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Transcriptome Data Analysis pp 127–138Cite as

Microarray-Based MicroRNA Expression Data Analysis with Bioconductor

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 1751))

Abstract

MicroRNAs (miRNAs) are small, noncoding RNAs that are able to regulate the expression of targeted mRNAs. Thousands of miRNAs have been identified; however, only a few of them have been functionally annotated. Microarray-based expression analysis represents a cost-effective way to identify candidate miRNAs that correlate with specific biological pathways, and to detect disease-associated molecular signatures. Generally, microarray-based miRNA data analysis contains four major steps: (1) quality control and normalization, (2) differential expression analysis, (3) target gene prediction, and (4) functional annotation. For each step, a large couple of software tools or packages have been developed. In this chapter, we present a standard analysis pipeline for miRNA microarray data, assembled by packages mainly developed with R and hosted in Bioconductor project.

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Author information

Authors and Affiliations

  1. Systemomics Center, College of Pharmacy, Harbin Medical University, Harbin, China

    Emilio Mastriani & Songling Zhu

  2. Genomics Research Center (State-Province Key Laboratories of Biomedicine-Pharmaceutics of China), Harbin Medical University, Harbin, China

    Emilio Mastriani & Songling Zhu

  3. School of Public Health, Shenzhen University Health Science Center, Shenzhen, China

    Rihong Zhai

Authors
  1. Emilio Mastriani
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  2. Rihong Zhai
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  3. Songling Zhu
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Editor information

Editors and Affiliations

  1. Department of Cell Biology and Genetics, School of Basic Medicine, Shenzhen University Health, Science Center, Shenzhen, China

    Yejun Wang

  2. Epigenomics and Computational Biology Lab, Biocomplexity Institute of Virginia Tech, Blacksburg, Virginia, USA

    Ming-an Sun

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Mastriani, E., Zhai, R., Zhu, S. (2018). Microarray-Based MicroRNA Expression Data Analysis with Bioconductor. In: Wang, Y., Sun, Ma. (eds) Transcriptome Data Analysis. Methods in Molecular Biology, vol 1751. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7710-9_9

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  • DOI: https://doi.org/10.1007/978-1-4939-7710-9_9

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7709-3

  • Online ISBN: 978-1-4939-7710-9

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