Affymetrix

##Introduction and Instructions

Welcome to the Affymetrix Tutorial. This tutorial will guide you through a basic Affymetrix experiment using freely available Affymetrix microarray data from Gene Expression Omnibus (GEO). Below is the recommended progression for advancing through this tutorial:

(1) Go through the Shared Features page to familiarize yourself with the methods shared across all microarray experiments
(2) Return to this page to go through the annotated code along with accompanying tutorial slides to become acquainted with a typical Affymetrix microarray experiment
(3) Download Affymetrix.R to work through the tutorial directly on your own computer/server
(4) Use Affymetrix.R as a template for your own Affymetrix microarray experiment, and refer to the annotated code and tutorial slides as needed

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Tutorial Slides

In the future, this page will hold links to the more useful slides that accompany this presentation

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Annotated Code

####Experiment Progression

Here are the steps for a basic Affymetrix microarray experiment:

1. Get Data
2. QC on Non-Normalized Data
3. Normalization
4. Batch Correction
5. Outlier Removal
6. QC on Normalized Data
7. Covariate Analysis
8. Annotating Probes
9. Collapse Rows
10. Differential Expression Analysis

1. Get Data

For this tutorial we will be using data from GEO, experiment GSE20295. This is a Parkinson's disease multi-region brain study. In the tutorial, we will only be looking at the prefrontal cortex.

It is key to download the affy R library for your Affymetrix experiment, since it contains functions for reading the raw data into your R session (ReadAffy) and normalization of Affymetrix arrays (rma).

For your Affymetrix experiment, you have two choices for getting your data into your R session:
**(a)**You load your data directly from a source (such as GEO or ArrayExpress) into your R session
**(b)**You download your data to your local computer or server before loading it into your R session

Option (a) can fully utilize tools such as the GEOquery R library. Specific sources often have R libraries for downloading microarray data directly into your R session. In the code tutorial, we use GEOquery for option (a).

Option (b) is more suitable if you are using data from your own experiment, OR you need to select specific parts of the entire dataset to analyze. Sometimes with large datasets (> 500 Mb) it is better to 'pre-filter', or only extract/download data that you need - in this experiment, we only want prefrontal cortex.

######(a) Load your data directly from a source into your R session (using GEOquery R library)

######(b) Download your data to your local computer or server before loading it into your R session

2. QC on Non-Normalized Data

3. Normalization

4. Batch Correction

5. Outlier Removal

6. QC on Normalized Data

7. Covariate Analysis

8. Annotating Probes

9. Collapse Rows

10. Differential Expression Analysis