Analysis of Variance in
Spectroscopic Imaging Data
from Human Tissues
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Abstract
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