Microarray analysis of gene expression in male germ cell tumors

Transcription

1 Microarray analysis of gene expression in male germ cell tumors

2 Microarray analysis of gene expression in male germ cell tumors General microarry data analysis workflow From raw data to biological significance Comparison statistics and correction for multiple testing Clustering for visualization and partitioning GeneSifter Overview Microarray analysis of gene expression in male germ cell tumors Identification of biological themes Normal vs. seminoma Common to all tumors Tumor specific

3 Analysis Workflow Raw data Normalized, scaled data Differentially expressed genes Identify and partition expression patterns Gene Summaries Biological themes (Pathways, molecular function, etc.)

4 Analysis Workflow Raw data Normalized, scaled data Differentially expressed genes Identify and partition expression patterns Gene Summaries Data upload Comparison statistics, correction for multiple testing Up and down regulated, magnitude, clustering Annotation (UniGene, Entrez Gene, Gene Ontologies, etc.) Biological themes (Pathways, molecular function, etc.) Ontology report, pathway report, z-score

5 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 -

6 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.

7 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): Reiner, A., et al. (2003) Identifying differentially expressed genes using false discovery rate controlling procedures. Bioinformatics 19(3):

8 microarraysuccess.com 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. Clustering methods can be used to identify patterns of gene expression in microarray datasets. Visualization Methods such as hierarchical clustering can be used to help identify patterns in a large dataset. Partitioning this type of cluster analysis can be used to separate data into discrete groups or clusters. - 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.

9 microarraysuccess.com Identification and partitioning of expression patterns Cluster analysis Used to identify groups, or clusters, of similar objects (gene expression profiles) on the basis of a set of feature vectors (expression measurements). Two general types - 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 methods partition the data (list of genes) into a pre-specified number (K) of mutually exclusive groups based on feature vector (expression profile).

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

11 Analysis Workflow 2 groups (Normal vs. Seminoma) MAS5 Raw data 3+ groups (All tumors) MAS5 Normalized, scaled data t-test ANOVA BH (FDR) BH (FDR) Differentially expressed genes Hierarchical clustering Upregulated genes Partitioning Identify and partition expression patterns One click gene summary Gene Summaries One click gene summary z-score report Biological themes (Pathways, molecular function, etc.) z-score report

12 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

13 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

14 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

15 The GeneSifter Data Center

16 Microarray analysis of gene expression in male germ cell tumors General microarry data analysis workflow From raw data to biological significance Comparison statistics and correction for multiple testing Clustering for visualization and partitioning GeneSifter Overview Microarray analysis of gene expression in male germ cell tumors Identification of biological themes Normal vs. seminoma Common to all tumors Tumor specific

17 Background Testicular Germ Cell Tumors Most common cancer in males Rates Increasing Seminoma undifferentiated germ cells Non seminoma Yolk Sac Tumor extraembryonic differentiation Teratoma multiple somatic lineages Embryonal Carcinoma least differentiated Choriocarcinoma - extraembryonic differentiation Mixed

18 The Data Affymetrix GeneChip Human Genome U133A Korkola JE, Houldsworth J, Dobrzynski D, Olshen AB, Reuter VE, Bosl GJ, Chaganti RS. Gene expression-based classification of nonseminomatous male germ cell tumors. Oncogene Jul 28;24(32):

19 Background - Data Gene expression-based classification of nonseminomatous male germ cell tumors Oncogene Jul 28;24(32): Korkola JE, Houldsworth J, Dobrzynski D, Olshen AB, Reuter VE, Bosl GJ, Chaganti RS.. Cell Biology Program, Memorial Sloan-Kettering Cancer Center, New York, NY 84 NSGCT (42 pure and 42 mixed) Affymetrix GeneChip human Genome U133A and U133B Identified 146 transcripts that classified histology (93% accuracy) Data available from GEO (GSE3218)

20 The Data Affymetrix GeneChip Human Genome U133A 5 Normal 10 Pure Yolk Sac Tumor 12 Pure Seminoma 15 Pure Teratoma 15 Pure Embryonal Carcinoma Korkola JE, Houldsworth J, Dobrzynski D, Olshen AB, Reuter VE, Bosl GJ, Chaganti RS. Gene expression-based classification of nonseminomatous male germ cell tumors. Oncogene Jul 28;24(32):

21 GeneSifter - Analysis Examples 2 groups (Normal testis vs. Seminoma Data Upload Affymetrix 3 + groups (All tumors.) 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

22 Pairwise Analysis Select group 1 5 Normal Testis Select group 2 12 Pure Seminoma

23 Pairwise Analysis Already normalized (MAS5) t-test Quality filter P (filters out A genes) Benjamini and Hochberg (FDR) Log transform data

24 Pairwise Analysis Gene List

25 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.

