Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.
Anal Chem. 2012 Jan 17;84(2):1063-9. doi: 10.1021/ac2026496. Epub 2011 Dec 28.
The analysis of cell types and disease using Fourier transform infrared (FT-IR) spectroscopic imaging is promising. The approach lacks an appreciation of the limits of performance for the technology, however, which limits both researcher efforts in improving the approach and acceptance by practitioners. One factor limiting performance is the variance in data arising from biological diversity, measurement noise or from other sources. Here we identify the sources of variation by first employing a high throughout sampling platform of tissue microarrays (TMAs) to record a sufficiently large and diverse set data. Next, a comprehensive set of analysis of variance (ANOVA) models is employed to analyze the data. Estimating the portions of explained variation, we quantify the primary sources of variation, find the most discriminating spectral metrics, and recognize the aspects of the technology to improve. The study provides a framework for the development of protocols for clinical translation and provides guidelines to design statistically valid studies in the spectroscopic analysis of tissue.
使用傅里叶变换红外（FT-IR）光谱成像分析细胞类型和疾病具有广阔的前景。然而，这种方法未能充分认识到技术性能的局限性，这限制了研究人员改进该方法的努力和该方法被从业者接受的程度。限制性能的一个因素是数据的变化，这些变化源于生物多样性、测量噪声或其他来源。在这里，我们首先使用高通量组织微阵列（TMA）采样平台来记录足够大和多样化的数据，从而确定了变化的来源。接下来，采用了一套全面的方差分析（ANOVA）模型来分析数据。通过估计可解释变异的部分，我们量化了主要的变异来源，找到了最具区分性的光谱指标，并认识到了改进技术的方面。该研究为临床转化的协议制定提供了框架，并为组织光谱分析中设计具有统计学意义的有效研究提供了指导。
