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Microarray data and pathway analysis: example from the bench
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Microarray data and pathway analysis: example from the bench

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Microarray data and pathway analysis: example from the bench ...

Microarray data and pathway analysis: example from the bench
by drs. Jolien Vermeire - HIVlab, Department of Clinical Chemistry, Microbiology and Immunology – UGent

The increased availability and lower cost of gene expression microarrays has stimulated the use of transcriptome studies in a high variety of fields. Generating expression data at whole-genome level can indeed be a powerful method to characterize cellular pathways involved in a certain biological process. However, the challenge of extracting relevant biological information from such large datasets still prevents researchers from exploiting this tool. In this presentation I will share my personal experience, as a 'researcher non-bioinformatician', with performing microarray data and pathway analyses. I will give a general overview of the different steps that where followed in order to transform raw gene expression data, obtained in context of HIV research, into useful biological information and highlight different methods and software tools that helped me in this process.

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Microarray data and pathway analysis: example from the bench Microarray data and pathway analysis: example from the bench Presentation Transcript

  • Microarray data andpathway analysis:
    Examplefrom the bench
    Jolien Vermeire, HIVlab, UGent
    September 28th 2011, WOUD mini-symposium, Gent
  • Information from microarray experiments
    environmental stimuli
    pathogenic factor
    disease
    drug/therapy
    B
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    MOLECULAR MECHANISM
    PATHWAY ANALYSIS
    GENE EXPRESSION ANALYSIS:
    BIOMARKER
    (DRUG TARGET)
  • Information from microarray experiments
    HIV
    B
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    MOLECULAR MECHANISM ?
  • Key issues in micorarray analysis
    1. Experimental design
    2. Data analysis
    • Preprocessing raw data
    • Identification differentially
    expressed genes
    • Pathway analysis
    ! 4 biological replicates!
    CD4+ T cells
    3. Data validation
    Sort eGFP+
    RNA
    Illumina gene
    expression analysis
  • Preprocessing of raw expression data
    Raw intensity values
    expression values
    • Background correction
    • Summarization
    • Normalization: “adjusting for effects that arise from variation in technology”
    • Different methods: eg. quantile normalization,…
    • Different Software : Platform dependent!
    • Free: R/Bioconductor-packages : beadarray, affy,...
    RMA Express,…
    • Commercial : Genespring (Agilent) ®
    Affymetrix expression console software ®

  • Preprocessing of raw expression data
    Quantile normalization with the R/Bioconductor package Beadarray
    Bioconductor packages : use manuals!
  • MICROARRAY DATA MINING
    expression values
    biological data
    Genes with highest FOLD CHANGE
    Literature search of individual genes
    ???
    not successful
    Better approach:
    Broad statistical selection of differentially expressed genes
    Pathway analysis
  • Selection of differentially expressed genes
    Multitude of statistical tests available!
    eg. Statistical Analysis of Microarrays (SAM)
    Rank Product analysis (RP)
    NA7
    NL43
    NA7
    NL43
    More powerful for low number of replicates!
    29
    159
    15
    167
    73
    59
    - RP analysis with RankProd R/Bioconductor package
    - pfp: 0.05
    downregulated genes
    upregulated genes
    # downregulated genes: 203
    # upregulated genes: 299
  • Pathway analysis
    Principle :
    Identification
    pathway/functions
    overrepresented
    in your dataset
    Tools : multitude of free and commercial software packages!
    • Ingenuity Pathway analysis:
    Based on Ingenuity Knowledge Database
    • Database for Annotation, Visualization and Integrated Discovery (DAVID):
    Based on public available databases (KEGG, GO,…)
  • Pathway analysis
    http://david.abcc.ncifcrf.gov/
  • Pathway analysis
  • Pathway analysis
  • Microarray data mining …continued
    Literature-based selection of interesting pathways/ genes !!
    pathway
  • Conclusions
    Microarray data analysis requires…
    Statistics for differentially expressed gene identification
    and pathway analysis
    Appropriate software in each step of the process
    Literature search
    Time to LEARN and PERFORM the above
  • Acknowledgements
    Prof. Dr. Bruno Verhasselt
    Alessia Landi, PhD Student
    Veronica Iannucci, PhD Student
    Pieter Meuwissen, PhD Student
    Evelien Naessens, Lab technician
    Hanne Vanderstraeten, Lab technician
    Kathleen Van Landeghem, Lab technician
    Anouk Van Nuffel, PhD Student
    Wojciech Witkowski, PhD STudent
    Caroline Stevens, Master student
    Natasja Mortier, Master student
  • Rank product analysis
    • Rank product : geometric mean of rank of a gene
    • Significance?
    • random permutations of genes in each comparison
    • percentage of false positives (pfp)
    Pfp cut-off: 0.05