A review on multi-omics integration for aiding study design of large scale TCGA cancer datasets.
Journal:
BMC genomics
Published Date:
Aug 22, 2025
Abstract
BACKGROUND: Rapid advancements in high-throughput sequencing technologies allow for detailed and accurate measurement of omics features within their biological context. The integration of different omics types creates heterogeneous datasets, presenting challenges in analysis due to variations in measurement units, sample numbers, and features. Currently, there is a lack of generalized guidelines for making decisions in multi-omics study design (MOSD), such as selecting an appropriate number of samples and features, type of preprocessing and integration for robust analysis results. We propose a suggestive guideline for MOSD, involving nine important factors: sample size, feature selection, preprocessing strategy, noise characterization, class balance, number of classes, cancer subtype combination, omics combination, and clinical features.
