July 15, 2000
Statistics for Molecular Biology and Bioinformatics: A computer intensive introductory course for graduate students
 Ron S. Kenett, Ph.D.

Over the past 45 years, the Double Helix has revolutionized biology, transforming it into Molecular Biology, the dissection of complex biological phenomena into specific molecules and their functions. Research in this area spans a wide range of domains, from the molecular basis and treatment of disease, to biochemistry, to development biology.  Modern high-throughput genomic technologies such as genome sequencing and whole genome expression analysis are now transforming the biological sciences again.  Bioinformatics is a new field of study that seeks to find relationships between genes. The problem/opportunity for Bioinformatics is the flood of data from genomics which must be analyzed in order to decipher the inherent structure of information within the data. Complementary to this effort is a problem of synthesis, reassembling our understanding of molecular pieces into predictive models of the biology as a composite system. 

Modern Statistical Methods are supporting this evolution, filling the gap created by new forms of analysis that are required to get results from genomics data and creating predictive models.

Statistics for Molecular Biology and Bioinfornatics is a graduate class designed to cover various aspects of statistics that are fundamental to modern biology. The course will be delivered in a computer lab environment in order to provide the students with hands on experience with statistical software and relevant case studies. Emphasis will be given to application and implementation and not on statistical theory.

The course will be partially based on the text Modern Industrial Statistics: Design and Control of Quality and Reliability by Ron S. Kenett, and S. Zacks, Duxbury Press, 1998. (Chapters indicated below refer to this text)

Syllabus

1.  Understanding Variability (Chapter 2)
Descriptive analysis of sample data, techniques of exploratory data analysis.
2.  Variability in Several Dimensions (Chapter 3)
Graphical display and analysis, dynamic graphics, regression analysis, contingency tables.
3.  Basic Models of Probability and Distribution Functions (Chapter 4)
Random variables, discrete and continuous distributions, multivariate distributions, order statistics.
4.  Sampling for Estimation of Finite Population Quantities (Chapter 5)
Sampling and the estimation problem.
5.  Parametric Statistical Inference (Chapter 6)
Estimators, confidence intervals, tests of goodness of  fit, Analysis Of Variance.
6.  Distribution Free Inference: Computer Intensive Techniques (Chapter 7)
Bootstrap sampling, bootstrap testing of hypothesis, nonparametric tests. 
7.  Multiple Linear Regression and Analysis of Variance (Chapter 8)
Regression on two variables, logistic regression, categorical data analysis.
8. Classical Design and Analysis of Experiments (Chapter 12)
Randomized designs, factorial designs, fractional factorial designs, central composite designs.