DNA Microarray Image Analysis - University of Illinois at Urbana ...
DNA Microarray Image Analysis - University of Illinois at Urbana ... DNA Microarray Image Analysis - University of Illinois at Urbana ...
Overview of Microarray Problem Biology Application Domain Validation Experiment Design and Hypothesis Microarray Experiment Data Analysis Image Analysis Data Warehouse Knowledge discovery in databases (KDD) Statistics Data Mining Artificial Intelligence (AI) 8
Types of Expected Microarray Data Mining and Analysis Results Hypothetical Examples: • Binary answers using tests of hypotheses — Drug treatment is successful with a confidence level x. • Statistical behavior (probability distribution functions) — A class of genes with functionality X follows Poisson distribution. • Expected events — As the amount of treatment will increase the gene expression level will decrease. • Relationships — Expression level of gene A is correlated with expression level of gene B under varying treatment conditions (gene A and B are part of the same pathway). • Decision trees — Classification of a new gene sequence by a “domain expert”. 9
- Page 1 and 2: February 4, 2005 DNA Microarray Ima
- Page 3 and 4: Publications • Journals: — Bajc
- Page 5 and 6: Microarray Problem: Major Objective
- Page 7: Input and Output of Microarray Data
- Page 11 and 12: 11 Microarray Data Processing Workf
- Page 13 and 14: DNA Microarray Image Analysis • T
- Page 15 and 16: Ideal Microarray Image? 1. Ideal cD
- Page 17 and 18: Microarray Image Technologies • A
- Page 19 and 20: Variations of Grid Geometry • Rot
- Page 21 and 22: Variation of Spot Morphology • Sp
- Page 23 and 24: Examples: Spatially Varying Backgro
- Page 25 and 26: 25 Examples: Spatial Resolution, Li
- Page 27 and 28: IMAGE ANALYSIS: MICROARRAY GRID ALI
- Page 29 and 30: Grid Alignment: Application Domains
- Page 31 and 32: 31 Microarray Grid Alignment: Previ
- Page 33 and 34: Microarray Grid Alignment: Previous
- Page 35 and 36: 35 Grid Alignment Algorithm Overvie
- Page 37 and 38: Image Down-Sampling • Design of r
- Page 39 and 40: Vertical and Horizontal Line Score
- Page 41 and 42: Optional Regularity Enforcement •
- Page 43 and 44: Processing Multiple Grids Line Disc
- Page 45 and 46: Spot Size & Spot Density 45 •Radi
- Page 47 and 48: Missing Spots The fewer the spots i
- Page 49 and 50: Down-sampling •Experimental resul
- Page 51 and 52: Grid Alignment Properties Color Inv
- Page 53 and 54: Multiple Grids: Semi-Automated vs.
- Page 55 and 56: 55 MICROARRAY FOREGROUND SEPARATION
- Page 57 and 58: Foreground Separation Using Spatial
Types <strong>of</strong> Expected <strong>Microarray</strong> D<strong>at</strong>a Mining and <strong>Analysis</strong><br />
Results<br />
Hypothetical Examples:<br />
• Binary answers using tests <strong>of</strong> hypotheses<br />
— Drug tre<strong>at</strong>ment is successful with a confidence level x.<br />
• St<strong>at</strong>istical behavior (probability distribution functions)<br />
— A class <strong>of</strong> genes with functionality X follows Poisson distribution.<br />
• Expected events<br />
— As the amount <strong>of</strong> tre<strong>at</strong>ment will increase the gene expression<br />
level will decrease.<br />
• Rel<strong>at</strong>ionships<br />
— Expression level <strong>of</strong> gene A is correl<strong>at</strong>ed with expression level <strong>of</strong><br />
gene B under varying tre<strong>at</strong>ment conditions (gene A and B are part<br />
<strong>of</strong> the same p<strong>at</strong>hway).<br />
• Decision trees<br />
— Classific<strong>at</strong>ion <strong>of</strong> a new gene sequence by a “domain expert”.<br />
9
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