Transcriptomic Data Normalization

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Abstract

High-throughput transcriptomic experiments that simultaneously measure the expression levels of thousands of genes have increasingly become the cornerstone of biological and biomedical research. The abundance of genomic data has facilitated holistic analysis of gene regulation, functions, and interactions by using various data-mining tools. However, before proceeding to any downstream analysis, the expression data needs to be normalized to minimize the impact of confounding factors on the estimation of biological variables of interest. Accordingly, this article focuses on data normalization strategies commonly used to capture biases specific to two major transcriptomics technologies, namely DNA microarray and RNA-seq.

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