DNA Microarray Data Analysis

Preface
List of Contributors
Contents
Introduction
	Introduction
		Why perform microarray experiments?
		What is a microarray?
		Microarray production
		Where can I obtain microarrays?
		Extracting and labeling the RNA sample
		RNA extraction from scarse tissue samples
		Hybridization
		Scanning
		Typical research applications of microarrays
		Experimental design and controls
		Suggested reading
	Affymetrix Genechip system
		Affymetrix technology
		Single Array analysis
		Detection p-value
		Detection call
		Signal algorithm
		Analysis tips
		Comparison analysis
		Normalization
		Change p-value
		Change call
		Signal Log Ratio Algorithm
	Genotyping systems
		Introduction
		Methodologies
		Genotype calls
		Suggested reading
	Overview of data analysis
		cDNA microarray data analysis
		Affymetrix data analysis
		Data analysis pipeline
	Experimental design
		Why do we need to consider experimental design?
		Choosing and using controls
		Choosing and using replicates
		Choosing a technology platform
		Gene clustering v. gene classification
		Conclusions
		Suggested reading
	Basic statistics
		Why statistics are needed
		Basic concepts
			Variables
			Constants
			Distribution
			Errors
		Simple statistics
			Number of subjects
			Mean (m)
			Trimmed mean
			Median
			Percentile
			Range
			Variance and the standard deviation
			Coefficient of variation
		Effect statistics
			Scatter plot
			Correlation (r)
			Linear regression
		Frequency distributions
			Normal distribution
			t-distribution
			Skewed distribution
			Checking the distribution of the data
		Transformation
			Log2-transformation
		Outliers
		Missing values and imputation
		Statistical testing
			Basics of statistical testing
			Choosing a test
			Threshold for p-value
			Hypothesis pair
			Calculation of test statistic and degrees of freedom
			Critical values table
			Drawing conclusions
			Multiple testing
		Analysis of variance
			Basics of ANOVA
			Completely randomized experiment
		Statistics using GeneSpring
			Simple statistics
			Tranformations
			Scatter plot and histogram
			Correlation
			Linear regression
			One-sample t-test
			Independent samples t-test and ANOVA
		Suggested reading
Analysis
	Preprocessing of data
		Rationale for preprocessing
		Missing values
		Checking the background reading
		Calculation of expression change
			Intensity ratio
			Log ratio
			Fold change
		Handling of replicates
			Types of replicates
			Time series
			Case-control studies
			Power analysis
			Averaging replicates
		Checking the quality of replicates
			Quality check of replicate chips
			Quality check of replicate spots
			Excluding bad replicates
		Outliers
		Filtering bad data
		Filtering uninteresting data
		Simple statistics
			Mean and median
			Standard deviation
			Variance
		Skewness and normality
			Linearity
		Spatial effects
		Normalization
		Similarity of dynamic range, mean and variance
		Examples using GeneSpring
			Importing data
			Background subtraction
			Calculation of expression change
			Replicates
			Checking linearity
			Normality
			Filtering
		Suggested reading
	Normalization
		What is normalization?
		Sources of systematic bias
			Dye effect
			Scanner malfunction
			Uneven hybridization
			Printing tip
			Plate and reporter effects
			Batch effect and array design
			Experimenter issues
			What might help to track the sources of bias?
		Normalization terminology
			Normalization, standardization and centralization
			Per-chip and per-gene normalization
			Global and local normalization
		Performing normalization
			Choice of the method
			Basic idea
			Control genes
			Linearity of data matters
			Basic normalization schemes for linear data
			Special situations
		Mathematical calculations
			Mean centering
			Median centering
			Trimmed mean centering
			Standardization
			Lowess smoothing
			Ratio statistics
			Analysis of variance
			Spiked controls
			Dye-swap experiments
		Some caution is needed
		Graphical example
		Example of calculations
		Using GeneSpring for normalization
		Suggested reading
	Finding differentially expressed genes
		Identifying over- and underexpressed genes
			Filtering by absolute expression change
			Statistical single chip methods
			Noise envelope
			Sapir and Churchill's single slide method
			Chen's single slide method
			Newton's single slide method
		What about the confidence?
			Only some treatments have replicates
			All the treatments have replicates: two-sample t-test
			All the treatments have replicates: one-sample t-test
		GeneSpring examples
		Suggested reading
	Cluster analysis of microarray information
		Basic concept of clustering
		Principles of clustering
		Hierarchical clustering
		Self-organizing map
		K-means clustering
		Principal component analysis
		Pros and cons of clustering
		Visualization
		Programs for clustering and visualization
		Function prediction
		GeneSpring and clustering
			Clustering tool
			Principal components analysis tool
			Predict parameter value tool
		Suggested reading
Data mining
	Gene regulatory networks
		What are gene regulatory networks?
		Fundamentals
		Bayesian network
		Calculating Bayesian network parameters
		Searching Bayesian network structure
		Conclusion
		Suggested reading
	Data mining for promoter sequences
		Introduction
		Introduction
		Finding promoter region sequences
		Using EnsMart to retrieve promoter regions
		Comparison of EnsMart and UCSC searches
		Pattern search without prior knowledge
		Summary
		GeneSpring and promoter analysis
		Suggested reading
	Annotations and article mining
		Retrieving annotations from public databases
		Retrieving annotations using BLAST
		Article mining
		Annotation and gene ontologies using GeneSpring
			Annotations
			Ontologies
Tools and data management
	Reporting results
		Why the results should be reported
		What details should be reported: the MIAME standard
		How the data should be presented: the MAGE standard
			MAGE-OM
			MAGE-ML; an XML-translation of MAGE-OM
			MAGE-STK
		Where and how to submit your data
			ArrayExpress and GEO
			MIAMExpress
			GEO
			Other options and aspects
		MIAME-compliant sample attributes in GeneSpring
		Suggested reading
	Software issues
		Data format conversions problems
		A standard file format
		Programming
			Perl
			Awk
			R
		Freeware software packages
			Cluster and treeview
			Expression profiler
			ArrayViewer
			MAExplorer
			Bioconductor
		Commercial software packages
			VisualGene
			GeneSpring
			Kensington
			J-Express
			Expression Nti
			Rosetta Resolver
			Spotfire
Index