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Bioinformatic Analysis of Epidemiological and Pathological Data

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Pathology and Epidemiology of Cancer

Abstract

Analysis of high-throughput genomic data is challenging and requires specialized knowledge of experimental design, genomic data preprocessing and quality control, high-dimensional data analysis, and machine learning. Each research project involving high-throughput genomic data is unique, and there is no recipe for data analysis that will fit every project and research question. In this chapter, we will introduce concepts of designing genomic experiments, basic principles of bioinformatic analysis of high-throughput genomic data, and discuss best practices for design and reproducibility of computational analyses. Our main purpose is to introduce the basic concepts of planning successful genomic studies using analysis of gene expression data as an example in order to facilitate communication with the statisticians and bioinformaticians who will be involved in designing the studies and data analysis. We will also briefly introduce statistical and bioinformatic methods commonly used for the analysis of high-throughput genomic data to help the readers follow computational analyses in the cancer research literature.

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Author information

Authors and Affiliations

  1. Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA

    Svitlana Tyekucheva & Giovanni Parmigiani

  2. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Svitlana Tyekucheva & Giovanni Parmigiani

Authors
  1. Svitlana Tyekucheva
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  2. Giovanni Parmigiani
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Corresponding author

Correspondence to Svitlana Tyekucheva .

Editor information

Editors and Affiliations

  1. Center for Molecular Oncologic Pathology, Dana Farber Cancer Institute, Boston, Massachusetts, USA

    Massimo Loda

  2. Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA

    Lorelei A. Mucci

  3. Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA

    Megan L. Mittelstadt

  4. King’s College London, Guy’s Hospital, London, United Kingdom

    Mieke Van Hemelrijck

  5. Department of Pathology, Dana-Farber Cancer Institute and Brigham & Women’s Hospital, Boston, Massachusetts, USA

    Maura Bríd Cotter

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Tyekucheva, S., Parmigiani, G. (2017). Bioinformatic Analysis of Epidemiological and Pathological Data. In: Loda, M., Mucci, L., Mittelstadt, M., Van Hemelrijck, M., Cotter, M. (eds) Pathology and Epidemiology of Cancer. Springer, Cham. https://doi.org/10.1007/978-3-319-35153-7_8

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  • DOI: https://doi.org/10.1007/978-3-319-35153-7_8

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