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Quantitative Sciences

The study of biostatistics, bioinformatics, systems biology and genomics focuses on developing and applying statistical and mathematical models in close collaboration with biomedical researchers. The mission of the Program in Quantitative Sciences is to train researchers who will contribute to biomedical research by developing new methods for the design and analysis of research studies and by formulating mathematical models of biologic systems, thereby contributing to our understanding of cancer biology and disease processes. The Quantitative Sciences Program currently has around 50 students and 70 faculty.

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)

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 protected]

Quantitative Sciences Tracks

  • Bioinformatics Track

    In addition to the general QS Program requirements, the Bioinformatics Track requires students to take the following courses:

    Course Requirements for PhD students who matriculated in or after 2019

    1. BMI 5007 (UT-BMI) – Methods in Health Data Science
    2. PH1976 (UT-SPH) – Fundamentals of Data Analytics & Predictions
      Note: PH1976 has the following prerequisites: PH1700 or equivalent; PH1975; and calculus, linear algebra, basic statistical theory and convex optimization methods at the introductory level.
      1. alternate course option: BMI 6323 (UT-BMI) – Machine Learning in Biomedical Informatics
    3. GS11 1113 – Introduction to Statistical Genetics
    4. Bioinformatics students must also select 1 bio-related elective course

    *Bioinformatics Track – experience with programming in R or Python is highly recommended.

    Electives:

    1. BMI 5304 (UT-BMI) - Advanced Database Concepts in Biomedical Informatics
    2. COMP 557 (Rice) - Artificial Intelligence
    3. COMP 602 (Rice) - Neural Machine Learning II
    4. STAT 615 (Rice) – Regression and Linear Models
      1. Alternate course option: PH1915 (UT-SPH) – Linear Regression
    5. STAT 519 (Rice) - Statistical Inference
    6. STAT 648 (Rice) - GRAPH Models & Networks

    Bioinformatics Track Timeline

    Year 1, Fall

    QS Student Seminar Series - GS01 1031

    Year 1, Spring

    Introduction to Bioinformatics - GS01 1143

    Biostatistics for Life Sciences - GS14 1612

    Fundamentals of Data Analytics & Predictions - PH1976 (UT-SPH) or BMI 6323 (UT-BMI)

    Year 2, Fall

    Introduction to Statistical Genetics - GS11 1113

    Methods in Health Data Science - BMI 5007 (UT-BMI)

    QS Student Seminar Series - GS01 1031

    1 Bio-related elective course

    Year 2, Spring

    Scientific Writing - GS21 1152

    Journal Club (attend for one semester during degree program - may be done after candidacy exam)


    Course Requirements for PhD students who matriculated prior to 2019

    1. BMI 5007 (UT-BMI) Methods in Health Data Science
    2. SPH 1976 (UT-SPH) Fundamentals of Data Analytics & Predictions – Or BMI 6323 (UT-BMI)
    3. BIOC 571 (Rice) Sequence Analysis
    4. 1 Bio-related Elective course
  • Biostatistics Track

    In addition to the general QS Program requirements, the Biostatistics Track requires students to take the following courses:

    Course Requirements for PhD students who matriculated in or after 2019

    1. GS01 1033 - Introduction to Biostatistics and Clinical Trials
    2. GS01 1023 – Survival Analysis
      1. Alternate course option: PH1831 (UT-SPH) – Survival Analysis

    *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.

    Electives:

    1. STAT 518 (Rice)- Probability (Required for students with no prior background)
      1. Alternate course option: PH1910 (UT-SPH) Theory of Biostatistics I
    2. STAT 519 (Rice) - Statistical Inference (Highly encouraged for students with no prior background)
      1. Alternate course option – PH1911 (UT-SPH) Theory of Biostatistics II
    3. STAT 532 (Rice) - Foundations of Statistical Inference I
    4. PH1915 (UT-SPH) - Linear Models I (highly recommended for students with no prior background)
    5. STAT 605 (Rice) - R for Data Science (highly recommended for students with no prior background)
      1. Alternate course option – PH1975 (UT-SPH) Introduction to Data Science
    6. PH1918 (UT-SPH) - Statistical Methods in Correlated Outcomes
    7. PH1835 (UT-SPH) - Statistical Methods in Clinical Trials
    8. GS01 1233 - GLM and Categorical Data Analysis
      1. Alternate course option – PH1830 (UT-SPH) Categorical Data Analysis
      2. Alternate course option – PH1916 (UT-SPH) Generalized Linear Models
    9. GS01 1273 - Modern Nonparametrics
    10. GS01 1283 - Foundations of Statistical Inference II (Rice STAT 533)
    11. GS01 1813 – Topics in Clinical Trials (Rice STAT 630)

