSC2

BiTSC2 infers tumor clonal phylogenies from single-cell DNA sequencing by jointly analyzing single nucleotide variations (SNVs) and copy number alterations (CNAs) using Bayesian inference to characterize tumor subclonal architecture.

Key Features:

• Joint SNV and CNA analysis: Integrates single-cell SNV and CNA data to jointly infer clonal genotypes and phylogenies.
• Bayesian inference framework: Employs a Bayesian model to represent uncertainty in clonal structure and mutation histories.
• MCMC sampling: Uses Markov Chain Monte Carlo sampling to estimate posterior distributions of subclonal parameters.
• Input from scDNA-seq reads: Accepts raw single-cell DNA sequencing (scDNA-seq) data as input for analysis.
• Error and missing-data modeling: Explicitly models allelic dropout rates, sequencing errors, and missing data in scDNA-seq.
• Subclone genotype and assignment: Estimates subclonal copy-number and SNV genotype matrices and assigns single cells to subclones.
• Phylogeny reconstruction: Reconstructs tumor subclonal evolutionary trees representing clonal architecture.
• Robustness to low-depth data: Demonstrates robustness in genotype recovery, subclone assignment, and tree reconstruction for low sequencing depth and high variant missingness.

Scientific Applications:

• Intra-tumoral heterogeneity analysis: Characterizes subclonal composition and genetic diversity within tumors using single-cell SNV and CNA data.
• Tumor evolution and progression: Reconstructs subclonal evolutionary histories to study tumor progression and metastasis.
• Therapeutic resistance and precision oncology: Identifies subclonal genotypes associated with treatment resistance to inform targeted therapy and personalized medicine research.

Methodology:

Applies Bayesian inference with MCMC sampling to jointly integrate SNV and CNA data from scDNA-seq, model allelic dropout, sequencing errors, and missing data, estimate subclonal SNV and copy-number genotype matrices, assign cells to subclones, and reconstruct subclonal evolutionary trees.