The Role of Statistician in Personalized Medicine: An Overview of Statistical Methods in Bioinformatics
The Role of The Statisticians in
An Overview of Statistical
Methods in Bioinformatics
Fakultas Teknik Industri
Institut Teknologi Sepuluh Nopember
Surabaya, 12 March 2014
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• Universitas Brawijaya Malang, FMIPA, Statistics
• Hasselt Universiteit, Belgium, MSc in Applied Statistics
• Hasselt Universiteit, Belgium, MSc in Biostatistics 2006-
• Hasselt Universiteit, Belgium, PhD Statistical
• Medical Epidemiology And Biostatistics Dept. Karolinska
Institutet, Sweden, Postdoctoral, 2011-2014
• Lecture and Researcher at Sekolah Tinggi Ilmu
• Adjunct Faculty at Medical Epidemiology and
Biostatistics Dept, Karolinska Institutet, Stockholm.
• Personalized Medicine
• Central Dogma
• Microarray Data Analysis
• Next Generation Sequencing
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• Drug Development:
– Takes 10-15 years
– Cost millions USD
• Who: Pharmaceutical, biotechnology, device companies,
Universities and government research agencies
• Regulatory: The US Food and Drug Administration (FDA)
– Safety – can people take it?
– Efficacy – does it do anything in humans?
– Effectiveness – is it better or at least as good as what is
– Do the benefits outweigh the risks?
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• Drug Development Stages:
- Drug Discovery
- Pre-clinical Development
- Clinical Development 4 Phases
• Statisticians are involved in all stages
• Stages are highly regulated
• Result is based on most of patients
• But .. Patients are created differently!
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• We’re all different in
- Physiological, demographic characteristics
- Medical history
- Genetic/genomic characteristics
• What works for a patient with one set of
characteristics might not work for another!
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• “One size does not fit all”
• Use a patient’s characteristics to determine best
treatment for him/her
• Genomic information is a great potential
-- > Personalized medicine:
“The right treatment for the right patient at the right
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Subgroup identification and targeted treatment
• Determine subgroups of patients who share certain
characteristics and would get better on a particular
• Discover biomarkers which can identify the subgroup
• Focus on finding and treating a subgroup
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Subgroup identification and targeted treatment
Genotype Phenotype Intervention Outcome
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Advanced Biomedical Technologies
• High-throughput microarrays and molecular imaging
to monitor SNPs, gene and protein expressions
• Next-Generation Sequencing
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• The full DNA sequence of an organism is called its
• A gene is a segment that specifies the sequence of
one or more protein.
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• The study of all the genes of a cell, or tissue, at :
– the DNA (genotype), e.g., GWAS SNP, CNV etc…
– mRNA (transcriptomics), Gene expression,
– or protein levels (proteomics).
• Functional Genomics: study the functionality of specific
genes, their relations to diseases, their associated
proteins and their participation in biological processes.
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• DNA microarrays are biotechnologies which
allow the monitoring of expression of
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• High efficacy and low/no side effect drug
• Genes related disease.
• Biological discovery
– new and better molecular diagnostics
– new molecular targets for therapy
– finding and refining biological pathways
• Molecular diagnosis of leukemia, breast cancer, etsc.
• Appropriate treatment for genetic signature
• Potential new drug targets
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Overview of the process
of generating high
expression data using
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• Experiment design Lab work Image processing
• Signal summarization (RMA, GCRMA)
• Data Analysis:
– Differentially Expressed genes
• Network / Pathways (GSEA etc..)
• Biological interpretations
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• Mega data, difficult to visualize
• Too few records (columns/samples), usually < 100
• Too many rows(genes), usually > 10,000
• Too many genes likely leading to False positives
• For exploration, a large set of all relevant genes is
• For diagnostics or identification of therapeutic
targets, the smallest set of genes is needed
• Model needs to be explainable to biologists
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• Cluster the genes
• Cluster the
• Cluster both simultaneously
• Biclustering algorithms
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• Cluster or Classify
genes according to
• Cluster tumors
according to genes
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• Linear Discriminat Analysis
• K nearest neighbour
• Logistic regression
• L1 Penalized Logistric regression
• Neural Network
• Support vector machines
• Random forest
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Aim: To improve understanding of host protein
profiles during disease progression especially in
Classification of Malaria Subtypes
•Identify panel of proteins which could distinguish
between different subtypes.
•Implement L1-penalized logistic regression
Penalized Logistic Regression
•Logistic regression is a supervised method for binary
or multi-class classification.
•In high-dimensional data (e.g., microarray): More
variables than the observations Classical logistic
regression does not work.
•Other problems: Variables are correlated
(multicolinierity) and over fitting.
•Solution: Introduce a penalty for complexity in the
• Shrinks all regression coefficients () toward zero
and set some of them to zero.
• Performs parameter estimation and variable
selection at the same time.
• The choice of λ is crucial and chosen via k-fold
• The procedure is implemented in an R package
L1 Penalized Logistic Regression
Classification of Severe Malaria Anemia vs.
Uncomplicated Malaria group
• Breast invasive carcinoma (BRCA) from the Cancer
Genome Atlas Project (TCGA).
• 329 tumor samples.
• Platform: illumina
• Paired-end reads (length 50 bp).
• 20 -100 million reads
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• To discover transcripts/isoforms which are only
significantly (high/low) expressed in a certain cancer
Pramana, et.al 55NBBC 2013
329 samples TCGA
- TCGA 150 samples
- External samples
Classification to mol-subtypes
- Use Swedish microarray data as
- Based on gene level FPKM
- Median and variance normalization
- K-nearest neighbor
- Classifier genes selection
- Transcript level FPKM of all
- For each transcript: Robust
- Multiple testing adjustment.
Pramana, et.al 56NBBC 2013
• R now is growing, especially in bioinformatics
– Statistics, data analysis, machine learning
– High Quality
– Open Source
– Extendable (you can submit and publish your own package!!)
– Can be integrated with other languages (C/C++, Java, Python)
– Large active user community
– Command-based (-)
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• Statistics plays important roles in developing
• Multidisciplinary field need collaboration with
• Bioinformaticians is one of the sexiest job
• Big Data in Medicine: Numerous opportunities to be
explored and discovered.
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Thank you for your attention….
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