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Microarray Analysis of Gene Expression in Huntington\'s Disease Peripheral Blood - a Platform Comparison. CodeLink compatible. Microarray Analysis of Gene Expression in Huntington\'s Disease Peripheral Blood - a Platform Comparison. General microarry data analysis workflow

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slide1

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

CodeLink compatible

slide2

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

General microarry data analysis workflow

From raw data to biological significance

Comparison statistics and correction for multiple testing

GeneSifter Overview

Gene Expression in Huntington\'s Disease Peripheral Blood

Identification of biological themes

Platform comparison

slide3

Analysis Workflow

Raw data

Normalized, scaled data

Differentially expressed genes

Identify and partition expression patterns

Gene Summaries

Biological themes

(Pathways, molecular function, etc.)

slide4

Analysis Workflow

Raw data

Data upload

Normalized, scaled data

Comparison statistics,

correction for multiple testing

Differentially expressed genes

Up and down regulated, magnitude,

clustering

Identify and partition expression patterns

Annotation (UniGene, Entrez Gene,

Gene Ontologies, etc.)

Gene Summaries

Ontology report, pathway report, z-score

Biological themes

(Pathways, molecular function, etc.)

slide5

microarraysuccess.com

Experiment Design

Experimental 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

slide6

microarraysuccess.com

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.

slide7

microarraysuccess.com

Differential Expression

  • 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.

slide8

GeneSifter – Microarray Data Analysis

Accessibility

Web-based

Secure

Data management

Data

Annotation (MIAME)

Multiple upload tools

CodeLink

Affymetrix

Illumina

Agilent

Custom

Differential Expression - Powerful, accessible tools for

determining Statistical Significance

R based statistics

Bioconductor

Comparison Tests

t-test, Welch’s t-test,

Wilcoxon Rank sum test, ANOVA,

Correction for Multiple Testing

Bonferroni, Holm,

Westfall and Young maxT,

Benjamini and Hochberg

Unsupervised Clustering

PAM, CLARA, Hierarchical clustering

Silhouettes

CodeLink compatible

slide9

GeneSifter – Microarray Data Analysis

Integrated tools for determining Biological Significance

One Click Gene Summary™

Ontology Report

Pathway Report

Search by ontology terms

Search by KEGG terms or Chromosome

the genesifter data center
The GeneSifter Data Center
  • Free resource
    • Training
    • Research
    • Publishing
  • 5 areas
    • Cardiovascular
    • Cancer
    • Neuroscience
    • Immunology
    • Oral Biology
  • Access to :
    • Data
    • Analysis summary
    • Tutorials
    • WebEx
the genesifter data center1
The GeneSifter Data Center

www.genesifter.net/dc

genesifter analysis examples
GeneSifter - Analysis Examples

Data Upload

CodeLink

2 groups

(Huntingtons Blood vs Healthy Blood)

3 + groups

(Time series, dose response, etc.)

Differential expression

Fold change

Quality

ANOVA

False discovery rate

Differential expression

Fold change

Quality

t-test

False discovery rate

Visualization

Hierarchical clustering

PCA

Partitioning

PAM

Silhouettes

Biological significance

Gene Annotation

Ontology report

Pathway report

slide13

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

General microarry data analysis workflow

From raw data to biological significance

Comparison statistics and correction for multiple testing

GeneSifter Overview

Gene Expression in Huntington\'s Disease Peripheral Blood

Identification of biological themes

Platform comparison

slide14

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
slide15

Pairwise Analysis

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

CodeLink Human 20K Bioarray

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.

slide16

Background - Data

  • 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
slide17

Pairwise Analysis

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

CodeLink Human 20K Bioarray

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.

slide18

Pairwise Analysis

Select group 1

14 normal

Select group 2

12 Huntingtons

slide19

Pairwise Analysis

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

biological significance
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.

ontology report z score
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

slide29

Pairwise Analysis - Summary

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

12 HD

14 Control

t-test,

Benjamini and

Hochberg (FDR)

Pattern selection

Z-scores

Biological processes

Protein biosynthesis (104)

Ubiquitin cycle (123)

RNA splicing (53)

KEGG

Oxidataive phosphorylation (35)

Apoptosis (22)

2606 increased

In HD

~20,000 genes

5684 genes

Biological processes

Neurogenesis (90)

Cell adhesion (120)

Sodium ion transport (29)

G-protein coupled receptor signaling (114)

KEGG

Neuroactive ligand-receptor interaction (56)

3078 decreased

In HD

slide30

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

General microarry data analysis workflow

From raw data to biological significance

Comparison statistics and correction for multiple testing

GeneSifter Overview

Gene Expression in Huntington\'s Disease Peripheral Blood

Identification of biological themes

Platform comparison

slide31

Pairwise Analysis

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

U133A Human Genome Array

MAS 5 signal

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.

slide32

Pairwise Analysis - Affymetrix

Already normalized

(median)

t-test

Quality filter – 50

(filters out genes with

signal less than 50)

Benjamini and Hochberg

(FDR)

Log transform data

slide33

Pairwise Analysis – Gene List

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

genesifter analysis examples1
GeneSifter - Analysis Examples

Data Upload

CodeLink

2 groups

(Huntingtons Blood vs Healthy Blood)

3 + groups

(Time series, dose response, etc.)

Differential expression

Fold change

Quality

ANOVA

False discovery rate

Differential expression

Fold change

Quality

t-test

False discovery rate

Visualization

Hierarchical clustering

PCA

Partitioning

PAM

Silhouettes

Biological significance

Gene Annotation

Ontology report

Pathway report

slide40

Cluster by Samples – ?

Affymetrix

CodeLink

slide42

Platform Comparison - Summary

CodeLinkAffymetrix

Transcripts Total 19729 22283

Increased in HD 2606 1976

Overlap (LL genes) 41% 65%

Top BP Ontologies

Ubiquitin cycle

RNA splicing

Regulation of translation

Apoptosis

Clustering of samples

slide43

Platform Comparison - Summary

CodeLinkAffymetrix

Increased in HD 2606 1976

Decreased in HD 3708 986

Unique ontology Oxidative Phos. IL-6 Biosynthesis

slide44

MicroarraySuccess.com

Seven Keys to Successful Microarray Data Analysis

Experiment

Design

Platform

Selection

Data

Management

System

Access

Differential

Expression

Biological

Significance

Data

Publication

Type of experiment

Two groups

Time series

Dose Response

Multiple treatments

Replicates

The more the better

Technical vs. biological

Platforms

cDNA

Oligo

One color

Two color

Feature Extraction

Software

File formats

Databases

Raw Data

Storing

Retrieving

Experiment Annotation

Samples

Protocols

Usability

Intuitive

Special training

System Access

Single user desktop

Single user server

Web-based

Sharing data

In the lab

Collaboration

Normalization

Differential Expression

Fold change

Comparison statistics

FWER/FDR

Pattern Identification

Clustering

Visualization

Partitioning

Gene Annotation

UniGene

LocusLink

Gene Ontology

KEGG

OMIM

Single Genes

Gene Summaries

Gene Lists

Ontology Report

Pathway Report

MIAME

What is it?

Publication

Public databases

GEO

ArrayExpress

SMD

Using public data

Meta analysis

Academic partner – University of Washington

the genesifter data center2
The GeneSifter Data Center

www.genesifter.net/dc

slide46

Thank You

CodeLink compatible

www.genesifter.net

Trial account, tutorials, sample data and Data Center

Eric Olson

[email protected]

206.283.4363

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