In addition to the general GSBS course requirements, the QS Program requires the following courses:
- Introduction to Bioinformatics GS01 1143
- Biostatistics for Life Sciences GS14 1612 (except for Biostatistics Track)
- Quantitative Sciences Student Seminar Series GS01 1031 (enroll every fall semester unless student has a direct course conflict)
- Journal Club (attend for one semester during degree program -- may be done after candidacy exam)
- Click here for Journal Club options
QS students are also expected to take courses required by one of the Program's four tracks as listed in the section below.
Program course requirements for MS students:
- GS01 1143 Introduction to Bioinformatics
- GS14 1612 Biostatistics for Life Sciences
- GS01 1031 Quantitative Sciences Student Seminar
Students who declare a Secondary Area of Concentration in Quantitative Sciences are required to take:
All of the QS program required courses or equivalent quantitative courses on a case-by case basis as approved by the Program Director
- You will be required to add a QS faculty to your advisory committee
- Please reach out to the QS Director for any further questions or concerns
Quantitative Sciences Program Prerequisites:
- Biostatistics Track – background requirements: 1) college-level calculus 2) linear algebra 3) and statistics. It is highly recommended that students have an MS degree in Statistics or Biostatistics.
- Bioinformatics/Systems Biology/Quantitative Genomics Tracks – experience with programming in R or Python is highly recommended.
For questions regarding the curriculum for the Quantitative Sciences program, please email email@example.com
Quantitative Sciences (QS) Program students are required to take an on-topic candidacy exam in which the research proposal is based on the student's project. The format follows the GSBS on-topic format and also includes a breadth of knowledge component. Time at the end of the exam will be used for each examiner to ask one breadth of knowledge question, plus follow-up questions as warranted.
The question guidelines are as follows:
- topics covered in class the student has already taken
- topics not already contained in the students grant proposal
- at least one biology or clinical practice-based question, as appropriate
- should be general questions about concepts that lead to follow-up that examines the boundaries of the student’s knowledge and ensures student has a firm understanding of basic principles of the field
- one question per examiner; 20 minutes total at the end of the exam
Please see the list of the Quantitative Sciences Program Candidacy Exam Committee Members below.
- Students should select 2 members from the list below to be on their exam committee (can select any 2 from the list below)
- Students should select their exam committee chair from this list
Candidacy Exam Committee Members:
Jeff Chang, PhD
Traver Hart, PhD
Prahlad Ram, PhD
Kim-Anh Do, PhD
Eduardo Vilar Sanchez, MD, PhD
Nidhi Sahni, PhD
Goo Jun, PhD
Xiaodong Zhang, PhD
Peng Wei, PhD
Linghua Wang, PhD
Ruitao Lin, PhD
Ziyi Li, PhD
James Long, PhD
Basic and Translational Cancer Biology
GS04 1235 (5 credits)
Hu, Jian; Ying, Haoqing. Five semester hours. Spring, annually. Grading System: Letter Grade. Prerequisite: None. Audit Permitted.
The Cancer Biology Core course will synthesize knowledge of critical aspects
inhuman cancer biology for understanding disease development, multidimensional molecular signatures, diagnostics, and therapeutics.
Curriculum Committee Commended Course for Academic Year 2020-2021
Biostatistics for Life Scientists
GS14 1612 (2 credits)
Liu, Yin. Two semester hours. Spring, annually. Grading System: Letter Grade. Prerequisite: None.
This is an entry-to-intermediate level course of biostatistics aimed at scientists in the life sciences. During the first half of the semester, the course will introduce students to the basic concepts and statistical tests that are routinely encountered in analyzing scientific data in designed experiments, as opposed to the analysis of clinical or epidemiological type data. Following an introduction to probability, students will learn what statistical tests are appropriate and how to run them. Emphasis is on intelligent usage rather than mathematical formality. Standard tests such as t, z,
chi squared, ANOVA and regression analyses will be learned, as well as how power analyses and calculating sample size is performed. During the second half of the semester, advanced topics in life sciences, including Poisson distributions, clustering methods and multidimensional analyses will be covered. Another goal of this course will be to build familiarity with the basic R toolkit for statistical analysis and graphics.
Foundations of Biomedical Research for Quantitative Students
GS21 1018 (7 credits)
Lorenz, Michael; Arur, Swathi. Seven semester hours. Fall, annually. Grading System: Pass or Fail. Prerequisite: Student must obtain permission from Dr. Mattox. The enrollment is limited to GSBS first-year and second-year students who will pursue the quantitative degree track.
