MAGOS

MAGOS infers subclonal compositions from tumor sequencing data to decompose intratumor heterogeneity using a model-based adaptive grouping approach that operates on standard-depth (30–50×) sequencing.

Key Features:

• Adaptive Error Model: Incorporates an adaptive error model that corrects the mean–variance dependency inherent in sequencing data at subclonal levels.
• Statistical Decomposition: Employs a model-based statistical decomposition method to delineate mixed subclonal populations within tumor samples.
• Performance and Efficiency: Demonstrates higher accuracy in subclone discovery and improved computational efficiency in simulations and real-world comparisons, reducing minimum sequencing depth requirements compared with methods that require >300× depth.
• Versatility in Data Analysis: Supports analysis of single nucleotide variants (SNVs) and copy number variants (CNVs) from one or multiple tumor samples and is applicable to whole-exome sequencing data.

Scientific Applications:

• Liver cancer subclonal analysis: Applied to whole-exome sequencing of 331 liver cancer samples and identified an association between subclonal diversity and patient overall survival.

Methodology:

Integrates an adaptive error model into a statistical decomposition framework to correct mean–variance dependencies in sequencing data and infer subclonal compositions from standard-depth (30–50×) sequencing using SNVs and CNVs.