Visualization Approaches for Gene Expression Data

1. Visualization Approaches forGene Expression Data Matt Hibbs Assistant Professor The Jackson Laboratory

2. Transcriptomics & Gene Expression DNA Transcription mRNA Ribosome Translation Proteins Simultaneous measurement of transcription for the entire genome Useful for broad range of biological questions

3. Outline • Technologies & Specific Concerns • cDNA microarrays (2-color & 1-color arrays) • RNA-seq • Normalization visualizations • Full data displays • Dimensionality reduction • Sequence-order displays • Comparative visualization • Future Directions

4. Technology: 2-color cDNA Microarrays reference mRNA test mRNA Add mRNA to slide for Hybridization add green dye add red dye hybridize A B C D Spot slide with known sequences Scan hybridized array A A B B C C D D

5. Technology: 2-color cDNA Microarrays

6. Technology: RNA-seq Image from WikiMedia

7. Normalization: MA-plot • Need to account for intensity bias between channels (red/green, or mult. 1-color) • MA-plot (also called RI-plot) shows relationship between ratio and intensity

8. Normalization: Box-Whisker Quantile • Quantile normalization often used to adjust for between chip variance • Box-Whisker plots typically used to visualize the process

9. Full Data Displays • Techniques to show all of the data at once • Heat Maps • Displays numerical values as colors • Good to see all data intuitively • Requires clustering to see patterns • Parallel Coordinates • Line plots of high-dimensional data • Easy to see/select trends or patterns • Esp. good for course data (time, drug, etc.)

10. Heat Maps Under-Expressed Over-Expressed … Rasterize Cluster … -3 0 +3

11. Heat Maps: Stats • Clustering important to see patterns • Hierarchical, K-means, SOM, etc… • Choice of distance metric in addition to method • Match the visualization mapping to the statistics used for analysis • Coloration based on actual numbers appropriate for Euclidian distance measures • Centered or normalized measures should use corresponding colorings

12. Heat Maps: Distance Metrics Euclidean Distance Pearson Correlation Spearman Correlation

13. Heat Maps: Stats lowest value highest value Data clustered using a rank-based statistic

14. Heat Maps: Overview + Detail Java TreeView, Saldanha et al. Data from Spellman et al., 1998

15. Parallel Coordinates • View expression vectors as lines • X-axis = conditions • Y-axis = value Time Searcher, Hochheiser et al.

16. Parallel Coordinates • Selection and Interaction methods can answer specific questions • Brushing techniques to select patterns • Cluttered displays for large datasets, limited number of conditions effectively shown Time Searcher, Hochheiser et al.

17. Dimensionality Reduction • Project data from large, high dimensional space to a smaller space (usually 2 or 3 D) • Several techniques: • SVD & PCA • Multidimensional scaling • Once projected into lower dimension, use standard 2D (or 3D) techniques

18. Dimensionality Reduction

19. Dimensionality Reduction: SVD Transform original data vectors into an orthogonal basis that captures decreasing amounts of variation … …

20. Dimensionality Reduction: SVD SVD

21. SVD Example G1 S G2 M M/G1 Legend Data from Spellman et al., 1998 GeneVAnD, Hibbs et al.

22. Sequence-based Visualization • View data in chromosomal order • Copy number variation & aneuploidies • common in cancers & other disorders • Competitive Genomic Hybridization (CGH) • mRNA sequencing (RNA-seq) • Borrows concepts from genome browsers

23. Sequence-based: CGH Java TreeView, Saldanha et al. Karyoscope plots

24. Sequence-based: RNA-seq IGV, http://www.broadinstitute.org/igv

25. Comparative Visualization Using multiple simultaneous complementary views of data Each scheme emphasizes different aspects – use multiple to show overall picture Show multiple, related datasets to identify common and unique patterns

26. Comparative Visualization: Single Dataset MeV, Saeed et al.

27. Comparative Visualization: Single Dataset Spotfire GeneSpring

28. Comparative Visualization: Multi-dataset HIDRA Data from Spellman et al., 1998 Dendrogram Heat Map Overview Hibbs et al.

29. Comparative Visualization: Multi-dataset HIDRA Data from Spellman et al., 1998 Selection Synchronized Details Hibbs et al.

30. Comparative Visualization: Multi-dataset HIDRA Data from Spellman et al., 1998 Selection Hibbs et al.

31. Summary & Tools R & bioconductor Java TreeView (Saldanha, 2004) Time Searcher (Hochheiser et al., 2003) Integrative Genomics Viewer (IGV; www.broadinstitute.org/igv) TIGR’s MultiExperiment Viewer (MeV; Saeed et al., 2003) HIDRA (Hibbs et al., 2007)

32. Trends & Future Directions • Emphasis on usability and audience • If a “wet bench” biologist can’t use it… • Incorporate common statistical analysis techniques with visualizations • e.g. differential expression tests, GO enrichments, etc. • Isoforms and Splice variants • New user interaction schemes • e.g. multi-touch interfaces, large-format displays • Low level “systems analysis” • linking together multiple types of data into unified displays

33. Acknowledgements • Hibbs Lab • Karen Dowell • Tongjun Gu • Al Simons • Olga Troyanskaya Lab • Patrick Bradley • Maria Chikina • Yuanfang Guan • Chad Myers • David Hess • Florian Markowetz • Edo Airoldi • Curtis Huttenhower • Kai Li Lab • Grant Wallace • Amy Caudy • Maitreya Dunham • Botstein, Kruglyak, Broach, Rose labs • Kyuson Yun • Carol Bult

34. Postdoctoral Opportunities inComputational & Systems Biology The Center for Genome Dynamics at The Jackson Laboratory www.genomedynamics.org Investigators use computation, mathematical modeling and statistics, with a shared focus on the genetics of complex traits Requires PhD (or equivalent) in quantitative field such as computer science, statistics, applied mathematics or in biological sciences with strong quantitative background Programming experience recommended The Jackson Laboratory was voted #2 in a poll of postdocs conducted by The Scientist in 2009 and is an EOE/AA employer