Data analysis for gene expression (2-5 cu)

Introduction

Microarray technology has made it possible to monitor large-scale gene expression (the level of activation of a gene) and has become incredibly popular in genetics research. This high throughput technique can provide information for thousands of genes in parallel and is producing huge amount of valuable data (data sets can easily have tens, hundreds of thousands or even millions of data points). This necessitates use of complex data-analysis tools for processing and data mining of this type of genomic data to understand the underlying genetic networks and to answer the complex biological questions involved

This course is designed to introduce computational and statistical concepts and tools necessary to analyse microarray based gene expression data, a skill that is in high demand by biotechnology, bioinformatics and pharmaceutical companies. The skills learned in this course will also be applicable to other problems involving large data sets, such as data mining and proteomics.

Date and place

• Lectures: 11.10. - 15.10. at 9-14 in hall CK107, Exactum building, Kumpula campus
• Seminar: 15.11. - 16.11. at 9-14 in the same place

Staff

The course is arranged in collaboration between Department of Computer Science, Department of Mathematics and Statistics, and Institute of Biotechnology.

Lecturers:

• Prof. Samuel Kaski, Department of Computer Science
• Dr. Madhuchhanda Bhattacharjee, Department of Mathematics and Statistics
• Doc. Petri Auvinen, Institute of Biotechnology

Course assistant:

• M.Sc. Arto Klami, Department of Computer Science, arto.klami@cs.helsinki.fi

Required background

The course is targeted to advanced (laudatur-level) students of computer science, statistics, and applied mathematics, but students from other fields are welcome as well. In particular mathematically oriented biology, bioinformatics. and medical students should benefit from the course.

Basic knowledge of probability, statistics, vector algebra, and calculus is assumed.

Format

The course comes in three flavors:

1. Lecture course (2cu). Requirements: presence and an essay.
2. Lecture course + exercises (3cu): Requirements: (A) plus a set of homework exercises.
3. Lecture course + exercises + project work + seminar (5cu): Requirements: (B) plus a project work and presentation of its results in a seminar

The seminar part has an upper limit of 16 students. Major students of the participating departments have precedence. Registration to the seminar during the first day of lectures.

Registration

The course has two different codes, one for the Department of Computer Science and one for the Department of Mathematics and Statistics. You have to choose which one you want to have in your records.

If computer science, register electronically according to the instructions at http://www.cs.helsinki.fi/opiskelu/ohjeet/ilmoittautuminen-en.htm

If mathematics and statistics, register electronically at http://www.math.helsinki.fi/kurssit/genexp.htm

If these do not work, email arto.klami@cs.helsinki.fi (comp.sci) or mab@rni.helsinki.fi (math/stat).

Lectures

Preliminary list of topics

• Introduction to genome biology
• Introduction to microarray techniques
• Preprocessing: image analysis and normalization
• Experimental design
• Finding expressed genes
• Classification
• Clustering and information visualization
• Basics on regulation
• Inference of regulatory networks
• New high-throughput measurements
• Data fusion

Project work and seminar

The seminar part goes deeper into current research problems. All participants choose one research problem from the recent literature, carry out a small-scale research project on the topic, in groups of 1-4 students, and report on the topic and their results in a mini-conference having a standard peer-review protocol.

Updated information about the course will be available at

http://www.cs.helsinki.fi/u/aklami/courses/genexp/