26 One-Click Gene Summary

27 Pairwise Analysis Gene List

28 Ontology Report

29 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

30 Z-score Report

31 Z-score Report

32 KEGG Report

33 Pairwise Analysis - Summary Normal Testis compared to Seminoma 5 Normal Testis 12 Pure Seminoma t-test, Benjamini and Hochberg (FDR) Pattern selection 2444 increased In Seminoma Z-scores Biological processes Protein biosysnthesis (155) Antigen presentation (19) RNA Splicing (30) Prostaglandin biosynthesis (5) KEGG Ribosome (76) ~21,000 transcripts 5990 genes 3546 decreased In Seminoma Biological processes Spermatogenesis (60) Protein ubiquitination (63) Protein amino acid dephosphorylation (39) KEGG Basal transcription factors (15) Ubiquitin mediated proteolysis (17) Circadian rhythm (7)

34 Microarray analysis of gene expression in male germ cell tumors General microarry data analysis workflow From raw data to biological significance Comparison statistics and correction for multiple testing Clustering for visualization and partitioning GeneSifter Overview Microarray analysis of gene expression in male germ cell tumors Identification of biological themes Normal vs. seminoma Common to all tumors Tumor specific

35 Pairwise Analysis Gene List Normal vs. Embryonal Carcinoma

36 Gene Lists Common and Unique Genes

37 Platform comparison Biological themes

38 Z-score Report

39 GeneSifter - Analysis Examples 2 groups (Normal testis vs. Seminoma Data Upload Affymetrix 3 + groups (All tumors.) 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

40 Projects User defined grouping of conditions Creates new set of normalized, averaged, transformed data Allows analysis across more than 2 groups Clustering, search by ontologies and other analysis options

41 Projects - Filtering ANOVA, 4 fold change cutoff, P quality, Benjamini and Hochberg (FDR)

42 Projects - Visualization Hierarchical clustering of 2573 genes

43 Projects - Partitioning

44 Projects Summary Cluster 4

45 Projects Summary Cluster 1 Cadmium ion binding

46 Projects - Visualization Hierarchical clustering of 2573 genes

47 Projects Pattern Search

48 Projects Increased in all tumors

49 Projects Increased in Teratoma

50 Projects Search by KEGG Pathway Wnt Signaling Pathway

51 Projects Search by KEGG Pathway Wnt Signaling Pathway

52 Projects Clustering of individual samples Hierarchical clustering of 57 samples

53 Projects Clustering of individual samples 2 Embryonal Carcinoma groups Group 2 Group 1

54 Pairwise Analysis Gene List Embryonal Carcinoma Group 1 vs. Group 2

55 Platform comparison Biological themes

56 Platform comparison Biological themes

57 Microarray analysis of gene expression in male germ cell tumors GSE3218 Affymetrix GeneChip Human Genome U133A 5 Normal 10 Pure Yolk Sac Tumor 12 Pure Seminoma 15 Pure Teratoma 15 Pure Embryonal Carcinoma 1000s of differentially expressed genes (normal vs tumor) Seminoma protein ubiquitination (dn), RNA splicing (up) 1000s of genes common to all tumors Spermatogenesis, carbohydrate metabolism (dn) Cell adhesion, regulation of actin cytoskeleton, immune response (up) 100s of genes common to 2 or more tumors Cadmium ion binding, cytokine activity (up) Small number of genes unique to each tumor 2 groups of embryonal carcinoma?

58 Microarray analysis of gene expression in male germ cell tumors Not examined: GSE3218 U133B Arrays Mixed tumors Metastatic vs primary Chemotherapy GSE1818 Agilent arrays 3 normal, 3 seminoma, 4 yolk sac tumor, 4 teratoma, 5 embryonal carcinoma Skotheim RI, Lind GE, Monni O, Nesland JM, Abeler VM, Fossa SD, Duale N, Brunborg G, Kallioniemi O, Andrews PW, Lothe RA. Differentiation of human embryonal carcinomas in vitro and in vivo reveals expression profiles relevant to normal development. Cancer Res Jul 1;65(13):

59 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

60 The GeneSifter Data Center

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