BMI206: Statistical Methods
OVERVIEW – SYLLABUS – READING – LABS – TUTORIALS – PROJECT
This course covers a survey of bioinformatics research areas and statistical methods needed to analyze data in these areas. The overall goal is to empower students to select and implement appropriate statistical analyses in research problems with large and complex data structures.
The general format will be to learn about statistical methods in the context of specific bioinformatics research topics through lecture, board work, and code examples; to discuss recent papers that use statistical methodology; and to implement the methods we learn about with genome-scale data in computer labs.
Some of the statistical methods are:
linear (LMs) and generalized linear models (GLMs),
categorical data analysis,
multiple hypothesis testing,
bootstrap and permutations,
Bioinformatics topics include:
functional genomics (RNA-seq, ChIP-seq, ATAC-seq),
genetic variation (eQTLs, GWAS),
biological networks (protein interactions, metabolic pathways).
Students are expected to have knowledge of undergraduate statistics, math, and molecular biology. The UCSF online biostatistics course is a required pre-requisite. A UCSF login is required to access the course material. Students should complete the course or ensure that they covered the topics in it through prior course work. BMI and several other programs offer TA guided sessions for this online course. In addition, students should read and be familiar with the material in Empowering statistical methods for cellular and molecular biologists, including the tutorial in supplemental materials.
Students should have significant prior experience writing code in at least one language. Labs will primarily use the R programming language, so familiarity with R is helpful. BMI covers introductory programming and algorithms prior to BMI206 in the Fall quarter. The UCSF Library, Gladstone Institutes, and Bakar Institute offer a variety of programming workshops. In BMI206, we focus on writing code to explore statistical models, so the emphasis is not on how to program or run specific pipelines.
This course runs for eight weeks and is therefore very fast-paced. Students are encouraged to prepare to get the most out of class by reviewing materials/topics before class and doing background reading or online courses as needed. See these resources and other background material for places to get started. Attendance and participation in class contributes to each student’s grade. Please plan Thanksgiving and winter break travel so as not to miss class.
Grades will be based on labs, class participation, and a project.
You are welcome to use the online materials for a self-guided experience. Classroom auditors may be possible if there are enough seats; priority goes to registered students.
Students with Disabilities
The Graduate Division embraces all students, including students with documented disabilities. UCSF is committed to providing all students equal access to all of its programs, services, and activities. Student Disability Services (SDS) is the campus office that works with students who have disabilities to determine and coordinate reasonable accommodations. Students who have, or think they may have, a disability are invited to contact SDS (StudentDisability@ucsf.edu); or 415-476-6595) for a confidential discussion and to review the process for requesting accommodations in classroom and clinical settings. More information is available online at https://sds.ucsf.edu. Accommodations are never retroactive; therefore students are encouraged to register with Student Disability Services (https://sds.ucsf.edu/) as soon as they begin their programs. UCSF encourages students to engage in support seeking behavior via all of the resources available through Student Life, for consistent support and access to their programs.