Messenger RNA (mRNA) profiling can identify body fluids present in a stain, yielding information on what activities could have taken place at a crime scene. To account for uncertainty in such identifications, recent work has focused on devising statistical models to allow for probabilistic statements on the presence of body fluids. A major hurdle for practical adoption is that evidentiary stains are likely to contain more than one body fluid and current models are ill-suited to analyse such mixtures. Here, we construct a likelihood ratio (LR) system that can handle mixtures, considering the hypotheses H1: the sample contains at least one of the body fluids of interest (and possibly other body fluids); H2: the sample contains none of the body fluids of interest (but possibly other body fluids). Thus, the LR-system outputs an LR-value for any combination of mRNA profile and set of body fluids of interest that are given as input. The calculation is based on an augmented dataset obtained by in silico mixing of real single body fluid mRNA profiles. These digital mixtures are used to construct a probabilistic classification method (a 'multi-label classifier'). The probabilities produced are subsequently used to calculate an LR, via calibration. We test a range of different classification methods from the field of machine learning, ways to preprocess the data and multi-label strategies for their performance on in silico mixed test data. Furthermore, we study their robustness to different assumptions on background levels of the body fluids. We find logistic regression works as well as more flexible classifiers, but shows higher robustness and better explainability. We test the system's performance on lab-generated mixture samples, and discuss practical usage in case work.
翻译:送信者 RNA (mRNA) 剖析可以辨别污点中存在的体液, 并生成关于犯罪现场可能开展的活动的信息。 为了说明这种辨别中的不确定性, 最近的工作侧重于设计统计模型, 以允许对体液的存在进行概率化说明。 实际采用的一个主要障碍是, 证据污点可能包含不止一种体液, 而当前模型不适于分析这种混合物。 这里, 我们构建了一个可以处理混合物的可能性比率( LR) 系统, 考虑到假设 H1: 样本含有至少一种感兴趣的体液( 可能还有其他体液 ) ; H2: 样本没有包含任何感兴趣的体液( 但可能还有其他体液流体液) 。 因此, LR- 系统输出出一个LRR值, 任何组合的体液流体流体流体和当前模型都可能不适于分析这种混合物。 我们的计算基于通过精度混合而获得的更精度化的数据比值( LRRR), 这些数字混合物至少包含一种更精确的体液流体液( 可能包含一种更高的体液流体液流体液流体液); ; 这些数字混合物用于构建一个更具有更精确性的背景, 的机态的机能的机能的机能分析方法, 我们的机能的机能化的机能性能性能性能分析方法用来用来构建一个更精确性能性能化, 。