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The (in)dependence of single-cell data inferences on model constructs.

Catherine M Grgicak1, Klaas Slooten2, Robert G Cowell3

  • 1Program in Forensic Sciences, Department of Chemistry, Rutgers University, Camden, NJ 08102, USA; Center for Computational and Integrative Biology, Rutgers University, Camden, NJ 08102, USA.

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Summary

This study compares three probabilistic models for interpreting single-cell electropherograms (scEPGs) in forensics. All models effectively resolve hypotheses, with weights of evidence (WoEs) showing strong agreement across models, indicating robust forensic applicability.

Keywords:
DCMEESCItForensic DNALR calibrationLikelihood ratioProbabilistic genotypingSingle-cell forensicsSingle-cell geneticsSingle-cell inferenceTD

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Area of Science:

  • Forensic Science
  • Genetics
  • Biostatistics

Background:

  • Single-cell analysis offers advanced forensic insights, but interpretation models can yield varied evidence weights (WoEs).
  • Understanding model discrepancies is crucial for consistent forensic evidence review.

Purpose of the Study:

  • To evaluate and compare the Weights of Evidence (WoEs) generated by three distinct probabilistic models for single-cell electropherograms (scEPGs).
  • To identify sources of inconsistency in forensic single-cell data interpretation across different models.

Main Methods:

  • Performance testing of three models (EESCIt, TD, DCM) on 996 scEPGs with true and false contributor tests.
  • Analysis of 201,192 outcomes per model to assess hypothesis resolution and WoE calibration.
  • Paired analyses using intraclass correlations to evaluate inter-model agreement for scEPG WoEs.

Main Results:

  • All three models effectively resolved hypotheses for scEPGs.
  • Weights of Evidence (WoEs) showed strong agreement across models, with intraclass correlations ≥ 0.99997.
  • Model calibration was generally accurate, with rare discrepancies linked to extreme stutter signals in specific scEPGs.

Conclusions:

  • The evaluated models (EESCIt, TD, DCM) appropriately state WoEs for scEPGs within their relevant ranges.
  • Findings support the legitimacy of single-cell data interpretation in forensics across these models.
  • Proposed interpretive adaptations can ameliorate differences in predicting rare events.