DIAG: A Framework for Evaluating Whole-Genome Amplification Quality in Single-Cell SNV Analysis

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Single-cell genomics reveals cellular heterogeneity crucial for understanding development, cancer, and aging.
• Whole-Genome Amplification (WGA) is essential for analyzing picogram-scale DNA from single cells.
• Existing quality control for WGA libraries overestimates information due to unaddressed amplicon redundancy.

Purpose of the Study:

• To develop a novel metric, the Depth of Independent Amplicons Gauge (DIAG), for accurately quantifying effective amplicons in WGA libraries.
• To establish DIAG as a reliable quality control measure for single-cell whole-genome amplification (scWGA) data.
• To benchmark various scWGA strategies using the proposed DIAG metric.

Main Methods:

• Development and validation of the DIAG metric using in silico datasets.
• Utilizing an organoid-derived ground-truth dataset to assess mutation fidelity.
• Comparative analysis of DIAG against traditional uniformity indices (Gini, KL divergence) under down-sampling.
• Systematic comparison of different scWGA strategies.

Main Results:

• The Depth of Independent Amplicons (DIA) directly correlates with the precision and specificity of mutation calling.
• DIAG provides a high-fidelity assessment of WGA libraries without external experiments, particularly for Single-Nucleotide Variant (SNV) calling.
• DIA demonstrates robustness and stability across varying sequencing strategies, unlike traditional uniformity indices.
• A standardized benchmarking of scWGA technologies was established.

Conclusions:

• DIAG offers a superior method for evaluating scWGA library quality, enhancing the accuracy of single-cell mutation analysis.
• The DIA metric provides a more reliable assessment of data quality compared to conventional uniformity measures.
• This work facilitates standardized comparisons of scWGA methods for high-resolution mutation detection.