Salience, legitimacy, and credibility of two end-to-end forensic single cell probabilistic systems

In cases for which there is no suspect, national forensic databases provide a mechanism by which to generate investigatory leads. National forensic DNA databases, however, have restrictions on what data to load. For example, uploading inferred alleles from DNA data that is a mixture of more than two contributors may be disallowed, leading to unresolved cases. A single-cell strategy has the potential to overcome the mixture gap by isolating each cell at the front-end of the pipeline. Once DNA signatures from each cell are obtained, they are clustered into groups. This is followed by asserting the probability we observe the data in the cluster had a person carrying genotype g donated. On applying Bayes' Rule, we obtain the probability of a genotype given the data in a cluster and model. If this probability is near one, it means that only one genotype reasonably explains the data and this genotype can be used in a national database query. Good clustering, therefore, is an invaluable step in single-cell forensic interpretation and it is for this reason we examine the fortitude of two clustering approaches - i.e., model-based clustering (MBC) and forensic-aware clustering (FAC) - within an end-to-end single-cell predictor named EESCIt™. Using proper scoring rules, we report the performance of our probabilistic single-cell evaluator and structure the analytics into categories of Salience, Legitimacy and Credibility (SLC). With Salience referring to the applicability of a technology to meet an actor's needs, we begin by discussing the relevance of single cell reports to forensic actors. Regarding Legitimacy, we determined the proportion of admixtures giving correct and incorrect cluster numbers and found that FAC returned correct cluster numbers for all admixtures tested. With improved clustering, 90 % of the loci returned only one credible genotype and it was the correct one, which improves on MBC's 84 %. We then examined the Brier Score and decomposed it into calibration and refinement. We show that the FAC-centered system returned better calibration scores than the MBC one, which was driven by its improved clustering performance. Regarding Credibility, we found that the FAC-based system also returned better refinement scores. With FAC being more Legitimate and Credible than an MBC system for single-cell forensics, we adopt it into EESCIt™, therein creating the first end-to-end single-cell probabilistic system able to address single-cell queries about how many donors there were, and who they were.