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

Transcription

1 Microarray Analysis of Gene Expression in Huntington's Disease Peripheral Blood - a Platform Comparison 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 Microarray Analysis of Gene Expression in Huntington's Disease Peripheral Blood - a Platform Comparison N. Eric Olson eric@genesifter.net

3 Microarray Analysis of Gene Expression in Huntington's Disease Peripheral Blood - a Platform Comparison General microarray data analysis workflow From raw data to biological significance Comparison statistics Correction for multiple testing Biological significance Platform comparisons Public microarray databases GeneSifter Overview Microarray analysis of gene expression in HD peripheral blood Identification of biological themes Platform comparison

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 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):

11 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.

12 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)

13 Differential Expression - Gene Lists

14 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.

15 Gene annotation for individual genes

16 Ontology reports identify biological themes

17 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

18 Analysis Workflow Examples 2 groups (HD and healthy blood ) 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 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

20 Platform Comparisons Normal Testis vs. Seminoma (GSE3218 & GSE1818) Independent samples, Affymetrix, Agilent Convert accession/ref Seq ids to Entrez Gene Ids Genes 55% overlap Identify significant ontologies/pathways Biological themes - 100% top 10 Additional references: Barnes M, Freudenberg J, Thompson S, Aronow B, Pavlidis P. Experimental comparison and cross-validation of the Affymetrix and Illumina gene expression analysis platforms. Nucleic Acids Res Oct 19;33(18): Print Larkin JE, Frank BC, Gavras H, Sultana R, Quackenbush J. Independence and reproducibility across microarray platforms. Nat Methods May;2(5): Epub 2005 Apr 21. Irizarry et al. Multiple-laboratory comparison of microarray platforms. Nat Methods May;2(5): Jun;2(6):477.

21 The Gene Expression Omnibus (GEO) Gene expression data repository (mostly microarrays) Over 3000 data sets All array platforms represented Searchable by Platform Species Experiment annotation Downloadable data Using the Gene Expression Omnibus (

22 Microarray Analysis of Gene Expression in Huntington's Disease Peripheral Blood - a Platform Comparison General microarray data analysis workflow From raw data to biological significance Comparison statistics Correction for multiple testing Biological significance Platform comparisons Public microarray databases GeneSifter Overview Microarray analysis of gene expression in HD peripheral blood Identification of biological themes Platform comparison

23 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

24 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

25 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

26 The GeneSifter Data Center

27 Microarray Analysis of Gene Expression in Huntington's Disease Peripheral Blood - a Platform Comparison General microarray data analysis workflow From raw data to biological significance Comparison statistics Correction for multiple testing Biological significance Platform comparisons Public microarray databases GeneSifter Overview Microarray analysis of gene expression in HD peripheral blood Identification of biological themes Platform comparison

28 Background - Huntington s Disease Huntington s Disease (HD) Autosomal dominant neurodegenerative disease Motor impairment Cognitive decline Various psychiatric symptoms Onset years Mutant Huntingtin protein (polyglutamine) Effects transcriptional regulation Transcription effects may occur outside of CNS

29 Background - Data 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 Aug 2;102(31):

30 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 Aug 2;102(31): 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

31 Pairwise Analysis Select group 1 14 normal Select group 2 12 Huntingtons

32 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

33 Pairwise Analysis Gene List

34 One-Click Gene Summary

35 Ontology Report

36 Z-score Report

37 Z-score Report

38 KEGG Report

39 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 2606 increased In HD Z-scores Biological processes Protein biosynthesis (104) Ubiquitin cycle (123) RNA splicing (53) KEGG Oxidataive phosphorylation (35) Apoptosis (22) ~20,000 genes 5684 genes 3078 decreased In HD Biological processes Neurogenesis (90) Cell adhesion (120) Sodium ion transport (29) G-protein coupled receptor signaling (114) KEGG Neuroactive ligand-receptor interaction (56)

40 Mouse model Huntington s Disease 3 WT untreated 3 WT treated 4 R6/2 untreated 3 R6/2 treated

41 RNA Splicing in mouse model 3 WT untreated, 4 R6/2 untreated t-test 500+ genes two-way ANOVA WT untreated and treated R6/2 untreated and treated Significant interaction 5 RNA splicing genes

42 Microarray Analysis of Gene Expression in Huntington's Disease Peripheral Blood - a Platform Comparison General microarray data analysis workflow From raw data to biological significance Comparison statistics Correction for multiple testing Biological significance Platform comparisons Public microarray databases GeneSifter Overview Microarray analysis of gene expression in HD peripheral blood Identification of biological themes Platform comparison

43 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 Aug 2;102(31):

44 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

45 Pairwise Analysis Gene List Human blood expression for Huntington s disease versus control, Affymetrix

46 Gene Lists Common and Unique Genes

47 Platform comparison Biological themes Affymetrix

48 Platform comparison Biological themes CodeLink

49 GeneSifter - Analysis Examples 2 groups (Huntingtons Blood vs Healthy Blood) Data Upload CodeLink 3 + groups (Time series, dose response, etc.) Differential expression Fold change Quality t-test False discovery rate Differential expression Fold change Quality ANOVA False discovery rate Visualization Hierarchical clustering PCA Partitioning PAM Silhouettes Biological significance Gene Annotation Ontology report Pathway report

50 Project Analysis - Clustering

51 Cluster by Samples All Genes CodeLink Affymetrix

52 Cluster by Samples All Genes CodeLink Affymetrix

53 Cluster by Samples? CodeLink Affymetrix

54 Cluster by Samples Y Chrom. Genes CodeLink Affymetrix

55 Platform Comparison - Summary CodeLink Affymetrix Transcripts Total Increased in HD Overlap (LL genes) 41% 65% Top BP Ontologies Ubiquitin cycle RNA splicing Regulation of translation Apoptosis Clustering of samples

56 Platform Comparison - Summary CodeLink Affymetrix Increased in HD Decreased in HD Unique ontology Oxidative Phos. IL-6 Biosynthesis

57 Resources Monthly Webinar Series Archived - 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

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