Visualization Approaches for
Gene Expression Data
Matt Hibbs
Assistant Professor
The Jackson Laboratory
March 4, 2010
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Transcriptomics & Gene
Expression
• Simultaneous measurement of
transcription for the entire genome
• Useful for broad range of biological
questions
DNA
Transcription
Translation
mRNA
Ribosome
Proteins
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Outline
• Technologies & Specific Concerns
– cDNA microarrays (2-color & 1-color arrays)
– RNA-seq
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•
•
•
•
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Normalization visualizations
Full data displays
Dimensionality reduction
Sequence-order displays
Comparative visualization
Future Directions
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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
A
C
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A
1.5
B
0.8
C
-1.2
D
0.1
Scan hybridized array
B
D
A
C
B
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
…
Cluster
…
Rasterize
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Under-Expressed
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+3
Over-Expressed10
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
Spearman Correlation
Pearson Correlation
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Heat Maps: Stats
Data clustered using a rank-based statistic
lowest value
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highest value
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Heat Maps: Overview + Detail
March 4, 2010
Data from Spellman et al., 1998
Java TreeView, Saldanha et al.
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Parallel Coordinates
• View expression vectors as lines
– X-axis = conditions
– Y-axis = value
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Time Searcher, Hochheiser et al.
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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
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Time Searcher, Hochheiser et al.
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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
Data from Spellman et al., 1998
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GeneVAnD, Hibbs et al.
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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
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Java TreeView, Saldanha et al.
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Sequence-based: RNA-seq
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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
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MeV, Saeed et al.
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Comparative Visualization: Single
Dataset
Spotfire
GeneSpring
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Comparative Visualization: Multidataset
Data from Spellman et al., 1998
Dendrogram
Heat Map Overview
HIDRA
Hibbs et al.
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Comparative Visualization: Multidataset
Data from Spellman et al., 1998
Selection
HIDRA
Hibbs et al.
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Synchronized
Details
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Comparative Visualization: Multidataset
Data from Spellman et al., 1998
Selection
HIDRA
Hibbs et al.
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Summary & Tools
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•
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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
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•
•
•
•
March 4, 2010
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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Postdoctoral Opportunities in
Computational & 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
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