DNA Microarray Data Analysis

✍ Scribed by Pasanen T., Tuimala J., Minna Laine M., et al.

• Publisher: CSC, the Finnish IT center for Science
• Year: 2003
• Tongue: English
• Leaves: 162
• Category: Library

✦ Table of Contents

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