Statistical approaches to candidate biomarker panel selection

Heidi M. Spratt, Hyunsu Ju

Research output: Chapter in Book/Report/Conference proceedingChapter

5 Scopus citations


The statistical analysis of robust biomarker candidates is a complex process, and is involved in several key steps in the overall biomarker development pipeline (see Fig. 22.1, Chap. 19). Initially, data visualization (Sect. 22.1, below) is important to determine outliers and to get a feel for the nature of the data and whether there appear to be any differences among the groups being examined. From there, the data must be pre-processed (Sect. 22.2) so that outliers are handled, missing values are dealt with, and normality is assessed. Once the processed data has been cleaned and is ready for downstream analysis, hypothesis tests (Sect. 22.3) are performed, and proteins that are differentially expressed are identified. Since the number of differentially expressed proteins is usually larger than warrants further investigation (50+ proteins versus just a handful that will be considered for a biomarker panel), some sort of feature reduction (Sect. 22.4) should be performed to narrow the list of candidate biomarkers down to a more reasonable number. Once the list of proteins has been reduced to those that are likely most useful for downstream classification purposes, unsupervised or supervised learning is performed (Sects. 22.5 and 22.6, respectively).

Original languageEnglish (US)
Title of host publicationAdvances in Experimental Medicine and Biology
PublisherSpringer New York LLC
Number of pages30
StatePublished - 2016

Publication series

NameAdvances in Experimental Medicine and Biology
ISSN (Print)0065-2598
ISSN (Electronic)2214-8019


  • Candidate biomarker selection
  • Data clustering
  • Data consistency
  • Data inspection
  • Data normalization
  • Data transformations
  • Machine learning
  • Outlier detection

ASJC Scopus subject areas

  • General Biochemistry, Genetics and Molecular Biology


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