Using 2-way ANOVA to dissect gene expression following myocardial infarction in mice

Transcription

1 Using 2-way ANOVA to dissect gene expression following myocardial infarction in mice Thank you for waiting. The presentation will be starting in a few minutes at 9AM Pacific Daylight Time. During this webinar you will be in listen only mode, so if you have a question, please type it into the Question and Answer panel at the end of the presentation. Dr. Olson will try to answer as many questions as possible at the end of the presentation. We will also make the slides and a recording of this presentation available after the webinar. Please contact Dr. Olson at eric@genesifter.net if you would like a copy of the slides.

2 Using 2-way ANOVA to dissect gene expression following myocardial infarction in mice N. Eric Olson

3 Using 2-way ANOVA to dissect gene expression following myocardial infarction in mice General microarray data analysis workflow From raw data to biological significance Comparison statistics Two-way ANOVA Correction for multiple testing GeneSifter Overview CardioGenomics Microarray analysis of gene expression following myocardial infarction Data overview Dissection of gene expression using 2-way ANOVA

4 The Microarray Data Analysis Process Experimental Design Number of groups, factors, replicates Data management Data, sample annotation, gene annotation, databases Differential Expression Comparison statistics, Correction for multiple testing, Clustering Biological significance Individual genes, Biological themes Platform Selection One-color, two-color, platform comparisons System access Ease of use, accessibility Making data public and using public data MIAME, Journals, GEO, meta-analysis

5 The Microarray Data Analysis Process Experimental Design Number of groups, factors, replicates Data management Data, sample annotation, gene annotation, databases Differential Expression Comparison statistics, Correction for multiple testing, Clustering Biological significance Individual genes, Biological themes Platform Selection One-color, two-color, platform comparisons System access Ease of use, accessibility Making data public and using public data MIAME, Journals, GEO, meta-analysis

6 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 Sep 1;19(13): Experimental Design and Other Issues in Microarray Studies - Kathleen Kerr -

7 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, 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.

8 t-test for comparison of two groups Calculate t statistic t = difference between groups difference within groups = Mean grp 1 Mean grp 2 ((s 12 /n 1 ) + (s 22 /n 2 )) 1/2 s = variance n = size of sample Determine confidence level for t (probability that t could occur by chance) df = n 1 + n 2-2 The larger the difference between the groups and the lower the variance the bigger t will be and the lower p will be

9 Differential Expression 2 groups, 4 replicates each Mean, standard deviation, fold change and p-value calculated 8 Mean Signal Exp Con Gene 1 Fold Change = 5.3 p = 0.19 Mean Signal Exp Con Gene 2 Fold Change = 5.3 p = 0.03 Fold change vs. p value

10 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 Dec;31(4): Glantz S. Primer of Biostatistics. 5 th Edition. McGraw-Hill. Glantz S, Slinker B. Primer of Regression and Analysis of Variance. McGraw-Hill.

11 Two-way ANOVA Example Triple treatment in Huntington s Disease model (R6/2 mice, GSE857, Affymetrix U74Av2) Treatment - + Disease effect Disease WT R6/ WT - WT + R6/2 - R6/2 + Gene expression pattern Treatment effect Interaction Disease and treatment effect (no Interaction)

12 Two-way ANOVA compared to t-test Triple treatment in Huntington s Disease model (R6/2 mice, GSE857, Affymetrix U74Av2) Treatment - + Disease WT R6/ t-test Two-way Disease Differences Pavlidis P, Noble WS. Analysis of strain and regional variation in gene expression in mouse brain. Genome Biol. 2001;2(10):RESEARCH0042.

