Challenges in Biomarker Discovery: Combining Expert Insights with Statistical Analysis of Complex Omics Data

Abstract

INTRODUCTION: The advent of high throughput technologies capable of comprehensive analysis of genes, transcripts, proteins and other significant biological molecules has provided an unprecedented opportunity for the identification of molecular markers of disease processes. However, it has simultaneously complicated the problem of extracting meaningful molecular signatures of biological processes from these complex datasets. The process of biomarker discovery and characterization provides opportunities for more sophisticated approaches to integrating purely statistical and expert knowledge-based approaches. AREAS COVERED: In this review we will present examples of current practices for biomarker discovery from complex omic datasets and the challenges that have been encountered in deriving valid and useful signatures of disease. We will then present a high-level review of data-driven (statistical) and knowledge-based methods applied to biomarker discovery, highlighting some current efforts to combine the two distinct approaches. EXPERT OPINION: Effective, reproducible and objective tools for combining data-driven and knowledge-based approaches to identify predictive signatures of disease are key to future success in the biomarker field. We will describe our recommendations for possible approaches to this problem including metrics for the evaluation of biomarkers.

Figure 1

PCA-based visualization of a proteomics data biomarker study. (A) PCA was performed on a proteomics dataset with a simple limit-of-detection based imputation of missing data and (B) no imputation using Sequential Projection Pursuit. The axes in both plots represent the first principal component (X axes) and the second principal component (Y axes).

Figure 2

A. Coexpression network of macrophage response to nanoparticle exposure showing the progression of functional modules through time. B. Standard heatmap of macrophage transcriptional response to nanoparticle exposure with modules defined using hierarchical clustering.