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The Bioinformatics Core Facility of the Swiss Institute of Bioinformatics is organizing a five-day course on advanced methods for (gene expression) microarray data on 2-6 November 2009, at the CIG in Lausanne. The course is open to anyone working in the field of life sciences in Switzerland, provided that they already have experience at least equivalent to the content of our Introduction to microarray analysis course (in particular: normalization and how to find differentially expressed genes between different conditions in a single dataset).
This course will pick up from where the introductory course left, and will explore how gene expression data can be combined with other types of information, and how researchers can analyze multiple, heterogenous datasets together, allowing them to tap into the wealth of publicly available data.
The course will consist of theory each morning followed by exercises (practical analysis of microarray) in the afternoon. Computers will be available, but participants are encouraged to bring their own laptop. Practical exercises will use the R statistical software. In line with the prerequirements indicated above, participants should ideally already know how to conduct a simple microarray analysis with R and Bioconductor; detailed tutorials are available on our course wiki page. Participants are also welcome to bring their own datasets for discussion (if time permits).
We plan to cover the following topics:
- How to find data, and how to compare data coming from different platforms: use of databases such as GEO and ArrayExpress; gene mapping and cross-platform/cross species matching, gene identifiers, etc
- Downloading, sorting and formatting the data: processing pipeline, (re)normalization, quality control, etc.
- Gene-by-gene differential expression: correlation of gene data with clinical variables, survival data, etc (normal, Cox and logit models).
- Comparing and combining results: different levels for combining data; case studies.
- Prediction, classification and independent validation
- Geneset analysis: combining information from multiple experiments using list of genes and methods such as GSEA
- (if time permits) Combining other technologies: we will briefly discuss how the techniques described during the course can allow us to combine microarray data with other genomic data, such as SNPs, arrayCGH or ChipSEQ data.
The course fee for academic participants is 100 CHF (please contact us if you are an industry participant); this amount includes copies of the slides, tea break in the morning and a social dinner on the Tuesday evening.