Time |
Topic |
Learning Objectives |
Before start |
Setup |
|
14:00 |
Working with Bioconductor
|
- Become familiar with the basic Bioconductor setup.
- Be able to install the appropriate Bioconductor packages for microarray analysis
- Be able to use the help system and vignettes for Bioconductor packages
|
14:20 |
Importing processed microarray data into R from GEO
|
- Be able to obtain data from GEO, including processed and raw data.
- Be able to explain and use the differences between GEO data types.
- Understand the concept of the ExpressionSet class of objects.
|
14:40 |
Importing raw (unprocessed) Affymetrix microarray data
|
- Be able to obtain supplemental data
- Be able to explain and use the differences between GEO data types.
- Understand the concept of the ExpressionSet class of objects.
|
14:55 |
Working with experimental metadata
|
- Be able to use metadata from GEO objects to construct useful R data objects
- Be able to use read.celfiles in combination with your own pData object to ensure data integrity
|
15:10 |
Microarray Data processing with RMA
|
- Understand and explain Background correct, normalise and summarisation steps for microarray data
|
15:50 |
coffee break
|
Break
|
16:00 |
Identifying differentially expressed genes using linear models (part 1)
|
- Be able to use limma to identify differentially expressed genes.
- Understand the formula class of objects in R, and use it to specify the appropriate model for linear modeling.
|
16:40 |
Identifying differentially expressed genes using linear models (part 2, factorial designs)
|
- Be able to use limma to identify differentially expressed genes.
- Understand the formula class of objects in R, and use it to specify the appropriate model for linear modeling.
|
17:20 |
From features to annotated gene lists
|
- Be able to use AnnotationDb methods to association annotations with platform data.
|
17:50 |
Basic downstream analysis of microarray data
|
- Be able to plot volcano plots and heatmaps in R.
- Be able to interpret the above plots generated.
- Be aware of some downstream analysis that are commonly done to interpret the results of differential expression analysis.
|
18:10 |
Finish |
|