    Biostatistics Track Timeline

    Year 1, Fall

    QS Student Seminar Series - GS01 1031

    (STAT 518 (Rice) - Probability - required for students with no prior background)
    (Strongly encourage to take PH1915 (UT-SPH) or STAT 615 (Rice) - Linear Regression for students with no background)
    (Strongly encourage to take PH1975 (UT-SPH) or STAT 605 (Rice) - R for Data Science for students with no background)

    Year 1, Spring

    Introduction to Bioinformatics - GS01 1143

    Introduction to Biostatistics and Clinical Trials - GS01 1033

    Survival Analysis - GS01 1023

    (Strongly encourage to take STAT 519 (Rice) Statistical Inference for students with no background)

    Year 2, Fall

    QS Student Seminar Series - GS01 1031

    Journal Club (attend for one semester during degree program - may be done after candidacy exam)

    Year 2, Spring

    Scientific Writing - GS21 1152

    Course Requirements for PhD students who matriculated prior to 2019

    1. STAT 518 (Rice) - Probability
      1. Alternate course option: PH1910 (UT-SPH) Theory of Biostatistics I
    2. PH1915 (UT-SPH) - Linear Models I
    3. STAT 605 (Rice) - R for Data Science
      1. Alternate course option – PH1975 (UT-SPH) Introduction to Data Science
    4. GS01 1233 - GLM and Categorical Data Analysis
      1. Alternate course option – PH1830 (UT-SPH) Categorical Data Analysis
      2. Alternate course option – PH1916 (UT-SPH) Generalized Linear Models
    5. GS01 1023 – Survival Analysis
      1. Alternate course option: PH1831 (UT-SPH) – Survival Analysis
  • Systems Biology Track

    In addition to the general QS Program requirements, the Systems Biology Track requires students to take the following courses:

    Course Requirements for PhD students who matriculated in or after 2019

    1. GS04 1235 Basic & Translational Cancer Biology
    2. PH1976 (UT-SPH) Fundamentals of Data Analytics & Predictions
      Note: PH1976 has the following prerequisites: PH1700 or equivalent; PH1975; and calculus, linear algebra, basic statistical theory and convex optimization methods at the introductory level.
      1. alternate course option: BMI 6323 (UT-BMI) – Machine Learning in Biomedical Informatics
    3. GS11 1113 Introduction to Statistical Genetics     

    *Systems Biology Track – experience with programming in R or Python is highly recommended.

    Electives:

    1. GS01 1133 Statistical Methods in Bioinformatics
    2. STAT 519 (Rice) Statistical Inference
    3. GS04 1183 Molecular Methods and Bioinformatics
    4. BIOC 570 (Rice) Computation with Biological Data
    5. BMI 5007 (UT-BMI) Methods in Health Data Science
    6. BMI 5304 (UT-BMI) Advanced Database Concepts in Biomedical Informatics
    7. GS11 1013 Genetics & Human Disease
    8. GS11 1033 Quantitative Methods in Genetic Epidemiology
    9. Other graduate level courses offered by Statistics at Rice and Biostatistics at UT-SPH, guided by mentor and program director

                                                                                     

     Systems Biology Track Timeline

    Year 1, Fall

    QS Student Seminar Series - GS01 1031                                                                                                                                                                         

    Year 1, Spring

    Introduction to Bioinformatics - GS01 1143

    Biostatistics for Life Sciences - GS14 1612

    Basic & Translational Cancer Biology - GS04 1235

    Fundamentals of Data Analytics & Predictions - PH1976 (UT-SPH)              

    Year 2, Fall

    Introduction to Statistical Genetics - GS11 1113

    QS Student Seminar Series - GS01 1031

    Journal Club (attend for one semester during degree program - may be done after candidacy exam)  

