Adv Exp Med Biol. 2026;1504:205-224. doi: 10.1007/978-3-032-18966-0_10.
ABSTRACT
Omics data, comprising a diverse array of high-throughput molecular datasets, present substantial statistical challenges due to their intrinsic heterogeneity and variability. Effectively distinguishing biologically meaningful variations from random noise requires the application and development of robust statistical approaches. Interdisciplinary collaboration plays a pivotal role in refining these methodologies and enhancing the understanding of intricate biological systems. This chapter reviews the importance of statistical methods in omics data analysis, highlighting the need for ongoing advancements to address key challenges, including experimental design, preprocessing, dimensionality reduction, statistical modeling of complex datasets, and the interpretation of results. The pursuit of improved reliability in biological insights creates opportunities for the development and refinement of advanced statistical methodologies.
PMID:42071146 | DOI:10.1007/978-3-032-18966-0_10
