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.

1. Microarray data andpathway analysis: Examplefrom the bench Jolien Vermeire, HIVlab, UGent September 28th 2011, WOUD mini-symposium, Gent

2. Information from microarray experiments environmental stimuli pathogenic factor disease drug/therapy B I O L O G I C A L P R O C E S S MOLECULAR MECHANISM PATHWAY ANALYSIS GENE EXPRESSION ANALYSIS: BIOMARKER (DRUG TARGET)

3. Information from microarray experiments HIV B I O L O G I C A L P R O C E S S MOLECULAR MECHANISM ?

7. Summarization

8. Normalization: “adjusting for effects that arise from variation in technology”

9. Different methods: eg. quantile normalization,…

10. Different Software : Platform dependent!

12. Preprocessing of raw expression data Quantile normalization with the R/Bioconductor package Beadarray Bioconductor packages : use manuals!

13. 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

14. 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

16. Pathway analysis http://david.abcc.ncifcrf.gov/

17. Pathway analysis

18. Pathway analysis

19. Microarray data mining …continued Literature-based selection of interesting pathways/ genes !! pathway

20. 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

21. 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

24. Significance?

25. random permutations of genes in each comparison

26. percentage of false positives (pfp)Pfp cut-off: 0.05