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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
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

  1. 1. Microarray data andpathway analysis:<br />Examplefrom the bench<br />Jolien Vermeire, HIVlab, UGent<br />September 28th 2011, WOUD mini-symposium, Gent<br />
  2. 2. Information from microarray experiments<br />environmental stimuli<br />pathogenic factor<br />disease<br />drug/therapy<br />B<br />I<br />O<br />L<br />O<br />G<br />I<br />C<br />A<br />L<br />P<br />R<br />O<br />C<br />E<br />S<br />S<br />MOLECULAR MECHANISM <br />PATHWAY ANALYSIS<br />GENE EXPRESSION ANALYSIS:<br />BIOMARKER<br />(DRUG TARGET)<br />
  3. 3. Information from microarray experiments<br />HIV<br />B<br />I<br />O<br />L<br />O<br />G<br />I<br />C<br />A<br />L<br />P<br />R<br />O<br />C<br />E<br />S<br />S<br />MOLECULAR MECHANISM ? <br />
  4. 4. Key issues in micorarray analysis<br />1. Experimental design<br />2. Data analysis<br /><ul><li> Preprocessing raw data
  5. 5. Identification differentially </li></ul> expressed genes<br /><ul><li> Pathway analysis</li></ul>! 4 biological replicates!<br />CD4+ T cells<br />3. Data validation<br />Sort eGFP+<br />RNA<br />Illumina gene <br />expression analysis<br />
  6. 6. Preprocessing of raw expression data<br />Raw intensity values<br />expression values<br /><ul><li> Background correction
  7. 7. Summarization
  8. 8. Normalization: “adjusting for effects that arise from variation in technology”
  9. 9. Different methods: eg. quantile normalization,…
  10. 10. Different Software : Platform dependent!
  11. 11. Free: R/Bioconductor-packages : beadarray, affy,...</li></ul> RMA Express,…<br /><ul><li> Commercial : Genespring (Agilent) ®</li></ul> Affymetrix expression console software ®<br /> …<br />
  12. 12. Preprocessing of raw expression data<br />Quantile normalization with the R/Bioconductor package Beadarray<br />Bioconductor packages : use manuals!<br />
  13. 13. MICROARRAY DATA MINING<br />expression values<br />biological data<br />Genes with highest FOLD CHANGE<br />Literature search of individual genes<br />???<br />not successful<br />Better approach:<br />Broad statistical selection of differentially expressed genes <br />Pathway analysis<br />
  14. 14. Selection of differentially expressed genes<br />Multitude of statistical tests available!<br />eg. Statistical Analysis of Microarrays (SAM)<br /> Rank Product analysis (RP)<br />NA7<br />NL43<br />NA7<br />NL43<br />More powerful for low number of replicates!<br />29<br />159<br />15<br />167<br />73<br />59<br />- RP analysis with RankProd R/Bioconductor package<br />- pfp: 0.05<br />downregulated genes<br />upregulated genes<br /># downregulated genes: 203<br /># upregulated genes: 299<br />
  15. 15. Pathway analysis<br />Principle : <br /> Identification <br />pathway/functions <br />overrepresented <br />in your dataset<br />Tools : multitude of free and commercial software packages!<br /><ul><li>Ingenuity Pathway analysis: </li></ul>Based on Ingenuity Knowledge Database<br /><ul><li>Database for Annotation, Visualization and Integrated Discovery (DAVID):</li></ul>Based on public available databases (KEGG, GO,…)<br />
  16. 16. Pathway analysis<br /><br />
  17. 17. Pathway analysis<br />
  18. 18. Pathway analysis<br />
  19. 19. Microarray data mining …continued<br />Literature-based selection of interesting pathways/ genes !! <br />pathway<br />
  20. 20. Conclusions<br />Microarray data analysis requires…<br /> Statistics for differentially expressed gene identification <br /> and pathway analysis<br /> Appropriate software in each step of the process <br /> Literature search<br /> Time to LEARN and PERFORM the above<br />
  21. 21. Acknowledgements<br />Prof. Dr. Bruno Verhasselt<br />Alessia Landi, PhD Student<br />Veronica Iannucci, PhD Student<br />Pieter Meuwissen, PhD Student<br />Evelien Naessens, Lab technician<br />Hanne Vanderstraeten, Lab technician<br />Kathleen Van Landeghem, Lab technician<br />Anouk Van Nuffel, PhD Student<br />Wojciech Witkowski, PhD STudent<br />Caroline Stevens, Master student<br />Natasja Mortier, Master student<br />
  22. 22.
  23. 23. Rank product analysis<br /><ul><li> Rank product : geometric mean of rank of a gene
  24. 24. Significance?
  25. 25. random permutations of genes in each comparison
  26. 26. percentage of false positives (pfp)</li></ul>Pfp cut-off: 0.05<br />