LICT Human-Machine-Interface
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BIODATA ANALYSIS & VISUALIZATION Jan Aerts Faculty of Engineering - ESAT/SCD http://saaientist.blogspot.com @jandot Tuesday 1 February 2011
Transcript of LICT Human-Machine-Interface
BIODATA ANALYSIS&
VISUALIZATION Jan Aerts
Faculty of Engineering - ESAT/SCD
http://saaientist.blogspot.com@jandot
Tuesday 1 February 2011
Involved in genomics research:
•chicken, cow, human genome DNA sequencing
•search for genetic variation responsible for phenotype/disease
Issues with
•filtering: finding the correct set of parameters
•pattern searching: grasping the significance and effect of the mutations
=> visual analyticsTuesday 1 February 2011
A. Filtering
Investigating parameter space...
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putative mutations
filter 1
filter 2
A B C
different settings for filters
filter 3
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AB
C
What we find...
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AB
C
What we find...
State of the art: run many filter pipelines and take intersection
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AB
C
What we should have found...
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parameter-space
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sometimes: bypass parameter-setting
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Aim: use interactive visualization of the “raw” data to:
•peep inside the black box
•get feel for the data
•get feel for how filter settings influence each other
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Aim: use interactive visualization of the “raw” data to:
•peep inside the black box
•get feel for the data
•get feel for how filter settings influence each other
Eradicate disease
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B. Pattern searching
Making sense of our data...
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Typical example: gene networks=> can we identify patterns?
same network
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How do these networks differ?
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Hive Plots, taken from http://mkweb.bcgsc.ca/linnet/
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Aim: help researchers make sense of complicated data:
•gene networks
•structural variation in the genome
• linked data
• ...
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Aim: help researchers make sense of complicated data:
•gene networks
•structural variation in the genome
• linked data
• ...
Eradicate disease
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Hurdles:
•big data (millions/billions of datapoints)
=> makes interactivity difficult
solution: indexing methods, data formats, dimensionality reduction, ...
• visual encoding
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Tools
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User groups:
researcher => clinician => patient
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Bioinformatics
Visualization
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So:
•visual analytics: visually identifying patterns in large datasets to inform on statistical analysis
•use visualization to make sense of complex data
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