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.

Resources

Moodle Link

In the programs

  • Semester: Spring
  • Exam form: During the semester (summer session)
  • Subject examined: Statistics for genomic data analysis
  • Lecture: 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
  • Lecture: 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
  • Lecture: 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
  • Lecture: 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
  • Lecture: 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
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Type: optional

Reference week

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