This document discusses the role of statisticians in personalized medicine and provides an overview of statistical methods used in bioinformatics. It begins with an introduction to the speaker's educational background and current positions. The rest of the document is outlined as follows: an introduction to personalized medicine and patients' heterogeneity; applications of microarray and next-generation sequencing technologies; statistical methods for microarray data analysis including gene selection, classification, clustering, and dose-response studies; and RNA-seq analysis from sequencing to identifying subtype-specific transcripts. Statistics plays an important role in developing personalized medicine through multidisciplinary collaboration and exploring big data in healthcare.

1. The Role of The Statisticians in
Personalized Medicine:
An Overview of Statistical
Methods in Bioinformatics
Setia Pramana
Teknik Fisika
Fakultas Teknik Industri
Institut Teknologi Sepuluh Nopember
Surabaya, 12 March 2014
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2. Educational Background
• Universitas Brawijaya Malang, FMIPA, Statistics
department, 1995-1999.
• Hasselt Universiteit, Belgium, MSc in Applied Statistics
2005-2006.
• Hasselt Universiteit, Belgium, MSc in Biostatistics 2006-
2007.
• Hasselt Universiteit, Belgium, PhD Statistical
Bioinformatics, 2007-2011.
• Medical Epidemiology And Biostatistics Dept. Karolinska
Institutet, Sweden, Postdoctoral, 2011-2014

3. Now?
• Lecture and Researcher at Sekolah Tinggi Ilmu
Statistik, Jakarta.
• Adjunct Faculty at Medical Epidemiology and
Biostatistics Dept, Karolinska Institutet, Stockholm.

4. Outline
• Personalized Medicine
• Central Dogma
• Microarray Data Analysis
• Next Generation Sequencing
• Summary
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5. Personalized Medicine
• 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)
• Evaluate:
– Safety – can people take it?
– Efficacy – does it do anything in humans?
– Effectiveness – is it better or at least as good as what is
currently available?
– Do the benefits outweigh the risks?
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6. Personalized Medicine
• 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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7. Patients Heterogeneity
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8. Patients Heterogeneity
• 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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9. Patients Heterogeneity
• “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
time”
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10. Subgroup identification and targeted treatment
• Determine subgroups of patients who share certain
characteristics and would get better on a particular
treatment
• Discover biomarkers which can identify the subgroup
• Focus on finding and treating a subgroup
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11. Subgroup identification and targeted treatment
Genotype Phenotype Intervention Outcome
Mutations/SN
Ps
Gene/Protein
Expression
Epigenetics
Diseases
Disability
etc
Drug
Regimes
Personalized
medicine
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12. Advanced Biomedical Technologies
• High-throughput microarrays and molecular imaging
to monitor SNPs, gene and protein expressions
• Next-Generation Sequencing
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13. Microarrays
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14. Central Dogma
Central Dogma
http://compbio.pbworks.com
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15. Gene
• The full DNA sequence of an organism is called its
genome
• A gene is a segment that specifies the sequence of
one or more protein.
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16. Genomics
• 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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17. Microarray
• DNA microarrays are biotechnologies which
allow the monitoring of expression of
thousand genes.
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18. Applications
• 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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19. Microarray
Overview of the process
of generating high
throughput gene
expression data using
microarrays.
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20. The Pipeline
• Experiment design  Lab work  Image processing
• Signal summarization (RMA, GCRMA)
• Normalization
• Data Analysis:
– Differentially Expressed genes
– Clustering
– Classification
– Etc.
• Network / Pathways (GSEA etc..)
• Biological interpretations
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21. Microarray Data Structure
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22. Preprocessed Data
Genes C1 C2 C3 T1 T2 T3
G8522 6.78 6.55 6.37 6.89 6.78 6.92
G8523 6.52 6.61 6.72 6.51 6.59 6.46
G8524 5.67 5.69 5.88 7.43 7.16 7.31
G8525 5.64 5.91 5.61 7.41 7.49 7.41
G8526 4.63 4.85 5.72 5.71 5.47 5.79
G8528 7.81 7.58 7.24 7.79 7.38 8.60
G8529 4.26 4.20 4.82 3.11 4.94 3.08
G8530 7.36 7.45 7.31 7.46 7.53 7.35
G8531 5.30 5.36 5.70 5.41 5.73 5.77
G8532 5.84 5.48 5.93 5.84 5.73 5.75Setia Pramana 23

