Bioinformatic Analysis of Epidemiological and Pathological Data View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2017

AUTHORS

Svitlana Tyekucheva , Giovanni Parmigiani

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. More... »

PAGES

91-104

References to SciGraph publications

  • 2008-12. Identifying differential correlation in gene/pathway combinations in BMC BIOINFORMATICS
  • 2013-04. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions in GENOME BIOLOGY
  • 2014-02. voom: precision weights unlock linear model analysis tools for RNA-seq read counts in GENOME BIOLOGY
  • 2009-11. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1 in NATURE
  • 2010-05. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation in NATURE BIOTECHNOLOGY
  • 2011-12. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome in BMC BIOINFORMATICS
  • 2009-12. A general modular framework for gene set enrichment analysis in BMC BIOINFORMATICS
  • 2008-07. Mapping and quantifying mammalian transcriptomes by RNA-Seq in NATURE METHODS
  • 2014-12. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 in GENOME BIOLOGY
  • 2011-01. Retraction: Genomic signatures to guide the use of chemotherapeutics in NATURE MEDICINE
  • 2011-12. GC-Content Normalization for RNA-Seq Data in BMC BIOINFORMATICS
  • 2010-10. Tackling the widespread and critical impact of batch effects in high-throughput data in NATURE REVIEWS GENETICS
  • 2006-08. Principal components analysis corrects for stratification in genome-wide association studies in NATURE GENETICS
  • 2010-12. Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments in BMC BIOINFORMATICS
  • 2004-09. Bioconductor: open software development for computational biology and bioinformatics in GENOME BIOLOGY
  • Book

    TITLE

    Pathology and Epidemiology of Cancer

    ISBN

    978-3-319-35151-3
    978-3-319-35153-7

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-35153-7_8

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    http://dx.doi.org/10.1007/978-3-319-35153-7_8

    DIMENSIONS

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