Respiratory parameters change during post-natal development, but the nature of their changes have not been well-described. The advent of commercially available plethysmographic instruments provided improved repeatability of measurements and standardization of measured breathing in mice across laboratories. These technologies thus allowed for exploration of more precise respiratory pattern changes during the post-natal developmental epoch. Current methods to analyze respiratory behavior utilize plethysmography to acquire standing values of frequency, volume and flow at specific time points in murine maturation. These metrics have historically been independently analyzed as a function of time with no further analysis examining the interplay these variables have with each other and in the context of postnatal maturation or during blood gas homeostasis. We posit that machine learning workflows can provide deeper physiological understanding into the postnatal development of respiration. In this manuscript, we delineate a machine learning workflow based on the R-statistical programming language to examine how variation and relationships of frequency (f) and tidal volume (TV) change with respect to inspiratory and expiratory parameters. Our analytical workflows could successfully predict age and found that the variation and relationships between respiratory metrics are dynamically shifting with age and during hypercapnic breathing. Thus, our work demonstrates the utility of high dimensional analyses to provide reliable class label predictions using non-invasive respiratory metrics. These approaches may be useful in large-scale phenotyping across development and in disease.

呼吸参数在出生后发育过程中会发生变化，但其变化的性质尚未得到很好的描述。市售体积描记仪的出现提高了测量的可重复性和实验室小鼠测量呼吸的标准化。因此，这些技术允许在出生后发育时期探索更精确的呼吸模式变化。目前分析呼吸行为的方法是利用体积描记法在小鼠成熟的特定时间点获取频率、体积和流量的常设值。这些指标历来被独立分析为时间的函数，没有进一步分析检查这些变量彼此之间以及在出生后成熟或血气稳态期间的相互作用。我们假设机器学习工作流程可以提供对呼吸的出生后发展的更深入的生理理解。在这份手稿中，我们描述了基于 R 统计编程语言的机器学习工作流程，以检查频率 (f) 和潮气量 (TV) 的变化和关系如何随吸气和呼气参数变化。我们的分析工作流程可以成功预测年龄，并发现呼吸指标之间的变化和关系随着年龄和高碳酸血症呼吸而动态变化。因此，我们的工作证明了高维分析的效用，可以使用非侵入性呼吸指标提供可靠的类别标签预测。这些方法可能有助于跨越发育和疾病的大规模表型分析。