Exploratory data analysis of a clinical study group: Development of a procedure for exploring multidimensional data.

ABSTRACT: Thorough knowledge of the structure of analyzed data allows to form detailed scientific hypotheses and research questions. The structure of data can be revealed with methods for exploratory data analysis. Due to multitude of available methods, selecting those which will work together well and facilitate data interpretation is not an easy task. In this work we present a well fitted set of tools for a complete exploratory analysis of a clinical dataset and perform a case study analysis on a set of 515 patients. The proposed procedure comprises several steps: 1) robust data normalization, 2) outlier detection with Mahalanobis (MD) and robust Mahalanobis distances (rMD), 3) hierarchical clustering with Ward's algorithm, 4) Principal Component Analysis with biplot vectors. The analyzed set comp

SUBMITTER: Konopka BM 

PROVIDER: S-EPMC6107146 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature