CodeLink compatible

CodeLink compatible

Microarray Analysis of Gene Expression in Huntington's Disease Peripheral Blood - a Platform Comparison

http://www.gehealthcare.com/index.html

General microarry data analysis workflow

From raw data to biological significanceComparison statistics and correction for multiple testingGeneSifter Overview

Gene Expression in Huntington's Disease Peripheral Blood

Identification of biological themesPlatform comparison


Analysis Workflow

Normalized, scaled data

Differentially expressed genes

Identify and partition expression patterns

Gene Summaries

Biological themes (Pathways, molecular function, etc.)

Raw data

Analysis Workflow

Normalized, scaled data

Differentially expressed genes

Identify and partition expression patterns

Gene Summaries

Biological themes (Pathways, molecular function, etc.)

Raw data

Comparison statistics, correction for multiple testing

Up and down regulated, magnitude, clustering

Annotation (UniGene, Entrez Gene, Gene Ontologies, etc.)

Ontology report, pathway report, z-score

Data upload

Experiment DesignExperimental design determines what can be inferred from the data as well as determining the confidence that can be assigned to those inferences. Careful experimental design and the presence of biological replicates are essential to the successful use of microarrays.

•Type of experiment– Two groups– Three or more groups

• Time series• Dose response• Multiple treatment

The type of experiment and number of groups will affect the statistical methods used to detect differential expression

•Replicates– The more the better, but at least 3– Biological better than technical

Rigorous statistical inferences cannot be made with a sample size of one. The more replicates, the stronger the inference.

Supporting material - Experimental Design and Other Issues in Microarray Studies - Kathleen Kerr -http://ra.microslu.washington.edu/presentation/documents/KerrNAS.pdf

microarraysuccess.com

http://ra.microslu.washington.edu/presentation/documents/KerrNAS.pdf












Differential Expression

The fundamental goal of microarray experiments is to identify genes that are differentially expressed in the conditions being studied. Comparison statistics can be used to help identify differentially expressed genes and cluster analysis can be used to identify patterns of gene expression and to segregate a subset of genes based on these patterns.

•Statistical Significance– Fold change

Fold change does not address the reproducibility of the observed difference and cannot be used to determine the statistical significance.

– Comparison statistics• 2 group

– t-test, Welch’s t-test, Wilcoxon Rank Sum, • 3 or more groups

– ANOVA, Kruskal-Wallis

Comparison tests require replicates and use the variability within the replicates to assign a confidence level as to whether the gene is differentially expressed.

Supporting material - Draghici S. (2002) Statistical intelligence: effective analysis of high-density microarray data. Drug Discov Today, 7(11 Suppl).: S55-63.


• Correction for multiple testing- Methods for adjusting the p-value from a comparison test based on the number of tests performed. These adjustments help to reduce the number of false positives in an experiment.

– FWER : Family Wise Error Rate (FWER) corrections adjust the p-value so that it reflects the chance of at least 1 false positive being found in the list.

• Bonferonni, Holm, W & Y MaxT– FDR : False Discovery Rate corrections (FDR) adjust the p-value so that it

reflects the frequency of false positives in the list.• Benjamini and Hochberg, SAM

The FWER is more conservative, but the FDR is usually acceptable for “discovery” experiments, i.e. where a small number of false positives is acceptable

Dudoit, S., et al. (2003) Multiple hypothesis testing in microarray experiments. Statistical Science 18(1): 71-103.Reiner, A., et al. (2003) Identifying differentially expressed genes using false discovery rate controlling procedures. Bioinformatics 19(3):368-375.

Differential Expression


AccessibilityWeb-basedSecureData management

DataAnnotation (MIAME)

Multiple upload toolsCodeLinkAffymetrixIlluminaAgilent Custom

Differential Expression - Powerful, accessible tools for determining Statistical Significance

R based statisticsBioconductorComparison Tests

t-test, Welch’s t-test, Wilcoxon Rank sum test, ANOVA,

Correction for Multiple TestingBonferroni, Holm, Westfall and Young maxT, Benjamini and Hochberg

Unsupervised ClusteringPAM, CLARA, Hierarchical clusteringSilhouettes

GeneSifter – Microarray Data Analysis

CodeLink compatible


GeneSifter – Microarray Data Analysis

Integrated tools for determining Biological Significance

One Click Gene Summary™Ontology ReportPathway ReportSearch by ontology termsSearch by KEGG terms or Chromosome

The GeneSifter Data Center

• Free resourceTrainingResearchPublishing

• 5 areasCardiovascularCancerNeuroscienceImmunologyOral Biology

• Access to :DataAnalysis summaryTutorialsWebEx


www.genesifter.net/dc

GeneSifter - Analysis Examples

Differential expressionFold changeQualityt-test False discovery rate

Differential expressionFold changeQualityANOVA False discovery rate

VisualizationHierarchical clusteringPCA

PartitioningPAMSilhouettes

Data UploadCodeLink

Biological significanceGene AnnotationOntology reportPathway report

2 groups(Huntingtons Blood vs Healthy Blood)

3 + groups(Time series, dose response, etc.)






Background - Huntington’s Disease

Huntington’s Disease (HD)

•Autosomal dominant neurodegenerative disease

•Motor impairment

•Cognitive decline

•Various psychiatric symptoms

•Onset 30-50 years

•Mutant Huntingtin protein (polyglutamine)

•Effects transcriptional regulation

•Transcription effects may occur outside of CNS

Pairwise Analysis

CodeLink Human 20K Bioarray

Human blood expression for Huntington’s disease versus control, CodeLink

Borovecki F, Lovrecic L, Zhou J, Jeong H, Then F, Rosas HD, Hersch SM, Hogarth P, Bouzou B, Jensen RV, Krainc D. Genome-wide expression profiling of human blood reveals biomarkers for Huntington's disease. Proc Natl Acad Sci U S A. 2005 Aug 2;102(31):11023-8.

Genome-wide expression profiling of human blood reveals biomarkers for Huntington's disease

Borovecki F, Lovrecic L, Zhou J, Jeong H, Then F, Rosas HD, Hersch SM, Hogarth P, Bouzou B, Jensen RV, Krainc D. Proc Natl Acad Sci U S A. 2005 Aug 2;102(31):11023-8.

Collected peripheral blood samples -

•14 Controls•12 Symptomatic HD patients•5 Presymptomatic HD patients

Identified 322 most differentially expressed genes (Con. Vs Symptomatic HD) using U133A array.

Used CodeLink 20K to confirm genes identifed using Affymetrix platform

Focused on 12 genes that showed most significant difference between Control and HD

Data available from GEO

Background - Data

Pairwise Analysis

CodeLink Human 20K Bioarray



Pairwise Analysis

Select group 114 normal

Select group 212 Huntingtons

Already normalized (median)

t-test

Quality filter – 0.75(filters out genes with signal less than 0.75)

Benjamini and Hochberg (FDR)

Log transform data

Pairwise Analysis

Pairwise Analysis – Gene List

Biological Significance

Gene Annotation Sources

• UniGene - organizes GenBank sequences into a non-redundant set of gene-oriented clusters. Gene titles are assigned to the clusters and these titles are commonly used by researchers to refer to that particular gene.

• LocusLink (Entrez Gene) - provides a single query interface to curated sequence and descriptive information, including function, about genes.

• Gene Ontologies – The Gene Ontology™ Consortium provides controlled vocabularies for the description of the molecular function, biological process and cellular component of gene products.

• KEGG - Kyoto Encyclopedia of Genes and Genomes provides information about both regulatory and metabolic pathways for genes.

• Reference Sequences- The NCBI Reference Sequence project (RefSeq) provides reference sequences for both the mRNA and protein products of included genes.

GeneSifter maintains its own copies of these databases and updates them automatically.

