Biomarkers Discovery through Multivariate Statistical Methods: A Review of Recently Developed Methods and Applications in Proteomics

Abstract

Biomarkers discovery is a discipline achieving increasing importance since it provides diagnostic/prognostic markers and may permit to investigate and understand the mechanism of development of the pathology, possibly suggesting new biomolecular therapeutic targets. Biomarkers discovery in proteomics is hampered by the use of high-throughput techniques providing a great number of candidates among which the true biomarkers have to be searched for. Moreover, often a small number of samples are available. Two main problems arise when biomarkers have to be searched for in such datasets: 1) the identification of reliable markers, avoiding false positives due to chance correlations; 2) the exhaustive identification of all candidate markers, to obtain a complete snapshot of the effect investigated. Biomarkers can be identified by two approaches: classical monovariate methods, where each biomarker is considered as independent (Student's t-test, Mann-Whitney test etc.) or multivariate methods, able to take into consideration the correlation structure of the data (i.e. interactions). These last ones are certainly to be preferred and should achieve the best compromise between the best predictive ability (accomplished through the use of variable selection procedures and exhaustivity. Here, we review the most recent applications of multivariate methods for the identification of biomarkers in proteomics with particular regard to the statistical methods exploited.

Metabolic fingerprinting for diagnosis of fibromyalgia and other rheumatologic disorders

“…space that indicates the distance between clusters. A class distance greater than 3.0 is regarded as significant to differentiate two groups of samples as different classes (35,36). ICDs were largely independent of one another with ICD values of 4.5 (FM and RA), 4.0 (FM and SLE), and 3.5 (RA and SLE) and no class overlapping (Table 4).…”

“…2) identified the most discriminating variables (i.e. candidate biomarkers) between classes; the greater the discrimination power, the more a variable influences the classification (35). Major IR bands responsible for the grouping of the three classes were in the region of 1700 and 1400 cm Ϫ1 , corresponding to amide I (1640 cm Ϫ1 ) and amide II (1555 cm Ϫ1 ) vibrations and contributions from both lipids and proteins (␦ as CH 2 and CH 3 ).…”

Transcriptional Profiling of Whisker Follicles and of the Striatum in Methamphetamine Self-Administered Rats

“…Multivariate analysis is a useful statistical technique for understanding the structure and relations of a dataset and for evaluating a biomarker candidate with respect to various experimental measurements [20,21]. As multivariate analyses, PCA and correlation analysis were conducted here using the MetaboAnalyst v4.0 (http://www.metaboanalyst.ca) online tool.…”

Toward reliable biomarker signatures in the age of liquid biopsies - how to standardize the small RNA-Seq workflow

“…small RNA) as independent, they are unable to capture the complete reality of highly multivariate (variables >> observations) and correlated datasets such as NGS read counts. By taking the synergies, antagonisms and redundancy inherent in each NGS dataset into consideration, multivariate analyses can reach much higher discriminative power and separate noise from signal ( 19 , 220 ). In reality, there will most likely be no single valid transcriptional biomarker for the physiological situation of interest.…”