Using 2-way ANOVA to dissect the immune response to ... · PDF fileUsing 2-way ANOVA to dissect the immune response to hookworm infection in ... response to hookworm infection in mouse

Using 2-way ANOVA to dissect the immune response to hookworm infection in mouse lung

General microarry data analysis workflow

From raw data to biological significanceComparison statisticsTwo-way ANOVAGeneSifter OverviewThe Gene Expression Omnibus (GEO)

Microarray analysis of gene expression following hookworm infection

Data overviewDissection of the immune response using 2-way ANOVA


Experimental DesignNumber of groups, factors, replicates

Data managementData, sample annotation, gene annotation, databases

Differential ExpressionComparison statistics, Correction for multiple testing, Clustering

Biological significanceIndividual genes, Biological themes

Platform SelectionOne-color, two-color, platform comparisons

System accessEase of you, accessibility

Making data public and using public dataMIAME, Journals, GEO, meta-analysis

The Microarray Data Analysis Process

Experimental DesignNumber of groups, factors, replicates

Data managementData, sample annotation, gene annotation, databases

Differential ExpressionComparison statistics, Correction for multiple testing, Clustering

Biological significanceIndividual genes, Biological themes

Platform SelectionOne-color, two-color, platform comparisons

System accessEase of you, accessibility

Making data public and using public dataMIAME, Journals, GEO, meta-analysis

The Microarray Data Analysis Process

Experiment Design•Type of experiment

– Two groups• Normal vs. cancer• Control vs. treated

– Three or more groups, single factor• Time series• Dose response• Multiple treatment

– Four or more groups, multiple factors• Time series with control and treated cells

The type of experiment and number of groups and factors will determine the statistical methods needed 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.

Pavlidis P, Li Q, Noble WS. The effect of replication on gene expression microarray experiments. Bioinformatics. 2003 Sep 1;19(13):1620-7. Experimental Design and Other Issues in Microarray Studies - Kathleen Kerr -http://ra.microslu.washington.edu/learning/documents/KerrNAS.pdf

Differential ExpressionThe 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, single factor

– One-way ANOVA, Kruskal-Wallis• 4 or more groups, multiple factors

– Two-way ANOVA

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.

difference between groups

difference within groups

t-test for comparison of two groups

Calculate t statistic

t =

Determine confidence level for t(probability that t could occur by chance)

df = n1 + n2 - 2

Mean grp 1 – Mean grp 2

((s12/n1) + (s2

2/n2))1/2=

s = variancen = size of sample

The larger the difference between the groups and the lower the variance the bigger t will be and the lower p will be

0

1

2

3

4

5

6

7

8

Exp Con0

2

4

6

8

10

12

14

16

18

Exp Con

Gene 1Fold Change = 5.3p = 0.19

Gene 2Fold Change = 5.3p = 0.03

Mea

n Si

gnal

Mea

n Si

gnal

Fold change vs. p value

2 groups, 4 replicates eachMean, standard deviation, fold change and p-value calculated

Differential Expression

Analysis of Variance (ANOVA)

•Like t-test, identifies genes with large differences between groups and small differences within groups

•For use with 3 or more groups

•One-way and two-way

•One-way examines effects of one factor on gene expression

•Two-way can examine effects of two factors on gene expression as well as the interaction of the two factors

Pavlidis P. Using ANOVA for gene selection from microarray studies of the nervous system. Methods. 2003 Dec;31(4):282-9. Glantz S. Primer of Biostatistics. 5th Edition. McGraw-Hill.Glantz S, Slinker B. Primer of Regression and Analysis of Variance. McGraw-Hill.

Two-way ANOVA Example

WT

-

WT

+

R6/

2 -

R6/

2 +

Triple treatment in Huntington’s Disease model (R6/2 mice, GSE857, Affymetrix U74Av2)

Treatment- +

Dis

ease WT

R6/2

3

3 3

Disease effect

Treatment effect

Interaction

Disease and treatment effect(no Interaction)

Gen

e ex

pres

sion

pat

tern

3

Pavlidis P, Noble WS. Analysis of strain and regional variation in gene expression in mouse brain. Genome Biol.2001;2(10):RESEARCH0042.

Two-way ANOVA compared to t-test

t-test Two-wayDisease Differences 274 791

Treatment- +

Dis

ease WT

R6/2

3

3 3

3

Triple treatment in Huntington’s Disease model (R6/2 mice, GSE857, Affymetrix U74Av2)

Analysis Workflow Examples2 groups

(apoE -/- aorta vs. wt aorta)5 groups, single factor

(Drosophila Innate Immune Response Time Series)12 groups, two factors(Immune response to hookworms

in mouse lung)

t-test

BH (FDR)

Up regulatedDown regulated

Gene Lists

One-way ANOVA

BH (FDR)

Clustering

Gene Lists

Two-way ANOVA

BH (FDR)

Clustering

Gene Lists

Individual genes of interest

Biological themes (Pathways, molecular functions, etc.)






