March 4, 2010 1

Visualization Approaches forGene Expression Data

Matt HibbsAssistant Professor

The Jackson Laboratory

March 4, 2010 2

Transcriptomics & Gene Expression

• Simultaneous measurement of transcription for the entire genome

• Useful for broad range of biological questions DNA

mRNA

Proteins

Ribosome

Transcription

Translation

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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

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Technology: 2-color cDNA Microarrays

Spot slide with known sequences

Add mRNA to slide for Hybridization

Scan hybridized array

reference mRNA test mRNA

add green dye add red dye

hybridizeA 1.5

B 0.8

C -1.2

D 0.1

A

CB

D

A

CB

D

A

CB

D

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Technology: 2-color cDNA Microarrays

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Technology: RNA-seq

Image from WikiMedia

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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

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Normalization: Box-Whisker Quantile

• Quantile normalization often used to adjust for between chip variance

• Box-Whisker plots typically used to visualize the process

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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.)

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Heat Maps

Under-Expressed Over-Expressed

ClusterRasterize

…

…

0 +3-3

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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

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Heat Maps: Distance Metrics

Euclidean Distance

Pearson Correlation

Spearman Correlation

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Heat Maps: Stats

Data clustered using a rank-based statistic

lowest value highest value

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Heat Maps: Overview + Detail

Java TreeView, Saldanha et al.Data from Spellman et al., 1998

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Parallel Coordinates• View expression vectors as lines

– X-axis = conditions– Y-axis = value

Time Searcher, Hochheiser et al.

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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

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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

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Dimensionality Reduction

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Dimensionality Reduction: SVD

…

…Transform original data vectors into an orthogonal basis that captures decreasing amounts of variation

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Dimensionality Reduction: SVD

SVD

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SVD Example

G1

S

G2

M

M/G1

Legend

GeneVAnD, Hibbs et al.Data from Spellman et al., 1998

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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

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Sequence-based: CGH• Karyoscope plots

Java TreeView, Saldanha et al.

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Sequence-based: RNA-seq

IGV, http://www.broadinstitute.org/igv

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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

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Comparative Visualization: Single Dataset

MeV, Saeed et al.

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Comparative Visualization: Single Dataset

Spotfire

GeneSpring

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Comparative Visualization: Multi-dataset

Dendrogram

Heat Map Overview

HIDRA

Data from Spellman et al., 1998

Hibbs et al.

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Comparative Visualization: Multi-dataset

HIDRA

Selection

SynchronizedDetails

Data from Spellman et al., 1998

Hibbs et al.

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Comparative Visualization: Multi-dataset

HIDRA

Selection

Data from Spellman et al., 1998

Hibbs et al.

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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)

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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

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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

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