AMC

AMC performs clustering of mutations from single-cell DNA sequencing (scDNA-seq) data to infer genotypes and reconstruct phylogenetic relationships for analysis of intra-tumor heterogeneity.

Key Features:

• Mutation Clustering: Clusters mutations by identifying common states shared across single cells to capture intra-tumor heterogeneity.
• Efficiency and Accuracy: Improves computational efficiency and accuracy for large-scale scDNA-seq datasets.
• Principal Component Analysis (PCA): Uses PCA for dimensionality reduction of mutation data to highlight significant patterns.
• K-means Clustering: Applies K-means clustering to group mutations based on reduced-dimensional representations.
• Maximum Likelihood Estimation: Infers genotypes per cluster using maximum likelihood estimation.
• Phylogenetic Tree Reconstruction: Reconstructs phylogenetic trees from inferred genotypes to elucidate evolutionary relationships within tumor samples.

Scientific Applications:

• Cancer Genomics: Profiles intra-tumor heterogeneity and tumor evolution using mutation clusters derived from scDNA-seq data.
• Evolutionary Analysis: Infers phylogenetic relationships within tumors to study clonal evolution.
• Large-scale scDNA-seq Data Analysis: Processes large-scale single-cell DNA sequencing datasets for high-resolution mutation and genotype analyses.

Methodology:

Principal Component Analysis (PCA) for dimensionality reduction; K-means clustering to group mutations; maximum likelihood estimation to infer genotypes per cluster; and phylogenetic tree reconstruction from inferred genotypes.