One-Click Gene Summary


Ontology Report

Ontology Report : z-score

R = total number of genes meeting selection criteria

N = total number of genes measured

r = number of genes meeting selection criteria with the specified GO term

n = total number of genes measured with the specific GO term

Reference:Scott W Doniger, Nathan Salomonis, Kam D Dahlquist, Karen Vranizan, Steven C Lawlor and Bruce R Conklin; MAPPFinder: usig Gene Ontology and GenMAPP to create a global gene-expression profile from microarray data, Genome Biology 2003, 4:R7

Z-score Report

Z-score Report

KEGG Report

Pairwise Analysis - Summary

~20,000 genes 5684 genes

2606 increasedIn HD

Biological processesProtein biosynthesis (104)Ubiquitin cycle (123)RNA splicing (53)

KEGGOxidataive phosphorylation (35)Apoptosis (22)

Biological processesNeurogenesis (90)Cell adhesion (120)Sodium ion transport (29)G-protein coupled receptor signaling (114)

KEGGNeuroactive ligand-receptor interaction (56)

3078 decreasedIn HD


12 HD14 Control

Z-scores Pattern selectiont-test, Benjamini and Hochberg (FDR)






Pairwise Analysis

U133A Human Genome ArrayMAS 5 signal

Human blood expression for Huntington’s disease versus control, Affymetrix


Already normalized (median)

t-test

Quality filter – 50(filters out genes with signal less than 50)

Benjamini and Hochberg (FDR)

Log transform data

Pairwise Analysis - Affymetrix


Human blood expression for Huntington’s disease versus control, Affymetrix

Gene Lists – Common and Unique Genes

Platform comparison – Biological themesAffymetrix

Platform comparison – Biological themesCodeLink

GeneSifter - Analysis Examples

Differential expressionFold changeQualityt-test False discovery rate

Differential expressionFold changeQualityANOVA False discovery rate

VisualizationHierarchical clusteringPCA

PartitioningPAMSilhouettes

Data UploadCodeLink

Biological significanceGene AnnotationOntology reportPathway report

2 groups(Huntingtons Blood vs Healthy Blood)

3 + groups(Time series, dose response, etc.)

Project Analysis - Clustering

Cluster by Samples – All Genes

CodeLink Affymetrix

Cluster by Samples – ?

CodeLink Affymetrix

Cluster by Samples – Y Chrom. Genes

CodeLink Affymetrix

Platform Comparison - Summary

CodeLink AffymetrixTranscripts Total 19729 22283Increased in HD 2606 1976Overlap (LL genes) 41% 65%

Top BP OntologiesUbiquitin cycleRNA splicingRegulation of translationApoptosis

Clustering of samples

Platform Comparison - Summary

CodeLink AffymetrixIncreased in HD 2606 1976Decreased in HD 3708 986Unique ontology Oxidative Phos. IL-6 Biosynthesis

DataDataPublicationPublication

BiologicalBiologicalSignificanceSignificance

Differential Differential ExpressionExpression

System System AccessAccess

DataDataManagementManagement

PlatformPlatformSelectionSelection

Experiment Experiment DesignDesign

Type of experimentTwo groupsTime seriesDose ResponseMultiple treatments

ReplicatesThe more the betterTechnical vs. biological

PlatformscDNAOligoOne colorTwo color

Feature ExtractionSoftwareFile formats

Databases

Raw DataStoringRetrieving

Experiment AnnotationSamplesProtocols

UsabilityIntuitiveSpecial training

System AccessSingle user desktopSingle user serverWeb-based

Sharing dataIn the labCollaboration

Normalization

Differential ExpressionFold changeComparison statisticsFWER/FDR

Pattern IdentificationClusteringVisualizationPartitioning

Gene AnnotationUniGeneLocusLinkGene OntologyKEGGOMIM

Single GenesGene Summaries

Gene ListsOntology ReportPathway Report

MIAMEWhat is it?Publication

Public databasesGEOArrayExpressSMD

Using public dataMeta analysis

Seven Keys to Successful Microarray Data Analysis

MicroarraySuccess.com

Academic partner – University of Washington


www.genesifter.net/dc

Eric Olson

[email protected]

Thank You

www.genesifter.netTrial account, tutorials, sample data and Data Center

CodeLink compatible

http://www.genesifter.net/




CodeLink compatible

Documents

patterns of gene expression

cluster analysis

entrez gene

gene ontologies

statistical methods

statistical intelligence

number of tests

raw datacomparison statistics