The microarray data analysis process - from raw data to biological significance. N. Eric Olson

Transcription

1 The microarray data analysis process - from raw data to biological significance N. Eric Olson eric@genesifter.net

2 Microarrays 60,000 56,000 Data Points per Experiment 50,000 40,000 30,000 20,000 10, c c Today (Northern Blot) (PCR) (Microarray) Microarrays Allow Measurement of Expression Profiles for Entire Genomes Rather than Individual Genes

3 The impact of microarrays in biological research Data Data Experiments Experiments Traditional Biological Research Microarrays in Biological Research

4 Microarray Challenges 80% 70% 60% 50% 40% 30% 20% 10% 0% Labs Responding Throughput Reliability Software Bioinformatics Funding Commitments

5 Microarray Analysis

6 Microarray Data Analysis Con CHP Exp CHP 2 group 3 replicates U133 Plus 2 (56K Genes) 2,000,000+ data points Normalization Comparison statistics Multiple Testing Correction Clustering? Differentially Expressed Genes 1000s of genes UniGene LocusLink Gene Ontologies Identification of gene families and pathways 10s to 100s of genes Individual genes of interest

7 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 you, accessibility Making data public and using public data MIAME, Journals, GEO, meta-analysis

8 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 you, accessibility Making data public and using public data MIAME, Journals, GEO, meta-analysis

9 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 Three 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 -

10 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.

11 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

12 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

13 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-factor 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.

14 Two-way ANOVA Data: Sex differences in salivary glands (CodeLink Ms 10K Bioarray) M Sex F Strain effect Gland Parotid Sublingual Par F Par M Sub F Sub M Gene expression pattern Sex effect Interaction Strain and sex effect (no Interaction)

15 Two-way ANOVA compared to t-test Data: Sex differences in salivary glands (CodeLink Ms 10K Bioarray) M Sex F Gland Parotid Sublingual Two-way t-test Sex Differences Pavlidis P, Noble WS. Analysis of strain and regional variation in gene expression in mouse brain. Genome Biol. 2001;2(10):RESEARCH0042.

16 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):

17 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 (FWER) 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.

18 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)

19 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.

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

21 Differential Expression - Gene Lists

22 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.

23 Gene annotation for individual genes

24 Ontology reports identify biological themes

25 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

26 Identify biological themes associated with gene clusters

27 Workflow Examples 2 groups (Androgen-dependent and androgenindependent prostate cancer) 5 groups, single factor (Drosophila Innate Immune Response Time Series) 2 groups, two factors (Parotid and sublingual gland, Male and Female) 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.)

28 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 you, accessibility Making data public and using public data MIAME, Journals, GEO, meta-analysis

29 Platform Selection The microarray platform used will have a substantial effect on the data analysis process. Two color arrays have several options for how the experiment can be designed. The second channel can either be part of the experiment or it can be an external reference RNA that is strictly used for the creation of a ratio. For single channel arrays the sample is hybridized to one array and the signal intensity is used for measuring gene expression. Microarray Platforms Affymetrix GeneChip: One-color oligo arrays, 22 probes/gene GEHC CodeLink Bioarray: One-color oligo array, 1 probe/gene Agilent Arrays: Two-color oligo array, 1 probe/gene Illumina bead arrays: One-color oligo bead array, 1 probe/gene ABI Arrays: One-color oligo array, 1 probe/gene Custom cdna or Oligo: usually two-color, 1 probe/gene Experimental design and analysis is generally more straightforward with one-color microarrays.

30 Platform Comparisons Huntington s Blood vs. Healthy (GSE1767 & GSE1751) Same samples on Affymetrix and CodeLink Genes 65% overlap Biological themes 100% top 10 Normal Testis vs. Seminoma (GSE3218 & GSE1818) Independent samples, Affymetrix, Agilent Genes 55% overlap 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.

