Analysing biomedical data
Paul Agapow / Translational Bioinformatics DSI-ICL / October 2017

Biomedical science
is now data science

The 4-headed
beast
● The 4 heads
○ Acquisition
○ Storage
○ Analysis
○ Sharing
● Big data 4Vs
○ Velocity
○ Volume
○ Variety
○ Veracity

The problems of biomedical data
Many ...
● Types
● Formats
● Silos
● Gaps
● Interactions
Difficult analysis
● The curse of dimensionality
● Multiple hypothesis testing &
false discovery
● Batch effects
● Life history
● Biased sampling
● Need for integrative analysis
Practical issues
● Unstructured data
● Managing big data
● Security
● Legal & privacy

Future
medicine
A mix of promise & peril
● More data
○ Genomic medicine
○ Other “omic” medicine
○ Wearables
○ EHR & digital health
● P4 medicine
○ Stratification
○ Analysis at the bedside
○ Patient participation
● Translational medicine
○ Leveraging health data for
research

Scientific data
doubles every
18 months
A new paper
is published
every 30
seconds
Most papers
are never
cited or
even read
No new principle will declare itself from below
a heap of facts. (Peter Medewar)

The analytical
challenges

Liberating
health data
● Enabling EHR for research
● Text extraction
● Unstructured to structured
data

Computationally intensive approaches
● Deep learning
● Concurrent computation
● Which to use? Which works?
● Implementation
● Interpretation
● Assisted & auto-discovery

Integrative
analysis
● The genome is not enough
● Complex interactions
● Statistical power
● Which is best?
● Interpretation

Building knowledge bases
● Extracting structured
information from
unstructured input
● Veracity
● Exploring / querying

Reproducibility

The “solutions”

Standards
● Clinical descriptions
● Measurements:
○ blood pressure
○ White cells
● Cross-study
Yes!
● Allows combining & comparing studies
● CDISC
● HPO
But!
● A lot of work

Data formats & storageYes!
● Plain text
● Open formats
● Structured formats
● Advantages:
○ Human & machine readable
○ Unambiguous
○ WYSIWYG
● Examples:
○ Open bio formats
○ CSV, TSV
No!
● Homebrew formats
● Proprietary / closed formats
● Binary formats
● Excel

Workflow systems & notebooks● Analysis as:
○ An executable recipe
○ A document or commentary
● Many candidates:
○ Workflows:
■ Snakemake
■ Nextflow
■ CWL etc.
○ Computational notebooks:
■ Jupyter / IPython
■ RMarkdown

Deep learning / machine learning
How do you know a biologist
is using deep learning in
their research?
Don’t worry, they’ll tell you.
● “Just” optimization and search techniques
● Takes a set of features and produces a
model that performs a classification or a
regression
● A series of layers that assemble features
into higher level features
● Several high-quality toolkits
● Some need for specialised hardware
(GPU)
● Interpretability
● Ground truths
● Needs lots of data

The pitfalls

Batch effects
● Technical sources of
variation
○ Reageants
○ Technician
○ Platform
○ ...
● Solutions:
○ Plot data
○ Don’t batch
○ COMBAT etc. (but
loss of information)

Omnigenics
What if every gene affected
every other gene?
● Pritchard et al 2017
● FOAF / six degrees of separation effect
● Implicated genes are a few drivers and an
enormous number of “related” loci
● Context?

The garden of forking paths
Multiple hypothesis testing

Conclusion
Taming the 4-headed beast
Acquiring: interpret EHR
Storing: data formats & systems
Analysing: statistics, correct for
batch effects, integrative analysis,
deep learning
Sharing: standards, data formats,
workflow systems