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Comprehensive Workflow for Integrative Transcriptomics Meta-Analysis

A. Nehme,F. Mazurier,K. Zibara

Published 2019 in Leveraging Biomedical and Healthcare Data

ABSTRACT

Abstract Large sets of transcriptomic data are available on databases, which offer the ability for reanalysis through metaanalysis and data integration. The latter approach was shown to improve statistical robustness by increasing the number of samples being analyzed. However, horizontal data integration requires careful data preparation to exclude nonbiological variations between studies, which may raise the probability of false discovery. Transcriptomic data preparation comprises 4 main steps that include alternative methods with different levels of sensitivity. In this chapter, we describe the methods used at each level and the various approaches employed to evaluate their performance before data analysis.

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