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  • Ten quick tips for avoiding pitfalls in multi-omics data . . .
    To avoid these and other pitfalls and common mistakes, we propose this study where we describe a few guidelines to keep in mind when performing multi-omics data integration
  • Multi-omics data integration considerations and study design . . .
    In this review, we discuss considerations of the study design for each data feature, the limitations in gene and protein abundance and their rate of expression, the current data integration methods, and microbiome influences on gene and protein expression
  • Strategies, Challenges, and Solutions in Multi-Omics Data . . .
    We will consider statistical models, multilayered strategies, data gathering procedures, and methods for unifying this data Additionally, we will cover the issues and remedies surrounding missing data, concluding with a detailed overview of value replacement techniques
  • A Guide to Multi-omics Integration Strategies
    Integrating multi-omics data can reveal new cell subtypes, cell interactions and interactions between the different omic layers leading to gene regulatory and phenotypic outcomes Since each omic layer is causally tied to the next, multi-omics integration serves to disentangle this relationship to properly capture cell phenotype (Figure 1)


















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