Biostatistics, Bioinformatics, and Systems Biology (GS01)

Prerequisites: Students are expected to have knowledge in basic Statistics Inference, Probability, and Linear Regression. Prior programming experience in R or Python is required. Consent of Instructor is also required. This course is designed to help students build essential statistical communication skills that are often underemphasized in traditional training. It focuses on preparing students to collaborate effectively with researchers from diverse backgrounds by teaching them how to: effectively interview collaborators to understand their research questions and objectives, articulate mutual goals and expectations specific to statistical consulting and interdisciplinary collaboration, define statistical objectives and deliverables that can guide the research process, and provide regular progress updates in a clear and actionable manner. Through two core components, a consulting clinic and a project-based learning curriculum, students gain hands-on experience in applying statistical and data science knowledge while refining their communication and collaboration skills. In the consulting clinic, students offer pro bono statistical consulting services to UTHealth/MDA community researchers, working under instructor supervision to apply these skills in real-world settings. The project-based learning component focuses on project scoping, collaborative practices, and reproducible workflows, equipping students with tools to translate research questions into actionable solutions and foster productive interdisciplinary collaborations. This course will prepare students for professional collaborations in academic and industry settings. Note: Students who are interested in the course but not sure whether they meet the prerequisites can contact the course directors. If the registration number goes above 12, the course directors will make decisions on who to admit to the course. Priorities will be given to QS program 1st and 2nd year PhD students and those who meet the prerequisites. The directors will provide guidance on preparing the prerequisites, through taking other basic statistical courses in class or through Coursera courses, for those who are interested in taking it in future. Letter Graded

Prerequisite: PH1910L Probability and Distribution Theory and Linear Regression or Consent of Instructor. Survival data are commonly encountered in scientific investigations, especially in clinical trials and epidemiologic studies. In this course, commonly used statistical methods for the analysis of failure-time data will be discussed. One of the primary topics is the estimation of survival function based on censored data, which include parametric failure-time models, and nonparametric Kaplan-Meier estimates of the survival distribution. Estimation of the cumulative hazard function and the context of hypothesis testing for survival data will be covered. These tests include the log rank test, generalized log-rank tests, and some non-ranked based test statistics. Regression analysis for censored survival data is the most applicable to clinical trials and applied work. The Cox proportional hazard mode, additive risk model, other alternative modeling techniques, and new theoretical and methodological advances in survival analysis will be discussed. Letter Graded

Prerequisite: None. This series is held bi-weekly for students to present their research project in front of their peers and program faculty. The focus of the session is for the students to practice presenting their project to a varied audience of peers and mentors. Attendees should be prepared to ask questions of the speaker and to provide constructive criticism. This is a required course for all QS Program students and participation is mandatory. All QS students must register for this course every semester unless the student has a direct course conflict. QS-affiliated students are expected to give a minimum of two talks; one pre-candidacy and one post-candidacy, and secondary ARC students are expected to give a minimum of one talk. Pass/Fail

Prerequisite: Some prior programming experience is highly recommended but is not a requirement. This course is intended to be an introduction to concepts and methods in bioinformatics with a focus on analyzing data merging from high throughput experimental pipelines such as next-gen sequencing. Students will be exposed to algorithms and software tools involved in various aspects of data processing and biological interpretation. Though some prior programming experience is highly recommended, it is not a requirement. Letter Graded

Prerequisites: Mathematical Statistics I (GS01 1083 or equivalent) and Linear Regression or permission of instructor. This course seeks to introduce students to the many developments in modern nonparametrics, including resampling methods, nonparametric and semiparametric regression models that have occurred over the last several decades. Topics include the bootstrap, jackknife, cross-validation, permutation tests, classification tree, random forests, nonparametric smoothing and regression, spline regression, and functional data analysis. While the course will focus on applications, time will be devoted to derivations and theoretical justifications of methods. The statistical software R will be used for the homework exercises. Letter Graded