1 / 12

A Short Overview of Microarrays

Tex Thompson Spring 2005. A Short Overview of Microarrays. Raw Data. Microarray data at its most raw consists of a spotted image, and information on what each spot represents (spot intensities and metadata). Genes may be spotted in replicate

moe
Download Presentation

A Short Overview of Microarrays

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Tex Thompson Spring 2005 A Short Overview of Microarrays

  2. Raw Data • Microarray data at its most raw consists of a spotted image, and information on what each spot represents (spot intensities and metadata). • Genes may be spotted in replicate • Affymetrix chips use a match/mismatch technology to guard against non-specific hybridization.

  3. Normalizing Data • Normalization of microarray data is the process of removing array-specific bias in order to make results between arrays comparable. • Intensity data relevant to a single gene needs to be combined and normalized in order to define “expression levels” for each gene. • The basic idea is that the expression level is proportional to the number of mRNA transcripts of that gene within the tissue of interest.

  4. RMA Normalization • Each array is assumed to have a common amount of “background noise.” • Normalization is performed by quantile normalization, such that the intensities across each chip are adjusted to produce identical distributions. • A statistician (or Google) could tell you much more about this.

  5. Diagram of Microarray Analysis mRNA ?????? Normalized Data Raw Data

  6. What Sorts of Questions Can We Ask? • What are the most highly/lowly expressed genes in a sample of interest? • What are the differentially expressed genes across two (or more) samples of interest? • What sets of genes are always upregulated or downregulated as a set? • What do you think?

  7. Clustering • Clustering is the process of assembling N objects into K “clusters” based on a set of measured characteristics. • For example, a common clustering application is clustering individual samples into clusters based on their gene expression. • Alternatively, clustering can be used to group together individual genes who similar expression patterns.

  8. Prediction • Prediction is the process of creating an algorithm for taking an unknown sample and putting it in a known classification scheme. • For example, a predictor might measure the gene expression levels of an unknown tissue sample and match it to the most probable classification. • This protocol is very common in studies of different types of cancer.

  9. Algorithms Of Interest • Principal Component Analysis (PCA) • Self-Organizing Maps (SOM) • Support Vector Machines (SVM) • Linear Discriminant Analysis (LDA) • K-Means Clustering • KNN Classifiers • Differential Expression Statistics • Assumptions of RMA Normalization

  10. Looking At The Data • Each array falls into one of four types: • Young • Middle-aged • Old, Mild Presbycusis • Old, Severe Presbycusis

  11. Looking At The Data X13_Frisina_S2_M430A.CEL X1_b_Frisina_S2_M430A.CEL 1415670_at 10.0073897626035 10.4616952671666 1415671_at 12.1960225217605 13.1951229785856 1415672_at 13.9737085433580 13.7746451795089 1415673_at 9.62027371983307 10.9092694066664

  12. Go To Work! I'll be available for questions via until 9:30am and via e-mail (tex@bioinformatics.rit.edu). These slides will be made available on the course website.

More Related