BMS 625 - Biostats/Computational Biology
This class is an introduction to biostatistics with application to the biomedical sciences and genetics, and introduction to computational biology.
The goal this course is for students to understand and apply the principles of statistics to appropriately design research studies, analyze the collected data, draw appropriate conclusions, and test assumptions. Students will learn basic modeling techniques such as linear regression, analysis of variance (ANOVA), and basic categorical data analysis. Students will be introduced to a sampling of advanced techniques and topics such as multivariate data analysis, missing data, multiple testing, survival analysis, and nonparametric methods. The overall goal is for students to have a broad understanding of good research design as well as understand some of the basic statistical methods available for the analysis of different types of data sets.
This class is offered in the Spring.
- Introduction to Statistics: sampling theory, distributions, summary statistics, types of variables, hypothesis testing, confidence intervals
- Introduction to the R language, using Excel and R for basic statistical analysis, summary of graphing capabilities
- Regression and Correlation
- T-tests and ANOVA
- Categorical data analysis, Multivariate Analysis
- Study design and power analysis; Advanced topics: survival analysis, nonparametric methods, mixed models, missing data, multiple testing
- Introduction to genome-scale data: types of experiments, current technologies, experimental output
- Analysis of genome-scale data: quality assessment and correction methods
- Analysis of genome-scale data: dimensional reduction by data clustering
- Analysis of genome-scale data: functional assessment by integrating annotation data
- Basics of genetic association mapping and quantitative trait loci: data types and regression models
- Advanced topics in genetic association studies: linkage disequilibrium, population stratification, and model systems.
- Advanced topics in genetic association studies: epistasis and pleiotropy