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Effective Removal of Noisy Data Via Batch Effect Processing

  • Ryan G. BentonEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1617)

Abstract

In order to have faith in the analysis of data, a key factor is to have confidence that the data is reliable. In the case of microRNA, reliability includes understanding the collection methods, ensuring that the analysis is appropriate, and ensuring that the data itself is accurate. A key element in ensuring data accuracy is the removal of noise. While there can be several sources of noise, a common source of noise is the batch effect, which can be defined as systematic variability in the data caused by non-biological factors. This chapter will present various techniques designed to remove variability caused by batch effects and the potential effectiveness.

Key words

MicroRNA Batch effects Normalization Knowledge Discovery in Databases Noise Removal 

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Copyright information

© Springer Science+Business Media LLC 2017

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of South Alabama School of ComputingMobileUSA

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