13 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 methods are more conservative, but the FDR methods are 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): Reiner, A., et al. (2003) Identifying differentially expressed genes using false discovery rate controlling procedures. Bioinformatics 19(3):

14 Multiple Hypothesis Testing in Microarray Experiments Per comparison error rate (PCER) the probability of error for each comparison Family-wise error rate (FWER) the probability of at least one error for all comparisons False discovery rate (FDR) the expected proportion of errors among your results An error means a false positive Example : 1000 genes and 50 differentially expressed using cutoff of 5% PCER - using a 5% PCER means a 5% chance of error for each comparison, so perhaps 50 errors for 1000 comparisons. This is not acceptable, you don t have confidence than any of your results are real (not errors). FWER using 5% FWER means there is a 5% chance that you have at least 1 error. This is very good and would be a very conservative requirement, you are confident that all of your results are real. FDR using 5% FDR you would expect 2.5 errors (5% of 50). This is probably acceptable, you are confident that most of your results are real.

15 Correction Example CodeLink Ms 10K BioArray Lacrimal + Placebo Lacrimal + Androgen 3 biological replicates 9982 Comparisons t-test 5% PCER : 2458 genes (estimate 499 errors) 5% FWER: 19 genes (5% chance of 1 error) 5% FDR: 904 genes (estimate 45 errors)

16 Identification and partitioning of expression patterns Cluster Analysis - clustering methods are descriptive or exploratory tools that can be used to identify groups within complex datasets. Visualization Methods such as hierarchical clustering can be used to help identify patterns in a large dataset. Hierarchical methods provide a hierarchy of clusters, from the smallest, where all objects (gene expression profiles) are in one cluster, through to the largest set, where each observation is in its own cluster. Partitioning this type of cluster analysis can be used to separate data into discrete groups or clusters. Partitioning methods partition the data (list of genes) into a prespecified number (K) of mutually exclusive groups based on feature vector (expression profile). - K-means -PAM (Partitioning around medoids) Cluster analysis is used to identify patterns of gene expression within large datasets and to segregate those genes based on these patterns. Quackenbush J. Computational analysis of microarray data. Nat Rev Genet Jun;2(6): Review. Kaufman L, Rousseeuw PJ: Finding Groups in Data: An Introduction to Cluster Analysis. New York: Wiley; 1990.

17 Identification and partitioning of expression patterns Hierarchical Partitioning Cluster analysis 1846 differentially expressed genes from FVB heart development time series.

18 Analysis Workflow Examples 2 groups (apoe -/- aorta vs. wt aorta) 5 groups, single factor (Drosophila Innate Immune Response Time Series) 18 groups, two factors (Gene expression after myocardial infarction in mouse) t-test BH (FDR) Up regulated Down 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.)

19 Using 2-way ANOVA to dissect gene expression following myocardial infarction in mice General microarray data analysis workflow From raw data to biological significance Comparison statistics Two-way ANOVA Correction for multiple testing GeneSifter Overview CardioGenomics Microarray analysis of gene expression following myocardial infarction Data overview Dissection of gene expression using 2-way ANOVA

20 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, one-way ANOVA, two-way ANOVA Correction for Multiple Testing Bonferroni, Holm, Westfall and Young maxt, Benjamini and Hochberg Unsupervised Clustering PAM, CLARA, Hierarchical clustering Silhouettes

21 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

22 The GeneSifter Data Center Free resource Training Research Publishing 6 areas Cardiovascular Cancer Endocrinology Neuroscience Immunology Oral Biology Access to : Data Analysis summary Tutorials WebEx

23 The GeneSifter Data Center

24 The Microarray Data Analysis Process Experimental Design Number of groups, factors, replicates Data management Data, sample annotation, gene annotation, databases Differential Expression Comparison statistics, Correction for multiple testing, Clustering Biological significance Individual genes, Biological themes Platform Selection One-color, two-color, platform comparisons System access Ease of use, accessibility Making data public and using public data MIAME, Journals, GEO, meta-analysis

25 The CardioGenomics Program for Genomic Appliactions Genomics of Cardiovascular Development, Adaptation, and Remodeling. NHLBI Program for Genomic Applications, Harvard Medical School. URL:

26 CardioGenomics Microarray Data Animal models of cardiomyopathy Human tissues Affymetrix Arrays MAS5 data and CEL files available

