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

Single-cell genomics offers novel insights into genomic heterogeneity within cell populations, reframing our understanding of human development, tumorigenesis, and aging. However, constrained by the picogram-scale DNA templates of individual cells, Whole-Genome Amplification (WGA) remains a necessary precondition. Current quality control frameworks primarily focus on amplification uniformity but fail to capture the molecular independence of DNA amplicons, leading to an overestimation of information content in redundant WGA libraries. Here, we propose the Depth of Independent Amplicons Gauge (DIAG) to accurately quantify the effective number of amplicons derived from the primary template. The robustness of the DIAG was first validated using in silico datasets, revealing that the Depth of Independent Amplicons (DIA) is directly coupled with the precision and specificity of mutation calling. Furthermore, we established an organoid-derived ground-truth to evaluate mutation fidelity in real biological contexts, confirming the practical utility of the DIAG. Our results demonstrate that the DIAG provides a high-fidelity assessment of an individual WGA library without the need for costly external experiments, especially in Single-Nucleotide Variant (SNV) calling. Furthermore, we revealed that traditional uniformity indices, like the Gini index or Kullback-Leibler (KL) divergence, exhibit incongruous fluctuations under down-sampling perturbations. In contrast, the DIA remains a robust and high-fidelity predictor of mutational accuracy, maintaining stability across varying sequencing strategies. Finally, we conducted a systematic comparison of current single-cell Whole-Genome Amplification (scWGA) strategies, providing a standardized benchmarking of diverse technologies for high-resolution single-cell mutation analysis.