Statistical Approaches to Analyzing Breath Sample Data in order to Determine Disease Status

Picomole公司开发了一种非侵入性呼吸分析设备，可以通过**识别呼吸中的化合物来检测肺部疾病。我们将证明，这种设备具有灵敏度和**特异性，到目前为止，只有通过更具侵入性的手术才能获得。它基于使用连续波腔衰荡光谱(CW-CRDS)获得的对中红外(MID-IR)区域化合物的吸收**测量。**我们将为呼气样本数据开发功能数据分析(FDA)技术，以区分**健康受试者和患有肺部疾病(例如肺癌)的受试者。FDA非常适合可以**表示为函数或曲线的数据(例如光谱数据)。泛函线性判别分析(FLDA)是经典统计**分类技术(线性判别分析)的**扩展，可以处理函数**数据集所特有的问题。当应用于Picomole提供的伪造的呼气样本数据时，FLDA完美地**区分了肺癌患者和非肺癌患者。我们**将探索的另一种分类技术是DDG图(**G组的深度与深度图)分类器。DDG曲线图分类器可将任何统计分类方法应用于数据深度测量。**数据深度测量量化观测相对于其基本分布的“深度”。DDG Plot**分类器已经应用于其他光谱数据集，取得了良好的效果，在这里显示出了良好的前景。我们的**方法将很容易扩展到其他感兴趣的疾病状态的分类。**Dalhousie研究人员将每月访问Picomole在蒙克顿的设施，以批判性地评估**正在进行的数据收集和处理方法，以及交流建模结果。皮科莫尔会

Picomole Inc. has developed a non-invasive breath analysis device that can detect pulmonary diseases by**identifying chemical compounds in the breath. We will demonstrate that this device has a sensitivity and**specificity that can so far only be obtained with more invasive surgeries. It is based on absorption**measurements of compounds in the mid-infrared (mid-IR) region obtained using Continuous Wave Cavity**Ring-Down Spectroscopy (CW-CRDS).**We will develop Functional Data Analysis (FDA) techniques for breath sample data that will distinguish**healthy subjects from those with pulmonary disease (e.g. lung cancer). FDA is ideally suited to data that can be**represented as functions or curves (e.g. spectroscopy data). Functional Linear Discriminant Analysis (FLDA) is**an extension of a classical statistical**classification technique (Linear Discriminant Analysis) and can deal with problems unique to functional**datasets. When applied to fabricated breath sample data made available by Picomole, FLDA perfectly**distinguished between subjects with lung cancer and those without. Another classification technique that we**will explore is DDG plot (Depth vs. Depth plots for**G groups) classifiers. DDG plot classifiers apply any statistical classification method to measures of data depth.**Data depth measures quantify how "deep" an observation is in relation to its underlying distribution. DDG plot**classifiers have been applied to other spectrometry datasets with good results and show promise here. Our**methods will be readily extendible to the classification of other disease statuses of interest.**Dalhousie researchers will make monthly visits to Picomole's facilities in Moncton to critically appraise**ongoing data collection and processing methods as well as to communicate modelling results. Picomole will