1. EXPression ANalyzer and DisplayER Adi Maron-Katz Igor Ulitsky Chaim Linhart Amos Tanay Rani Elkon Seagull Shavit Dorit Sagir Eyal David Roded Sharan Israel Steinfeld Yossi Shiloh Ron Shamir Ron Shamir ’ s Computational Genomics Group

2. Schedule  Data, preprocessing, grouping (10:15-11:00)  Hands-on part I (11:00-11:20)  Group analysis (11:20-11:45)  Hands-on part II (11:45-12:05)  Coffee Break (12:05 – 12:20)  Spike (12:20-12:50)  Hands-on part III (12:50-13:10)  Lunch Break (13:10 – 14:10)  Amadeus (14:10 – 14:40)  Hands-on part IV (14:40 – 15:00)

3. EXPANDER EXPANDER – an integrative package for analysis of gene expression data Built-in support for 15 organisms: human, mouse, rat, chicken, fly, zebrafish, C.elegans, yeast (s. cereviciae and s. pombe), arabidopsis, tomato, listeria, E. coli, aspargillus* and rice. Demonstration - on oligonucleotide array data, which contains expression profiles measured in several time points after serum stimulation of human cell line.

4. What can it do?  Low level analysis:  Missing data estimation (KNN or manual)  Data adjustments (merge conditions, divide by base, take log)  Normalization  Probes & condition filtering  High level analysis  Detecting patterns/groups in the data (supervised clustering, differential expression, clustering, bi- clustering, network based grouping).  Ascribing biological meaning to patterns (searching for enrichment within groups).

5. Input data Functional enrichment Preprocessing Promoter signals Visualization utilities Location enrichment miRNA Targets enrichment Links to public annotation databases Grouping KEGG pathway enrichment

6. EXPANDER – Data Input data: ­ Expression matrix (probe-row; condition-column) Expression matrix One-channel data (e.g., Affymetrix) Dual-channel data, in which data is log R/G (e.g. cDNA microarrays) ‘.cel’ files ­ ID conversion file: maps probes to genes ID conversion file ­ Gene groups data: defines gene groups Gene groups data

7. EXPANDER – Data (II)  Data definitions:  Defining condition subsets  Data type & scale (log)  Data Adjustments:  Missing value estimation (KNN or arbitrary)  Reorder conditions  Merging conditions  Merging probes by gene IDs  Divide by base  Log data (base 2)

8. EXPANDER – Preprocessing  Normalization: removal of systematic biases from the analyzed chips quantilelowess  Implemented methods: quantile, lowess  Visualization: box plots, scatter plots (simple, M vs. A)box plots  Filtering: Focus downstream analysis on the set of “responding genes”  Fold-Change  Variation  Statistical tests: T-test, SAM ( Significance Analysis of Microarrays)  It is possible to define “VIP genes”.  Standardization : Mean=0, STD=1 (visualization) Standardization

9. Input data Functional enrichment Preprocessing Promoter signals Visualization utilities Location enrichment miRNA Targets enrichment Links to public annotation databases Grouping KEGG pathway enrichment

10. Cluster Analysis partition the responding genes into distinct groups, each with a particular expression pattern  co-expression → co-function  co-expression → co-regulation Partition the genes to achieve:  High Homogeneity within clusters  High Separation between clusters

11. Cluster Analysis (II) Implemented algorithms:  CLICK, K-means, SOM, Hierarchical Visualization:  Mean expression patternsMean expression patterns  Heat-mapsHeat-maps  Chromosomal positions Chromosomal positions  Network sub-graph  PCA  Clustered matrix

12. Biclustering  Relevant knowledge can be revealed by identifying genes with common pattern across a subset of the conditions  Novel algorithmic approach is needed: Biclustering Clustering seeks global partition according to similarity across ALL conditions >> becomes too restrictive on large datasets.

