Vincenzo Lagani - Data analysis in life science

1. Data Analysis
in Life Science
Vincenzo Lagani
DataFest Tbilisi 2019

2. 2
Vincenzo Lagani
 from Calabria, Italy
 Associate Professor at Ilia State
University, Tbilisi, Georgia
 Co-founder of Gnosis Data Analysis,
Heraklion, Greece

3. 3
Outline
1. Machine Learning in Life Science
2. Open Data in Life Science
3. On-line tools for data analysis

4. 1.
Machine Learning
in Life Science
a challenging task
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5. 5
Molecular
Biology
Medicine
Precision
medicine
And many
more
Life Science: a vast field
Biology

6. 6
Molecular biology: a normal day inside a cell
Adapted from https://www.novusbio.com/apoptosis-pathway

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What information can we collect?
Oh, the beauty of modern biotechnology!
Genomic modifications
Differences in the DNA code
Epigenetic modifications
Changes in the DNA surrounding
Adapted from https://www.wikipedia.org

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Gene expression
Level of operation of
each gene
Proteomics / metabolomics
Concentration of fundamental
molecules within the cell
Adapted from https://www.wikipedia.org
What information can we collect?
Oh, the beauty of modern biotechnology!

9. 9
Current revolution: single cell data
Partially adapted from https://shenorrlab.github.io/bseqsc/index.html
Bulk Measurements
Each sample encoded
as a vector
Single cell
Measurements
Each sample encoded
as a matrix
C1
C2
CN

10. 10
Processed bulk data: a familiar 2D matrix
Gene expression data from GEO study GDS4206

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Processed bulk data: a familiar 2D matrix
Metabolomics data from Metabolomics Workbench (ST000005)

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Visualizing processed bulk data
Gene expression data from GEO study GDS4206

13. 13
Connecting with medicine
Non-relapse
Relapse
Gene expression data from GEO study GDS4206

14. 14
Data Science tasks in Life Science
diagnosis, prognosis, risk stratification
supervised learning
population identification
clustering, dimensionality
reduction
Identification of molecular
interactions
network reconstruction
curation of biological findings
text mining, knowledge representation

15. 15
Supervised learning:
Risk prediction for Lung Cancer
Adapted from Markaki et al, EBioMedicine 2018

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Supervised learning:
Risk prediction for Lung Cancer
Adapted from Markaki et al, EBioMedicine 2018
http://mensxmachina.org/en/HUNT-NTNU-lung-cancer-risk-calculator/

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Deep learning application:
identifying rare cell-populations
Arvaniti et al. Nature Communicatiomn 2018

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Network reconstruction:
retrieving protein interactions
Papoutsoglou et al. NAR 2017
http://scenery.csd.uoc.gr

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Text mining:
identification of relevant findings
https://www.nlm.nih.gov/bsd/stats/cit_added.html
Citation added to MEDLINE yearly

20. 2.
Open Data in Life
Science
unfortunately, not as open as we
would like
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(Important) example: Clinical Trials
 Many countries require or strongly
recommend clinical trials registration
 https://clinicaltrials.gov
 https://www.clinicaltrialsregister.eu
 There is no legal obligation in
communicating the results
 ~half of approved studies does not produce a publication
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0114023
No obligation of providing the raw data

22. 22
Are the data from clinical trials
becoming more open?
 Some institutes already provide their raw
data
 https://biolincc.nhlbi.nih.gov/studies/ (heart, lung, blood)
 https://data-archive.nimh.nih.gov/ndct/ (mental health)
 https://repository.niddk.nih.gov/home/ (diabetes, kidney)
 https://www.ukdataservice.ac.uk/ (UK central repository)
 Several initiatives for:
 making mandatory to allow access to the data in order to
publish any result
 creating suitable standards and procedures for opening
clinical trials’ data

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Open Data for molecular biology
 Most journals require molecular data to be
deposited in on-line repository before
publication
 Molecular data raise fewer privacy concerns
(except for genomic data)
 A number of standards have been developed
for exchanging and storing molecular data
 MIAME (Minimum Information About a Microarray
Experiment), MIAPE (minimum information about a
proteomics experiment), et cetera.

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A history started 25 years ago
Imker, Front. Res. Metr. Anal 2018

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Raw data repository:
Gene Expression Omnibus (GEO)

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Meta repository:
BioDataome (dataome.mensxmachina.org)

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Meta repository:
OmicsDI (https://www.omicsdi.org/)

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Protein-protein interaction repository:
String (https://string-db.org)

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Pathway repository:
Reactome (https://reactome.org/)

30. 3.
On-line tools for
data Analysis
let’s analyze some data!
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31. Thanks!
Any questions?
You can find me at:
vincenzo.lagani@iliauni.edu.ge
vincenzo.lagani@gnosisda.ge
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32. Credits
Special thanks to
◎Ilia State University: www.iliauni.edu.ge
◎my former colleagues from University of Crete:
http://mensxmachina.org/en/
◎Gnosis Data Analysis: www.gnosisda.gr
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