23. Challenges
• 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
desired
• 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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24. Microarray Data Analysis Types
• Gene Selection
–find genes for therapeutic targets
• Classification (Supervised)
–identify disease (biomarker study)
–predict outcome / select best treatment
• Clustering (Unsupervised)
–find new biological classes / refine existing ones
–Understanding regulatory relationship/pathway
–exploration
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25. Gene Selection
• Modified t-test
• Significance Analysis of Microarray (SAM)
• Limma (Linear model for microarrays )
• Linear Mixed model
• Lasso (least absolute selection and shrinkage operator)
• Elastic-net
• Etc,
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26. Visualization
• Dimensionality reduction
• PCA (Principal Component Analysis)
• Biplot
• Heatmap
• Multi dimensional scaling
• Etc
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27. Clustering
• Cluster the genes
• Cluster the
arrays/conditions
• Cluster both simultaneously
• K-means
• Hierarchical
• Biclustering algorithms
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28. Clustering
• Cluster or Classify
genes according to
tumors
• Cluster tumors
according to genes
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30. Classification
• Linear Discriminat Analysis
• K nearest neighbour
• Logistic regression
• L1 Penalized Logistric regression
• Neural Network
• Support vector machines
• Random forest
• etc
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31. Aim: To improve understanding of host protein
profiles during disease progression especially in
children.

32. Classification of Malaria Subtypes
•Identify panel of proteins which could distinguish
between different subtypes.
•Implement L1-penalized logistic regression

33. 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
model.
36

34. Penalized Logistic Regression
Logistic model:
Maximize the log-likelihood:
•-Penalization (Lasso):
•
37

35. • 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
cross-validation procedure.
• The procedure is implemented in an R package
called penalized.
38
L1 Penalized Logistic Regression

36. Classification of Severe Malaria Anemia vs.
Uncomplicated Malaria group
39
AUC: 0.86

37. Dose-response Microarray Studies
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38. Dose-response Microarray Studies
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Implemented in R package IsoGene and IsoGeneGUI.

39. Dose-response Microarray Studies
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40. Gene Signature for Prostate Cancer
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41. Gene Signature for Prostate Cancer
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42. Gene Signature for Prostate Cancer
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43. Next Generation Sequencing
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44. Next Generation Sequencing
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Reading the order of bases of DNA fragments

45. NGS used for:
• Whole genome re-sequencing
• Metagenomics
• Cancer genomics
• Exome sequencing (targeted)
• RNA-sequencing
• Chip-seq
• Genomic Epidemiology
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46. Next Generation Sequencing
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• Produce Massive Data and fast
• Problem is storage and analysis

47. RNA-seq Pipeline
• Align to a reference genome using Tophat.
Reference
Pramana, et.al 51NBBC 2013
Source: Trapnell et.al, 2010

48. RNA-seq Pipeline
• Measure gene expression using Cufflinks: FPKM
(Fragments Per Kilobase of transcript per Million
mapped reads).
Reference Gene
Transcript 2
Transcript 1
Isoform/Transcript FPKM
Gene FPKM
Sample 1
Sample 2
Sample 3
Pramana, et.al 52NBBC 2013 Source: Trapnell et.al, 2013

49. Setia Pramana 53

50. Subtype-specific Transcripts/Isoforms
• 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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51. Subtype-specific Transcripts/Isoforms
• To discover transcripts/isoforms which are only
significantly (high/low) expressed in a certain cancer
subtype.
Pramana, et.al 55NBBC 2013

52. Analysis Flow
329 samples TCGA
Discovery set
179 samples
Validation set
- TCGA 150 samples
- External samples
Classification to mol-subtypes
- Use Swedish microarray data as
training data.
- Based on gene level FPKM
- Median and variance normalization
- K-nearest neighbor
- Classifier genes selection
Subtype-specific Transcript
- Transcript level FPKM of all
genes
- For each transcript: Robust
contrast tests.
- Multiple testing adjustment.
Pramana, et.al 56NBBC 2013

53. Subtype-specific Transcripts/Isoforms
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54. Subtype-specific Transcripts/Isoforms
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55. Subtype-specific Transcripts/Isoforms
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56. Software?
• R now is growing, especially in bioinformatics
– Statistics, data analysis, machine learning
– Free
– 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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57. Summary
• Statistics plays important roles in developing
personalized medicine
• Multidisciplinary field  need collaboration with
different experts.
• Bioinformaticians is one of the sexiest job
• Big Data in Medicine: Numerous opportunities to be
explored and discovered.
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58. Thank you for your attention….
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