This course will provide incoming graduate students with a broad overview of modern biomedical sciences, spanning historical perspectives to cutting edge approaches. The course combines traditional didactic lectures and interactive critical thinking and problem solving exercises to provide students with a strong background in fundamental graduate-level topics including genetics, molecular and cellular biology, biochemistry, physiology, developmental biology and biostatistics. This is the GSBS Core Course which will be graded pass/fail and together with Introduction to Biostatistics and Bioinformatics (GS01 1033) fulfills the GSBS breadth requirement for quantitative-track students.
Foundations of Statistical Inference II
GS01 1283 (3 credits)
Long, James. Three semester hours. Spring, annually. Grading System: Letter Grade. Prerequisite: Rice STAT 532. Audit permitted.
This is the second semester course in a two-semester sequence in mathematical statistics. The course topics include random variables, distributions, small and large sample theorems of decision theory and Bayesian methods, hypothesis testing, point estimation, and confidence intervals; also topics such as exponential families, univariate and multivariate linear models, and nonparametric inference will also be discussed. This course is cross-listed at Rice STAT 533. The venue of the course will be at Rice University.
Genetics and Human Disease
GS11 1013 (3 credits)
Hanis, Craig. Three semester hours. Fall, annually. Grading System: Letter Grade. Prerequisite: consent of instructor; general genetics and statistics recommended
This course introduces principles and methods of human genetic analysis with special reference to the contribution of genes to our burden of disease. Although molecular, biochemical and morphogenic processes controlled by genes will be briefly surveyed, the aim is to describe the analytical processes whereby genetic mechanisms are inferred and genes located on chromosomes.
Introduction to Bioinformatics
GS01 1143 (3 credits)
Spring and Fall
andNavin, Nicholas. Three-credit hour. Fall and Spring annually. Grading System: Letter Grade.
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.
Introduction to Biostatistics and Clinical Trials
GS01 1033 (3 credits)
Yuan, Ying; Liu, Suyu. Three semester hours. Spring, annually. Grading System: Letter Grade. Prerequisite: Calculus and linear algebra
This course is a one-semester overview of statistical concepts most often used in the design and analysis of biomedical studies. It provides an introduction to the analysis of biomedical and epidemiological data. The focus is on non-model-based solutions to one sample and two sample problems. The course also includes an overview of statistical genetics and bioinformatics concepts. Because this course is primarily for statistics majors, the applied methods will be related to theory whenever practical. Students will gain experience in the general approach to data analysis and in the application of appropriate statistical methods. Emphasis will be on the similarity between various forms of analysis and reporting results in terms of measures of effect or association. Emphasis will also be given to identifying statistical assumptions and performing analyses to verify these assumptions. Because effective communication is essential to effective collaboration, students will gain experience in presenting results for statistically naive readers.
Introduction to Statistical Genetics
GS11 1113 (3 credits)
Fu, Yun-Xin. Three semester hours. Fall, annually. Grading System: Letter Grade. Prerequisite: Consent of instructor.
This course is designed as an introduction to statistical genetics/computational biology, and serves as the entry point to several courses in this area. It reviews the key statistical concepts and methods relevant to statistical genetics, discusses various topics that have significant statistical component in genetics, particularly in population and quantitative genetics. Topics include estimation of gene frequencies, segregation analysis, test of genetic linkage, genetics of quantitative characters, inheritance of complex characters, forensic science and paternity testing, phylogeny and data mining. This course is cross-listed at School of Public Health (PH1986L). The venue will be at School of Public Health.
GS01 1273 (3 credits)
Wei, Peng; Li, Yisheng; and Wang, Jian. Three semester hours. Spring, biennial. Grading System: Letter Grade. Prerequisites: GS01 1083: Mathematical Statistics I (or equivalent) and Linear Regression or Consent of Instructor.
This course seeks to introduce students to the many developments in modern
, 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. nonparametrics
Quantitative Sciences Student Seminar Series
GS01 1031 (1 credits)
Li, Liang. One semester hour. Fall, annually. Grading System: Pass/Fail. 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.
GS01 1023 (3 credits)
Sun, Ryan; Huang, Xuelin. Three semester hours. Spring, odd-numbered years. Grading System: Letter Grade. Prerequisite: Introduction to Biostatistics and Bioinformatics (GS01 0033) or permission 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 ranktest, 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 appliedwork. The Cox proportional hazard mode, additive risk model, other alternative modeling techniques, and new theoretical and methodological advances in survival analysis will be discussed.
Topics in Clinical Trials
GS01 1813 (3 credits)
Lin, Ruitao. Three semester hours. Fall, even years. Grading System: Letter Grade. Prerequisite: Prior courses in probability and statistics, permission of the instructor. Cross-listed course at Rice STAT 630.
This course will provide an overview of methods for the design and analysis of clinical trials. Topics will include fundamental principles and commonly used designs for phases I, II and III trials. Advanced topics will include flaws with many conventional methods, hybrid designs, dealing with multiple outcomes, bias correction, precision medicine, and Bayesian methods.