AccessibilityWeb-basedSecureData management

DataAnnotation (MIAME)

Multiple upload toolsCodeLinkAffymetrixIlluminaAgilent Custom

Differential Expression - Powerful, accessible tools fordetermining Statistical Significance

R based statisticsBioconductorComparison Tests

t-test, Welch’s t-test, Wilcoxon Rank sum test, one-way ANOVA, two-way ANOVA

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

Unsupervised ClusteringPAM, CLARA, Hierarchical clusteringSilhouettes

GeneSifter – Microarray Data Analysis

GeneSifter – Microarray Data AnalysisIntegrated 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

• 6 areasCardiovascularCancerEndocrinologyNeuroscienceImmunologyOral Biology

• Access to :DataAnalysis summaryTutorialsWebEx

The GeneSifter Data Center

www.genesifter.net/dc

Using the Gene Expression Omnibus (http://www.microarraysuccess.org/newsletter)

The Gene Expression Omnibus (GEO)

Gene expression data repository (mostly microarrays)

Over 3000 data sets

All array platforms represented

Searchable byPlatformSpeciesExperiment annotation

Downloadable data






Project Analysis : Two-way ANOVA

Scott lab, Johns Hopkins University(Bloomberg School of Public Health )

Affymetrix Mouse 430 2.0

Wild type and SCID mice

Control and 5 time points after infection

CEL files available(loaded and MAS5 processed in GeneSifter)

Alex Loukas, and Paul Prociv. Immune Responses in Hookworm Infections. Clinical Microbiology Reviews, October 2001, p. 689-703, Vol. 14, No. 4

Analysis of Variance (ANOVA)

•Like t-test, identifies genes with large differences between groups and small differences within groups

•For use with 3 or more groups

•One-way and two-way

•One-way examines effects of one factor on gene expression

•Two-way can examine effects of two factors on gene expression as well as the interaction of the two factors

Pavlidis P. Using ANOVA for gene selection from microarray studies of the nervous system. Methods. 2003 Dec;31(4):282-9. Glantz S. Primer of Biostatistics. 5th Edition. McGraw-Hill.Glantz S, Slinker B. Primer of Regression and Analysis of Variance. McGraw-Hill.


Factor One: Strain (2 levels, SCID, WT)Factor Two: Time after infection (6 levels, con, 2,3,4,8,12 dpi)

Gen

e ex

pres

sion

pat

tern

WT SCIDStrain:Time:

Strain Effect

Time Effect

Interaction



Identify Factors

Indicate number of levels for each

Identify levels for each factor


Assign levels for each factor to cells

Include fold-change cutoff if desired

Select effect to filter on first (you can switch later)

Two-way ANOVA : Strain Effects

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, that can be used by databases such as Entrez Gene.

• 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: usigGene Ontology and GenMAPP to create a global gene-expression profile from microarray data, Genome Biology 2003, 4:R7

Z-score Report

KEGG Report


Strain effects - Visualization

Visualization of 517 genes(strain effect p < 0.001)

Segregation of expression patterns using k-medoids clustering

Strain effects - Partitioning

Silhouette widths are used to find “best” number of clusters

k mean sil. width2 0.714 0.416 0.25

Dudoit S, Fridlyand J. A prediction-based resampling method for estimating the number of clusters in a dataset. Genome Biol. 2002 Jun 25;3(7):RESEARCH0036. Epub 2002 Jun 25.

Strain effects - Partitioning

Strain : Cluster 1

Strain : Cluster 2

Two-way ANOVA : Time Effects

Two-way ANOVA : Time Effects

Time : Cluster 1

Time : Cluster 2

Two-way ANOVA : Interaction

Two-way ANOVA : Interaction

Interaction : Cluster 3

Interaction : Cluster 2

Two-way ANOVA : Summary

Immune response to hookworms in mouse lung12 groups (3 biological replicates)

2 factors (Strain and Time)

~39,000 genes 56 genes

Z-scores Pattern selection –Hierachical clustering, PAM(Interaction)

Two-way ANOVA

Interaction

Strain

Time

517 genes

1054 genes

Biological processTranscription (4)Circadian Rhythm (3)

Biological processImmune response (8)Chitin catabolism (4)

Strain effects, time effects and interaction

GeneSifter Workflow Examples2 groups

(apoE -/- aorta vs. wt aorta)5 groups, single factor

(Drosophila Innate Immune Response Time Series)12 groups, two factors(Immune response to hookworms

in mouse lung)

t-test

BH (FDR)

Up regulatedDown regulated

Gene Lists

One-way ANOVA

BH (FDR)

Clustering

Gene Lists

Two-way ANOVA

BH (FDR)

Clustering

Gene Lists

Individual genes of interest

Biological themes (Pathways, molecular functions, etc.)

Resources

Monthly Webinar Series

6/15/06 - Using 2-way ANOVA to dissect the immune response to hookworm infection in mouse lung

Archived - The microarray data analysis process - from raw data to biological significance

Archived - Microarray analysis of gene expression in androgen-independent prostate cancer

Archived - Microarray analysis of gene expression in male germ cell tumors Archived - Microarray analysis of gene expression in Huntington's Disease

peripheral blood - a platform comparison

Eric [email protected]

Thank You

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

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Using 2-way ANOVA to dissect the immune response to ... · PDF fileUsing 2-way ANOVA to dissect the immune response to hookworm infection in ... response to hookworm infection in mouse