March 4, Visualization Approaches for Gene Expression Data Matt Hibbs Assistant Professor The Jackson Laboratory

March 4, Transcriptomics & Gene Expression Simultaneous measurement of transcription for the entire genome Useful for broad range of biological questions DNA mRNA Proteins Ribosome Transcription Translation

March 4, 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

March 4, Technology: 2-color cDNA Microarrays Spot slide with known sequences Add mRNA to slide for Hybridization Scan hybridized array reference mRNAtest mRNA add green dye add red dye hybridize A1.5 B0.8 C-1.2 D0.1 A C B D A C B D A C B D

March 4, Technology: 2-color cDNA Microarrays

March 4, Technology: RNA-seq Image from WikiMedia

March 4, 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

March 4, Normalization: Box-Whisker Quantile Quantile normalization often used to adjust for between chip variance Box-Whisker plots typically used to visualize the process

March 4, 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.)

March 4, Heat Maps Under-ExpressedOver-Expressed ClusterRasterize … … 0+3-3

March 4, 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

March 4, Heat Maps: Distance Metrics Euclidean Distance Pearson Correlation Spearman Correlation

March 4, Heat Maps: Stats Data clustered using a rank-based statistic lowest valuehighest value

March 4, Heat Maps: Overview + Detail Java TreeView, Saldanha et al. Data from Spellman et al., 1998

March 4, Parallel Coordinates View expression vectors as lines –X-axis = conditions –Y-axis = value Time Searcher, Hochheiser et al.

March 4, Parallel Coordinates Time Searcher, Hochheiser et al. Selection and Interaction methods can answer specific questions Brushing techniques to select patterns Cluttered displays for large datasets, limited number of conditions effectively shown

March 4, 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

March 4, Dimensionality Reduction

March 4, Dimensionality Reduction: SVD … … Transform original data vectors into an orthogonal basis that captures decreasing amounts of variation

March 4, Dimensionality Reduction: SVD SVD

March 4, SVD Example G1 S G2 M M/G1 Legend GeneVAnD, Hibbs et al. Data from Spellman et al., 1998

March 4, 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

March 4, Sequence-based: CGH Karyoscope plots Java TreeView, Saldanha et al.

March 4, Sequence-based: RNA-seq IGV,

March 4, 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

March 4, Comparative Visualization: Single Dataset MeV, Saeed et al.

March 4, Comparative Visualization: Single Dataset Spotfire GeneSpring

March 4, Comparative Visualization: Multi- dataset Dendrogram Heat Map Overview HIDRA Data from Spellman et al., 1998 Hibbs et al.

March 4, Comparative Visualization: Multi- dataset HIDRA Selection Synchronized Details Data from Spellman et al., 1998 Hibbs et al.

March 4, Comparative Visualization: Multi- dataset HIDRA Selection Data from Spellman et al., 1998 Hibbs et al.

March 4, Summary & Tools R & bioconductor Java TreeView (Saldanha, 2004) Time Searcher (Hochheiser et al., 2003) Integrative Genomics Viewer (IGV; TIGR’s MultiExperiment Viewer (MeV; Saeed et al., 2003) HIDRA (Hibbs et al., 2007)

March 4, 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

March 4, 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

March 4, The Center for Genome Dynamics at The Jackson Laboratory 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 Postdoctoral Opportunities in Computational & Systems Biology