31 System Access System access, defined both in terms of physical access and the usability of the microarray data analysis system, is critical for the microarray data analysis process; the people who need to work with the data must be able to access the system when needed and they must be able to make full use of it. Usability - Intuitive interface Special training The usability of the system determines the amount of time spent learning the system and who will be able to work with the data. Accessibility Single-user desktop Single-user server Web-based The type of system used will determine who can access it, how many people can access it and the locations from which it can be accessed.

32 System Access Web-based systems Thin client runs through any browser on any platform IE, FireFox, Netscape, Opera, etc. Mac, PC, Linux Secure access from any computer on the internet Any computer in the lab Access from home or traveling Facilitates sharing data and collaboration Flexible data loading load from one location, analyze from another Live presentations of data

33 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 you, accessibility Making data public and using public data MIAME, Journals, GEO, meta-analysis

34 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 (

35 The Gene Expression Omnibus (GEO) Search by experiment annotation (or species or platform).

36 The Gene Expression Omnibus (GEO) 26 results

37 Androgen-independent prostate cancer

38 Androgen-independent prostate cancer Sample information Author (submitter) information Export Data GEO format Raw data (CEL files) for many

39 GeneSifter - Analysis Examples 2 groups (ADPC vs AIPC) Data Upload Affymetrix 3 + groups Differential expression Fold change Quality Wilcoxon rank sum 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

40 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

41 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

42 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

43 The GeneSifter Data Center

44 The Data Affymetrix GeneChip Human Genome U133A Tumor tissue from 20 primary prostate cancer tumors 10 Androgen Dependent 10 Androgen Independent CEL Land CHP data available CEL files loaded into GeneSifter and processed with GC-RMA Best CJ, Gillespie JW, Yi Y, Chandramouli GV, Perlmutter MA, Gathright Y, Erickson HS, Georgevich L, Tangrea MA, Duray PH, Gonzalez S, Velasco A, Linehan, WM, Matusik RJ, Price DK, Figg WD, Emmert-Buck MR, Chuaqui RF. Molecular alterations in primary prostate cancer after androgen ablation therapy. Clin Cancer Res Oct 1;11(19 Pt 1):

45 Pairwise Analysis Select group 1 10 ADPC Select group 2 10 AIPC Already normalized (Quantile, GC-RMA) Wilcoxon rank sum test Benjamini and Hochberg (FDR) Data already log transformed (GC-RMA)

46 Pairwise Analysis Gene List

47 One-Click Gene Summary

48 Pairwise Analysis Gene List

49 Ontology Report

50 Z-score Report

51 Z-score Report

52 KEGG Report

53 Pairwise Analysis - Summary Androgen-Dependent Prostate Cancer compared to Androgen-Independent Prostate Cancer 10 Androgen Dependent 10 Androgen Independent Wilcoxon, Benjamini and Hochberg (FDR) ~21,000 transcripts 468 genes Pattern selection 222 increased In AIPC 246 decreased In AIPC Z-scores Biological processes Cell adhesion (15) Cell cycle (19) RNA processing (13) Cellular localization (12) KEGG Apoptosis (3) Biological processes Protein biosynthesis (25) Regulation of apoptosis (11) KEGG Ribosome (15) Oxidative phosphorylation (7)

54 GeneSifter - Analysis Examples 2 groups (ADPC vs AIPC) Data Upload Affymetrix 3 + groups Differential expression Fold change Quality Wilcoxon rank sum 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

55 Metastatic PC Comparison Affymetrix U133 Plus 2 4 Benign prostate 5 Localized tumors 4 Metastatic (hormone refractory)

56 Project Metastatic PC Comparison Clustering using 486 AIPC gene set

57 Project Metastatic PC Comparison Clustering using AIPC Ribosome genes

58 Project Metastatic PC Comparison Clustering using AIPC Cell adhesion genes

59 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 you, accessibility Making data public and using public data MIAME, Journals, GEO, meta-analysis

60 Thank You Monthly Webinar Series 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