Multi-group diagnostic classification of high-dimensional data using differential scanning calorimetry plasma thermograms

• Published in: PloS one Vol. 14; no. 8; p. e0220765
• Main Authors: Rai, Shesh N., Srivastava, Sudhir, Pan, Jianmin, Wu, Xiaoyong, Rai, Somesh P., Mekmaysy, Chongkham S., DeLeeuw, Lynn, Chaires, Jonathan B., Garbett, Nichola C.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 20.08.2019; Public Library of Science (PLoS)
• Subjects: Adolescent; Adult; Aged; Aged, 80 and over; Bioinformatics; Biology and Life Sciences; Biopolymer denaturation; Blood plasma; Blood Proteins - chemistry; Calorimetry; Calorimetry, Differential Scanning - methods; Care and treatment; Cervical cancer; Cervix; Classification; Comparative analysis; Computer and Information Sciences; Consent; Data points; Data reduction; Datasets; Demographics; Diagnostic systems; Differential scanning calorimetry; Disease; Female; Generalized linear models; Health care; Heat measurement; Humans; Lung cancer; Lung diseases; Lung Neoplasms - blood; Lung Neoplasms - diagnosis; Male; Medical diagnosis; Medicine; Medicine and Health Sciences; Methods; Middle Aged; People and Places; Physical Sciences; Protein Denaturation; Regression Analysis; Review boards; Statistical methods; Uterine Cervical Neoplasms - blood; Uterine Cervical Neoplasms - diagnosis; Young Adult; Louisville Kentucky; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0220765

The thermoanalytical technique differential scanning calorimetry (DSC) has been applied to characterize protein denaturation patterns (thermograms) in blood plasma samples and relate these to a subject's health status. The analysis and classification of thermograms is challenging because of the high-dimensionality of the dataset. There are various methods for group classification using high-dimensional data sets; however, the impact of using high-dimensional data sets for cancer classification has been poorly understood. In the present article, we proposed a statistical approach for data reduction and a parametric method (PM) for modeling of high-dimensional data sets for two- and three- group classification using DSC and demographic data. We compared the PM to the non-parametric classification method K-nearest neighbors (KNN) and the semi-parametric classification method KNN with dynamic time warping (DTW). We evaluated the performance of these methods for multiple two-group classifications: (i) normal versus cervical cancer, (ii) normal versus lung cancer, (iii) normal versus cancer (cervical + lung), (iv) lung cancer versus cervical cancer as well as for three-group classification: normal versus cervical cancer versus lung cancer. In general, performance for two-group classification was high whereas three-group classification was more challenging, with all three methods predicting normal samples more accurately than cancer samples. Moreover, specificity of the PM method was mostly higher or the same as KNN and DTW-KNN with lower sensitivity. The performance of KNN and DTW-KNN decreased with the inclusion of demographic data, whereas similar performance was observed for the PM which could be explained by the fact that the PM uses fewer parameters as compared to KNN and DTW-KNN methods and is thus less susceptible to the risk of overfitting. More importantly the accuracy of the PM can be increased by using a greater number of quantile data points and by the inclusion of additional demographic and clinical data, providing a substantial advantage over KNN and DTW-KNN methods.