This article reports on the research methodology and findings from a study to generate and interpret data from single-cell analyses, and it discusses the process of identifying, analyzing, and resolving sources of inconsistencies for the data analysis.
Recent developments in single-cell analysis have revolutionized basic research and have garnered the attention of the forensic domain. Though single-cell analysis is not new to forensics, the ways in which these data can be generated and interpreted are. Modern interpretation strategies report likelihood ratios that rely on a model of the world that is a simplification of it. It is, therefore, plausible that different reasonable models will assign noticeably different weights of evidence (WoEs) to some of these data, resulting in inconsistent reports and protracted reviews of that evidence, potentially across years. With one goal of research being to identify and understand sources of inconsistencies during early stages, the authors undertake a study that evaluates WoE at the limit of one single-cell electropherogram (scEPG) across three architecturally distinct probabilistic models. The three are named EESCIt (Evidentiary Evaluation of Single Cells), TD (Top-Down), and DCM (Discrete Cell Model). To do this, they performance test the three models on a set of 996 individual scEPGs and conduct one H1-true, i.e., true contributor, and 201 H2-true, i.e., false contributor, tests, per scEPG. With the 201,192 outcomes per model, they confirm that scEPGs well resolve the hypotheses, regardless of what model was applied. The authors’ findings show that all three models appropriately stated WoEs for scEPGs when reporting positive WoE, and the two continuous model’s WoE reasonably represented the findings when WoE < -1 for most loci. To further explore, the authors continued with paired analyses that evaluated the agreement in WoE, per scEPG, across models. Unlike unpaired analyses, this evaluation determines if well performing models return equivalent results for the same scEPG. To ameliorate differences in predicting rare, though impactful, events they proffer interpretive adaptions that extend beyond manually addressing the phenomena. With the WoE being calibrated within their relevant regions across EESCIt, TD and DCM, the authors categorize each as meeting the pillar of legitimacy for single-cell data within their intended WoE ranges. (Published Abstract Provided)