Group classification based on high-dimensional data: application to differential scanning calorimetry plasma thermogram analysis of cervical cancer and control samples

Shesh N Rai,^1,2 Jianmin Pan,¹ Alex Cambon,² Jonathan B Chaires,^3–5 Nichola C Garbett<sup>3,4

1</sup>Biostatistics Shared Facility, James Graham Brown Cancer Center, University of Louisville, ²Department of Bioinformatics and Biostatistics, University of Louisville, ³Biophysical Core Facility, James Graham Brown Cancer Center, University of Louisville, ⁴Department of Medicine, University of Louisville, ⁵Department of Biochemistry and Molecular Biology, University of Louisville, Louisville, KY, USA

Abstract: Differential scanning calorimetry has been applied to identify protein denaturation patterns, or thermograms, in blood plasma samples that are indicative of health status. Data sets generated by differential scanning calorimetry are high dimensional, and it is complex to analyze and classify thermogram patterns. The I-RELIEF method is commonly used for group classification from high-dimensional data sets, such as gene expression data. We report the development and validation of a new method of data reduction and modeling of high-dimensional data sets. The performance of our method was demonstrated through its application to the analysis of differential scanning calorimetry plasma thermogram data. Our method was found to provide superior classification performance compared with the I-RELIEF method.

Keywords: plasma thermogram, differential scanning calorimetry, group classification