Biostatistics and Statistical Bioinformatics

1. Biostatistics and Statistical Bioinformatics Setia Pramana Universitas Brawijaya Malang, 7 October 2011

2. Becoming a Statistician?

3. Who Need Statisticians? • Can only become a lecturer/teacher? • NO…… More applied fields: • My classmates work in: • Information and Communication Technology. • Research and Developments • Governments: Ministry of Finance, PLN, Bank Indonesia, Danareksa, etc. • Entrepreneur • Many more... • Writer.... • Read the book: 9 Summers 10 Autumns

4. Astronomy Market research Sport Economy Medical Social Statistics Politics Biology Psychology Agriculture Industry Banking Entrepreneur

5. Biostatisticians

6. Biostatistics • The study of statistics as applied to biological areas such as Biological laboratory experiments, medical research (including clinical research), and public health services research. • Biostatistics, far from being an unrelated mathematical science, is a discipline essential to modern medicine – a pillar in its edifice’ (Journal of the American Medical Association (1966)

7. Biostatistics • Public Health: • Epidemiology • Modeling Infectious Diseases: HIV, HCV • Disease Mapping • Genetics: family related disease • Bioinformatics • Image Processing • Data Mining • Pattern recognition • etc

8. Biostatistics • Agriculture • Experimental Design • Genetics • Biomedical Research • Evidence-based medicine • Clinical studies • Drug Development

9. Statistical Methods? • t-test • ANOVA • Regression • Cluster analysis • Discriminant analysis • Non-Linear Modeling • Multiple comparison • Linear Mixed Model • Bayesian • Etc, • z

10. Biostatisticians in Drug development

11. Drugs Development • Takes 10-15 years • Cost more than 1 million USD • To ensure that only the drugs that are that are both safe and effective can be marketed. • Stages: - Drug Discovery - Pre-clinical Development - Clinical Development -> 4 Phases Statisticians are involved in all stages (a must)

12. discovery of compound; synthesis and purification of drug substance; manufacturing procedures Pharmaceutical development Pre-clinical (animal) studies pharmacological profile; acute toxicity; effects of long-term usage Investigational New Drug application Phase I clinical trials small; focus on safety medium size; focus on safety and short-term efficacy; Phase II clinical trials Phase III clinical trials large and comparative; focus on efficacy and cost benefits New Drug Application „real world” experience; demonstrate cost benefits; rare adverse reactions Phase IV clinical trials 12

13. International Conference on Harmonization (ICH) • The international harmonization of requirements for drug research and development so that information generated in one country or area would be acceptable to other countries or areas. • Regions: Europe, USA, Japan. • All clinical trials must follow ICH regulations. • Statistics plays important role. • Statistical Principles for Clinical Trials (ICH E9).

14. Preclinical and Clinical Development • Statisticians are involved from the beginning of the study • Planning the study • Formulating the hypothesis • Choosing the endpoint • Choosing the design and sample size • Conduct of the study • Patient accrual • Data collection • Data Quality control, Data analysis • Publication of results

15. bioinformatics

16. Bioinformatics • Bioinformatics is a science straddling the domains of biomedical, informatics, mathematics and statistics. • Applying computational techniques to biology data • Functional Genomics • Proteomics • Sequence Analysis • Phylogenetic • Etc,.

17. “Informatics” in Bioinformatics • Databases • Building, Querying • Object DB • •Text String Comparison • Text Search • Finding Patterns • AI / Machine Learning • Clustering • Data mining • etc

18. Central Dogma of Molecular Biology • Genes contain construction information • All structure and function is made up by proteins

19. Genomics • Premise: Physiological changes -> Gene expression changes -> mRNA abundance level changes • Objective: Use gene expression levels measured via DNA microarrays to identify a set of genes that are differentially expressed across two sets of samples (e.g., in diseased cells compared to normal cells)

20. Microarrays Technology • DNA microarrays are a new and promising biotechnology which allow the monitoring of expression of thousand genes simultaneously

21. Gene Expression Analysis • Overview of the process of generating high throughput gene expression data using microarrays.

22. Preprocessed data Genes C1 C2 C3 T1 T2 T3 G8521 6.89 7.18 6.60 7.40 7.15 7.40 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 G8527 8.28 7.88 7.84 8.12 7.99 7.97 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 5.75

23. Applications • High efficacy and low/no side effect drug • Personalized medicine. • 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, • Appropriate treatment for genetic signature • Potential new drug targets

24. Challenges • Mega data, difficult to visualize • Too few records (columns/samples), usually < 100 • Too many rows(genes), usually > 1,000 • Too many columns likely to lead 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

25. 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

26. Gene Selection • Modified t-test • Significance Analysis of Microarray (SAM) • Limma (Linear model for microarrays ) • Random forest • Lasso (least absolute selection and shrinkage operator) • Linear Mixed model • Elastic-net • Etc,

27. Visualization • Dimensionality reduction • PCA (Principal Component Analysis) • Biplot • Multi dimensional scaling • Etc

28. Clustering • Cluster the genes • Cluster the arrays/conditions • Cluster both simultaneously • K-means • Hierarchical • Biclustering algorithms

29. Clustering • Cluster or Classify genes according to tumors • Cluster tumors according to genes

30. Biclustering • A biclustering method is an unsupervised learning method which looks for sub-matrices in a data matrix with a high similarity of elements. • Algorithms: Statistical based, AI, machine learning. • BiclustGUI: A User Friendly Interface for Biclustering Analysis

31. Bicluster Structure

32. Software/Statistical Packages • Minitab • SAS • SPSS • R • S-Plus • Matlab • Stata

33. 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 (-)

34. Summary • Statisticians can flexibly get involved in many fields. • Only tools, applications are widely range. • Biostatisticians have many opportunities in public health services ( Centers for Disease Control and Prevention, CDC), pharmaceutical companies, research institutions etc. • Statistical Bioinformatics: cutting edge technology -> methods are growing -> many more developments in future.

35. Thank you for your attention... hafidztio@yahoo.com http://setiopramono.wordpress.com