The poster was made during a 2.5 day NSF sponsored workshop on August 5th – 7th on the University of Washington, Seattle campus, which brought together 100 graduate students from diverse domain sciences and engineering with Data Scientists from industry and academia to discuss and collaborate on Big Data / Data Science challenges. In addition to keynote presentations from high profile speakers, the participants presented posters covering their own research and worked collaboratively to begin to solve some of the Grand Challenge problems facing Data Enabled Science & Engineering disciplines. This presentation was made by 10 graduate students, whose united them was high-dimensional biological data. For more info, see http://depts.washington.edu/dswkshp/

1. X-team #2
High Dimensional
Biological Butterflies
Data Science Workshop 2015

2. What do we have in common?

3. High-dimensional biological data
● High-throughput genotyping and phenotyping
● Finding biological meaning in big data with
high N and/or P

4. The ability to harvest the wealth of information contained in
biomedical Big Data will advance our understanding of
human health and disease; however, lack of appropriate
tools, poor data accessibility, and insufficient training, are
major impediments to rapid translational impact. -NIH BD2K

5. Data integration
● Data fragmentation
o individual vs population
o multiple -omics
o multiple sources
● Discovery and prediction
o genome and functional
annotation
Statistical learning
methods
● Data quality
○ hidden sources of variability
○ limitations of short read
sequencing
Data annotation
Genome assembly/error
correction
Problem Solution

6. Success Stories
Domain Science Data Science Methods
Metabolic pathway - Ingenuity Pathway Analysis (http://www.ingenuity.com/products/ipa)
Genomic data - Quality Control
- FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/)
- EasyQC for genome-wide association meta-analyses
(http://www.nature.com/nprot/journal/v9/n5/full/nprot.2014.071.html)
- Batch effect
- PEER (http://www.ncbi.nlm.nih.gov/pubmed/22343431)
- SVA (http://www.ncbi.nlm.nih.gov/pubmed/22257669)
- scLVM (Buettner et al., 2015)
- Data storage and sharing
- NCBI (http://www.ncbi.nlm.nih.gov)
- GitHub (https://github.com)
- UCSC genome browser (http://genome.ucsc.edu/)
- Gene annotation
- Gene Ontology (http://geneontology.org/page/documentation)
Proteomics - Protein Data Bank (PDB) (http://www.rcsb.org/pdb/home/home.do)
Disease Survivability - WEKA (Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, Ian H. Witten
(2009); The WEKA Data Mining Software: An Update; SIGKDD Explorations, Volume 11, Issue 1.)

7. Same data, different interpretation
Gilad & Mizrahi-Man 2015
F1000Research, 4:121

8. Interdisciplinary
Research
Interdisciplinary data science essentials

9. Going Forward
● Create and maintain a HowTo website for
Data Science computational tools and
methods.
http://data-science-for-biologists.wikia.com/wiki/Data_Science_for_Biologists_Wikia
● Collaborate via Github

10. Thanks!