Statistics for genomic data analysis
MATH-474 / 5 credits
Teacher: Goldstein Darlene
Language: English
Remark: Cours donné en alternance tous les deux ans
Summary
After a short introduction to basic molecular biology and genomic technologies, this course covers the most useful statistical concepts and methods for the analysis of genomic data.
Content
- Molecular biology and technology background
- R software and BioConductor packages
- Robust regression/High-density oligo array signal quantification/Quality assessment for Affymetrix GeneChips
- Empirical Bayes method for identifying differentially expressed genes
- Linear models for designed experiments
- Hypothesis testing, ROC curves, multiple hypothesis testing
- Gene set testing
- Cluster analysis
- Classical and machine learning methods for classification
- Sequence data (NGS) analysis
- Generalized linear modeling for differential expression (NGS)
- Additional topics as time permits: e.g. Meta-analysis, genome-wide association studies (GWAS)
Keywords
statistics; statistical methods; data analysis; DNA; RNA; mRNA; genomics; genomic data; microarray; sequencing data; NGS; NGS technologies; machine learning; R statistical software; BioConductor
Learning Prerequisites
Important concepts to start the course
Elementary statistics
Previous experience with R is helpful (but not necessary)
Learning Outcomes
By the end of the course, the student must be able to:
- Apply appropriate methods to analyze genomic data
- Carry out targeted analyses of genomic data
- Design genomic experiments
Transversal skills
- Access and evaluate appropriate sources of information.
- Write a scientific or technical report.
Teaching methods
Lectures and computer practical exercises
Expected student activities
Regular attendance in class, practical exercises, prepare a short report (max. 10 pages) on an analysis of genomic data using tools and methods from the course
Assessment methods
Evaluation is based on a written report of a genomic data analysis project.
Dans le cas de l'art. 3 al. 5 du Règlement de section, l'enseignant décide de la forme de l'examen qu'il communique aux étudiants concernés.
In the programs
- Semester: Spring
- Exam form: During the semester (summer session)
- Subject examined: Statistics for genomic data analysis
- Courses: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Spring
- Exam form: During the semester (summer session)
- Subject examined: Statistics for genomic data analysis
- Courses: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Spring
- Exam form: During the semester (summer session)
- Subject examined: Statistics for genomic data analysis
- Courses: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Spring
- Exam form: During the semester (summer session)
- Subject examined: Statistics for genomic data analysis
- Courses: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Spring
- Exam form: During the semester (summer session)
- Subject examined: Statistics for genomic data analysis
- Courses: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Exam form: During the semester (summer session)
- Subject examined: Statistics for genomic data analysis
- Courses: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional