Statistical Analysis of IMS Dataset with ClinproTool Software

Abstract

The procedures of performing principal component analysis (PCA) (1) and data mining method for biomarker discovery (2) using ClinProTools 2.1 software (Bruker Daltonics) are described. (1) First, PCA of an IMS dataset is described. PCA is widely used in many fields, such as biology, medicine, pharmacy, and economics. PCA is a statistical method used to reduce multidimensional data sets to lower dimensions, and the results of PCA are generally discussed in terms of component scores and loadings (see Sect. 11.1). (2) Second, the data mining method from an IMS dataset is described. Biomarkers are objective indicators of particular pathogenic processes, pharmacological responses, or normal biological states and can be any kind of molecule in living organisms. Biomarkers are essential for the diagnosis and prediction of diseases, for example, mass screening for newborn infants using biomarkers is performed in many countries. The accurate and effective processing of complicated data is needed for biomarker discovery (see Sect. 11.2).