27 Using 2-way ANOVA to dissect gene expression following myocardial infarction in mice General microarray data analysis workflow From raw data to biological significance Comparison statistics Two-way ANOVA Correction for multiple testing GeneSifter Overview CardioGenomics Microarray analysis of gene expression following myocardial infarction Data overview Dissection of gene expression using 2-way ANOVA

28 CardioGenomics - Mouse Myocardial Infarction Model 6 time points after ligation of LAD artery (1hr -> 8wk) Three sites - Sham LV, Infarcted LV, Non-infarcted LV Affymetrix Mouse U74Av2 Array MAS5 text files loaded (signal and detection call) CEL files also available (RMA or GC-RMA)

29 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 Dec;31(4): Glantz S. Primer of Biostatistics. 5 th Edition. McGraw-Hill. Glantz S, Slinker B. Primer of Regression and Analysis of Variance. McGraw-Hill.

30 Project Analysis : Two-way ANOVA Factor One: Site (3 levels, LV, NILV, ILV) Factor Two: Time after ligation (6 levels, 1 hr, 4 hr, 24 hr,48 hr,1 wk, 8 wk) Site: Time: LV NILV ILV Gene expression pattern Site Effect Time Effect Interaction

31 Project Analysis : Two-way ANOVA

32 Project Analysis : Two-way ANOVA Identify Factors Indicate number of levels for each

33 Project Analysis : Two-way ANOVA Identify levels for each factor

34 Project Analysis : Two-way ANOVA Assign levels for each factor to cells

35 Project Analysis : Two-way ANOVA Include fold-change cutoff if desired Select effect to filter on first (you can switch later)

36 Two-way ANOVA : Interaction

37 Two-way ANOVA : Interaction

38 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.

39 One-Click Gene Summary

40 Two-way ANOVA : Interaction

41 KEGG Report

42 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

43 KEGG Report

44 Two-way ANOVA : Interaction

45 Interaction - Visualization Visualization of 2513 genes (Interaction p < 0.001)

46 Interaction Partition Clustering

47 Interaction Cluster 1

48 Interaction Cluster 2

49 Interaction Cluster 3

50 Interaction Cluster 4

51 Two-way ANOVA : Summary Gene expression following myocardial infarction 18 groups (3 biological replicates) 2 factors (Site and Time) Differential Expression (Two-way ANOVA, interaction) Visualization (Hierarchical clustering) Partitioning (Partitioning around medoids) Biological significance (Biological process and KEGG) Glucan metabolism (10) Oxidative phosphorylation (56) Fatty acid metabolism (15) ~12,000 transcripts 2513 genes Cell division (21) Immune cell activation (18) Regulation of actin cytoskeleton (26) Cell adhesion (24) Proteolysis (21) TGF beta signaling (7) Inflammatory response (19) Regulation of cell cycle (21) Toll-like receptor signaling (10)

52 Gene expression following myocardial infarction Pathways Biological processes Molecular functions Inflammatory response Positive regulation of cell proliferation Regulation of cell cycle Toll-like receptor signaling Jak-STAT signaling pathway Cell division Immune cell activation Small GTPase mediated signal transduction Regulation of actin cytoskeleton Leukocyte transendothelial migration Cell adhesion Cell cycle arrest Extracellular matrix structural constituent Proteolysis TGF beta signaling

53 Future analysis - Transcription factors 63 genes with transcription factor activity differentially expressed - Isolate differences between infarcted and non-infarcted two-anova with only two sites (ilv and nilv)

54 GeneSifter Workflow Examples 2 groups (apoe -/- aorta vs. wt aorta) 5 groups, single factor (Drosophila Innate Immune Response Time Series) 18 groups, two factors (Gene expression after myocardial infarction in mouse) t-test BH (FDR) Up regulated Down 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.)

55 Resources Monthly Webinar Series 7/20/06 - Using 2-way ANOVA to dissect gene expression following myocardial infarction in mice Archived - 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

56 Thank You Trial account, tutorials, sample data and Data Center Eric Olson