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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

Staff

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

Lecturers:

Course assistant:

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 essay is replaced by a seminar report, so no separate essay is required.

The essay required for (A) and (B) should be four pages in length. It should be returned to the course assistant by the end of October, as well as the solutions to the exercise problems (B). See next section for more insturctions.

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. In addition to the seminar presentation, the participants will have to return a written report before 21th November.

Passing the course

For all formats:

Fill in the course feedback form (select the correct course from the list).

For format A (2cu):

  1. Choose a topic (related to the course) for the essay, and email a short description of the topic to the course assistant by 20th October. A typical essay covers some part of the lectures by summarizing and extending (based on a book or articles on the topic) the material presented in the lectures.
  2. Write a four page essay on the chosen topic, and return it to the course assistant by 31st October. Preferred way of submission is Postscript or PDF file as an email attachment, but also paper versions are accepted (Room B211 or Arto Klami, PO Box 68, 00014 University of Helsinki).

For format B (3cu):

In addition to the above, solve the exercises and return the solutions to the assistant by 31st October.

For format C (5cu):

Solve the exercises as in format B , but instead of an essay there is the project work and its report. The details are given on a separate page.

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 (comp.sci) or mab@rni.helsinki.fi (math/stat).

Lectures

Schedule

Time Room Monday
9-11 B120
  • Administrative issues
  • Introduction to genome biology
12-14 B120
  • Introduction to microarray techniques
Tuesday
9-11 CK107
  • Preprocessing: image analysis and normalization
12-14 CK107
  • Experimental design
  • Finding expressed genes
Wednesday
9-11 C323
  • Classification
12-14 C323
  • Clustering and information visualization
Thursday
9-11 C323
  • Basics on regulation
12-14 C323
  • Inference of regulatory networks
Friday
9-11 C323
  • New high-throughput measurements
12-14 C323
  • Data fusion
  • Summary and questions

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.

Schedule

Time Presenter Topic (and link to the project report)
9-10 Juan Carlos Borras Automatic data extraction from raw microarray images
10-11 Heidi Hemmoranta Gene expression profiling of 'HSC-enriched' cell population
12-13 Paula Silvonen Comparison of two clustering methods on yeast data
13-14 Aurelijus Narbutas Global gene expression analysis in yeast hsp104 knockout system

Course material

These should be read before the course (note: easy and quick reading!):

Additional reading (useful and interesting):

Further reading will be referenced during the lectures.

Page authoring information

Author Arto Klami
Last modified: Tue Mar 28 09:50:18 EEST 2006


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