MCB 585: Multidisciplinary/Quantitative Approaches to Solving Biological Problems

Welcome to MCB 585! This course is offered by the Molecular & Cellular Biology Department at the University of Arizona.

MCB585 is an advanced graduate course focused on quantitative approaches to biological questions. Students will learn how to acquire, access, and interact with large biological datasets. The first part of the course relies heavily on learning and utilizing the R Statistical Programming language, but students are not expected to have programming experience upon entering the course. The Fall 2025 course is broken up into two units. The first unit will focus on introducing R and using R in basic statistical operations. This section incorporates elements of experimental design and appropriate application of statistical tests as a frame for learning R. analyzing functional genomic and high-throughput data with an introduction to bioinformatics, machine learning, and data integration.

Instructors:

George Sutphin, Assistant Professor, Molecular & Cellular Biology
Guang Yao, Associate Professor, Molecular & Cellular Biology

Course Goals:

Students will be able to:

Use the R programming environment to approach basic problems in biological sciences.
Apply basic biostatistics methods, such as selecting and employing an appropriate statistical test and conducting a power analysis based on preliminary data.
Access, manipulate, and analyze large biological datasets.
Perform RNA-sequencing analysis starting from raw reads all the way to differential expression and pathway/network analysis.
Understand how statistical computing approaches can be used to integrate and visualize complex data.
Understand basic biological concepts and techniques in functional genomics.

Course Requirements:

Attendance in class sessions.
Participation in discussions and in-class computer exercises.
Reading assignments, completed before class.
Completion of On Your Own content and computer exercises.
Completion of final project proposal, report, and presentation.

Grading Summary:

40% attendance and participation in exercises and discussion
25% completion of assignments
5% final project proposal
15% final project report (including code)
15% final project presentation

Prerequisites

MCB585 is an advanced course intended for second-year graduate students. The pre-requisites for the course are:

One year of graduate-level coursework

Two core courses required for the MCB, BIOC or CMM PhD

Schedule

	Setup	Download files required for the lesson
8/26/2025 -- In Class	1. Getting started with R and RStudio	What is R? What is RStudio? How do I use the RStudio graphical user interface? How do I perform basic calculations in R? How do I assign values to variables in R? What are functions, and how are they used in R?
8/26/2025 -- On Your Own	2. Getting started with R and RStudio -- Additional Detail	How can I write R code that other people can understand and use? How do I manage the variables, files, and memory usage in my workspace? How do I write my own functions? How do I install and load packages to access application-specific tools? Where can I get help?
8/28/2025 -- In Class	3. Basic Data Types and Data Structures in R	What are the most common data types in R? What are the basic data structures in R? How do I access data within the basic data structures?
8/28/2025 -- On Your Own	4. R Data Types -- In-Depth	What are the basic data types in R? What are factors and how do they differ from other data types? How is missing data represented in R? How is infinity represented in R?
9/4/2025 -- In Class	5. Data Frames, Basic Indexing, Reading/Writing Data	What is a data frame? How does indexing differ for data frames relative to basic data structures? How do I access data frame subsets? How do I read data from a .csv or .txt file into R? How do I write data to a .csv or .txt file?
9/9/2025 -- In Class	6. Lists and Advanced Indexing	What are lists and what is their relationship to vectors and data frames? How can we leverage indexing for more advanced data extraction?
9/11/2025 -- In Class	7. Manipulating and Plotting Data	How do I read data from a .csv or .txt file into R? How do I write data to a .csv or .txt file? How do I calculate simple statistics from my data? How can I plot my data? How do I save my plots to a PDF file?
9/11/2025 -- On Your Own	8. Advanced Data Manipulation and Plotting	How do I rapidly calculate statistics based on relevant variables in a dataset? How to I begin to build more complex graphics? What tools are available for generating publication quality graphics in R?
9/16/2025 -- In Class	9. Decision Making and Loops	How do I write code to make decisions about data? How do I use the same code to treat different data sets in different ways? How can I perform the same operations on multiple data sets or across multiple subsets of a single dataset?
9/16/2025 -- On Your Own	10. Decision Making and Loops -- Additional Detail	What operators and functions are available for running test in if statements? Are there different indexing strategies for different situations using a for loop? Should I use a loop or an apply statement?
9/18/2025 -- In Class	11. Distributions and Normality	What is the difference between the population and sampling distribution? Which distribution is more important for hypothesis testing? What is the normal distribution and what are it’s attributes? How can I tell if my data is normally distributed?
9/18/2025 -- On Your Own	12. Distributions and Normality -- Additional Detail	Is there a statistical method for assessing normality? What options do I have if my data is not normally ditributed?
9/23/2025 -- In Class	13. Hypothesis Testing	What is the formal process for hypothesis testing? How does hypothesis testing relate to the population and sampling distributions? What are the assumptions of a t-test? What does a P-value mean? What is the right statistical test to use for my data?
9/23/2025 -- On Your Own	14. Multiple Test Correction	What is the consequence of running multiple statistical comparisons? How do we define a family of tests? What are the strategies for controlling error in multiple testing?
9/25/2025 -- In Class	15. Survival Analysis	How is time-to-event data structured? What are the elements that separate time-to-event data from single-point observations? How do we visualize time-to-event data? What statistical tests are available for time-to-event data hypothesis testing?
9/25/2025 -- On Your Own	16. Advanced Survival Analysis	How do you extract life table information from survfit() objects in R? How do you plot age-specific mortality (aka the hazard function) when a life table is not provided?
9/30/2025 -- In Class	17. Power Analysis	What is power analysis and what is its primary goal? What aspects of an experimental design can be tweaked to prepare for statistical testing? What is our primary tool for increasing power to detect?
10/2/2025 -- On Your Own	18. Simulating Experiments	What is a simulated experiment? What are the applications for simulated experiments? How can we use simulation to improve (and generalize) power analysis?
12/3/2024, 12/5/2024, 12/10/2024	19. Final Projects	What are the requirements of the MCB 585 individual class projects?