Transcript lecture
Microarray Technology Types Normalization Microarray Technology • Microarray: – New Technology (first paper: 1995) • Allows study of thousands of genes at same time – Glass slide of DNA molecules • Molecule: string of bases (25 bp – 500 bp) • uniquely identifies gene or unit to be studied http://kbrin.a-bldg.louisville.edu/CECS694/ Fabrications of Microarrays • Size of a microscope slide Images: http://www.affymetrix.com/ Differing Conditions • Ultimate Goal: – Understand expression level of genes under different conditions • Helps to: – Determine genes involved in a disease – Pathways to a disease – Used as a screening tool Gene Conditions • • • • • Cell types (brain vs. liver) Developmental (fetal vs. adult) Response to stimulus Gene activity (wild vs. mutant) Disease states (healthy vs. diseased) Expressed Genes • Genes under a given condition – mRNA extracted from cells – mRNA labeled – Labeled mRNA is mRNA present in a given condition – Labeled mRNA will hybridize (base pair) with corresponding sequence on slide Two Different Types of Microarrays • Custom spotted arrays (up to 20,000 sequences) – cDNA – Oligonucleotide • High-density (up to 100,000 sequences) synthetic oligonucleotide arrays – Affymetrix (25 bases) – SHOW AFFYMETRIX LAYOUT Custom Arrays • Mostly cDNA arrays • 2-dye (2-channel) – RNA from two sources (cDNA created) • Source 1: labeled with red dye • Source 2: labeled with green dye Two Channel Microarrays • Microarrays measure gene expression • Two different samples: – Control (green label) – Sample (red label) • Both are washed over the microarray – Hybridization occurs – Each spot is one of 4 colors (Slide source: http://www.bsi.vt.edu/) Microarray Image Analysis • Microarrays detect gene interactions: 4 colors: – – – – Green: high control Red: High sample Yellow: Equal Black: None • Problem is to quantify image signals Information Extraction — Spot Intensities —mean (pixel intensities). —median (pixel intensities). — Background values —Local Take the average —Morphological opening —Constant (global) Background —None Signal — Quality Information Speed Group Microarray Page http://stat-www.berkeley.edu/users/terry/zarray/Html/image.html Single Color Microarrays • Prefabricated – Affymetrix (25mers) • Custom – cDNA (500 bases or so) – Spotted oligos (70-80 bases) Single Color Microarrays • Expressed sequences washed over chips • Expressed genes hybridize • Light passed under to see intensity (or hybridized oligos show dark color) Single Color Microarrays Image: http://www4.nationalacademies.org/ Affymetrix Technology DESOKY, 2003 Affymetrix Technology DESOKY, 2003 Lithography • It is a printing technology. • Lithography was invented by Alois Senefelder in Germany in 1798. • The printing and non-printing areas of the plate are all at the same level, as opposed to intaglio and relief processes in which the design is cut into the printing block. • Lithography is based on the chemical repellence of oil and water. Lithography Designs are drawn or painted with greasy ink or crayons on specially prepared limestone. The stone is moistened with water, which the stone accepts in areas not covered by the crayon. An oily ink, applied with a roller, adheres only to the drawing and is repelled by the wet parts of the stone. The print is then made by pressing paper against the inked drawing. Affymetrix Array Construction STROMBERG, 2003 Affymetrix Technology Biotin (one dye) instead of 2 colors One treatment per chip 11, 16, or 20 gene markers pairs per gene DESOKY, 2003 Affymetrix Data • Each gene labeled as “present”, “marginal”, or “absent.” – Present: gene expressed and reliably detected in the RNA sample • Label chosen based on a p-value Affymetrix Design of probes PM to maximize hybridization MM to ascertain the degree of cross-hybridization PM MM Probe pair Probe set STROMBERG, 2003 Inferential statistics Paradigm Parametric test Nonparametric Compare two unpaired groups Unpaired t-test Mann-Whitney test Compare two paired groups Paired t-test Wilcoxon test Compare 3 or more groups ANOVA Inferential statistics Is it appropriate to set the significance level to p < 0.05? If you hypothesize that a specific gene is up-regulated, you can set the probability value to 0.05. You might measure the expression of 10,000 genes and hope that any of them are up- or down-regulated. But you can expect to see 5% (500 genes) regulated at the p < 0.05 level by chance alone. To account for the thousands of repeated measurements you are making, some researchers apply a Bonferroni correction. The level for statistical significance is divided by the number of measurements, e.g. the criterion becomes: p < (0.05)/10,000 or p < 5 x 10-6 Data matrix (20 genes and 3 time points from Chu et al.)