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

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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