    Year 2, Spring

    Scientific Writing - GS21 1152

  • Quantitative Genomics Track

    In addition to the general QS Program requirements, the Quantitative Genomics Track requires students to take the following courses:

    Course Requirements for PhD students who matriculated in or after 2019

    1. PH1830 (UT-SPH) Categorical Data Analysis
             1. alternate course option: STAT 545 (Rice) GLM & Categorical Data Analysis
             2. alternate course option: PH1916 (UT-SPH) Generalized Linear Models
    2. PH1976 (UT-SPH) Fundamentals of Data Analytics & Predictions
      Note: PH1976 has the following prerequisites: PH1700 or equivalent; PH1975; and calculus, linear algebra, basic statistical theory and convex optimization methods at the introductory level.
      1. alternate course option: BMI 6323 (UT-BMI) – Machine Learning in Biomedical Informatics
    3. GS11 1113 Introduction to Statistical Genetics
    4. GS11 1013 Genetics of Human Disease          

    *Quantitative Genomics Track – experience with programming in R or Python is highly recommended.

    Electives:

    1. GS11 1092 Genetic Epidemiology of Chronic Disease
    2. GS11 1033 Quantitative Methods in Genetic Epidemiology
    3. GS11 1711 Seminar in Genetics & Population Biology
    4. GS11 1103 Evolution of DNA and Protein Sequences
    5. PH2780 (UT-SPH) Applied Genetic Methods in Public Health
    6. Other graduate level courses offered by Statistics at Rice and Biostatistics at UT-SPH, guided by mentor and program director

            

    Quantitative Genomics Track Timeline

    Year 1, Fall

    QS Student Seminar Series - GS01 1031

    Categorical Data Analysis - PH1830 (UT-SPH)

    Year 1, Spring

    Introduction to Bioinformatics - GS01 1143

    Biostatistics for Life Sciences - GS14 1612

    Fundamentals of Data Analytics & Predictions - PH1976 (UT-SPH)

    Year 2, Fall

    Introduction to Statistical Genetics - GS11 1113

    Genetics of Human Disease - GS11 1013

    QS Student Seminar Series - GS01 1031

    Journal Club (attend for one semester during degree program - may be done after candidacy exam)

    Year 2, Spring

    Scientific Writing - GS21 1152

Candidacy Exam

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

Program Requirements

Course Descriptions

  • Basic and Translational Cancer Biology
    Course Detail

    GS04 1235 (5 credits)
    Spring

    The Cancer Biology Core course will synthesize knowledge of critical aspects in human cancer biology for understanding disease development, multidimensional molecular signatures, diagnostics, and therapeutics. Auditing this is permitted with Course Directors' approval.     

    >> Curriculum Committee commended course for Academic Year 2020-2021 <<

  • Biostatistics for Life Scientists
    Course Detail

    GS14 1612 (2 credits)
    Spring

    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
    Course Detail

    GS21 1018 (7 credits)
    Fall

    Enrollment in this course 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.

    >> Curriculum Committee Commended Course for Academic Year 2021-2022 <<

    Course Web Page

  • Foundations of Statistical Inference II
    Course Detail

    GS01 1283 (3 credits)
    Spring

    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. Audit is permitted with Course Director's consent. 

  • Genetics and Human Disease
    Course Detail

    GS11 1013 (3 credits)
    Fall

    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
    Course Detail

    GS01 1143 (3 credits)
    Spring

    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
    Course Detail

    GS01 1033 (3 credits)
    Spring

    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 and Bioinformatics
    Course Detail

    GS11 1113 (3 credits)
    Fall

    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.

  • Modern Nonparametrics
    Course Detail

    GS01 1273 (3 credits)
    Spring

    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. 

  • Quantitative Sciences Student Seminar Series
    Course Detail

    GS01 1031 (1 credits)
    Fall

    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.

  • Survival Analysis
    Course Detail

    GS01 1023 (3 credits)
    Spring

    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.

  • Topics in Clinical Trials
    Course Detail

    GS01 1813 (3 credits)
    Fall

    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. This course is cross-listed at Rice University as STAT 630.