13. * Bicluster (=module) : subset of genes with similar behavior in a subset of conditions * Computationally challenging: has to consider many combinations of sub-conditions Biclustering II SAMBA = Statistical Algorithmic Method for Bicluster Analysis * A. Tanay, R. Sharan, R. Shamir RECOMB 02

14. Drawbacks/ limitations: Usefull only for over 20 conditions Parameters How to asses the quality of Bi- clusters

15. Biclustering Visualization

16. Network based grouping Goal: to identify modules using gene expression data and interaction networks. GE data + Interactions file (.sif). MATISSE (Module Analysis via Topology of Interactions and Similarity SEts). I. Ulitsky and R. Shamir. BMC Systems Biology (2007)

17. Motivation Detect functional modules: groups of  interacting proteins  co-expressed genes Integrative analysis - can identify weaker signals Identifies a group of genes as well as the connections between them

18. Front vs Back nodes Only variant genes (front nodes) have meaningful similarity values These can be linked by non regulated genes (back nodes). Back nodes correspond to:  Post-translational regulation  Partially regulated pathways  Unmeasured transcripts

19. Advantages of MATISSE Works even when only a fraction of the genes expression patterns are informative No need to prespecify the number of modules

20. Network based clustering visualization Similar to clustering visualization (gene list, mean patterns, heat maps, etc.). Interactions map

21. Supervised Grouping Differential expression: t-test, SAM (Significance Analysis of Microarrays) Similarity group (correlation to one probe/gene) Rule based grouping (define a pattern)

22. Hands-on part I (1-3)

23. Input data Functional enrichment Preprocessing Promoter signals Visualization utilities Location enrichment miRNA Targets enrichment Links to public annotation databases Grouping KEGG pathway enrichment

24. Ascribe functional meaning to gene groups Gene Ontology Gene Ontology (GO) a database of functional annotations. We extract files for all supported organisms. TANGO TANGO: Apply statistical tests that seek over-represented GO functional categories in the groups. TANGO

25. Enriched GO Functional Categories Hierarchical structure → highly dependent categories. Problems:  High redundancy  Multiple testing corrections assume independent tests TANGO

26. Functional Enrichment - Visualization

27. Input data Functional enrichment Preprocessing Promoter signals Visualization utilities Location enrichment miRNA Targets enrichment Links to public annotation databases Grouping KEGG pathway enrichment

28. Inferring regulatory mechanisms from gene expression data Assumption: co-expression → transcriptional co-regulation → common cis-regulatory promoter elements Computational identification of cis-regulatory elements that are over-represented in promoters of the co-expressed gene PRIMA - PRomoter Integration in Microarray Analysis * Elkon, et. Al, Genome Research (2003)

29. PRIMA – general description Input:  Target set (e.g., co-expressed genes)  Background set (e.g., all genes on the chip) Analysis:  Identify transcription factors whose binding site signatures are enriched in the ‘Target set’ with respect to the ‘Background set’. TF binding site models – TRANSFAC DB Default: From -1000 bp to 200 bp relative the TSS

30. Promoter Analysis - Visualization

31. Input data Functional enrichment (TANGO) Normalization/ Filtering Promoter signals (PRIMA) Visualization utilities Location enrichment miRNA Targets enrichment (FAME) Links to public annotation databases Grouping (Clustering/ Biclustering/ Network based clustering)

32. miRNA Enrichment Analysis Goal: to predict micorRNAs (miRNAs) regulation by detecting miRNAs whose binding sites are over/under represented in the 3' UTRs of gene groups. FAME = Functional Assignment of MiRNAs via Enrichment

33. FAME miRNA targets Genes sharing a common function Significance of overlap Rather than using a simple hyper- geometric test, FAME also considers: The uneven distribution of 3’ UTR lengths Confidence values assigned to individual miRNA target sites (context scores)

34. FAME TargetScan predictions of miRNA targets, weighted by context scores ` Target gene set Individual miRNA miRNA Targets Degree preserving random permutations Expected weight Actual weight P-value Sum of edge weights between miRNA and the target set The same method can also be used for a group of miRNA Accounts for the distribution of 3’ UTR lengths Used to rank miRNA-target set pairs

35. Implementation in Expander Usage very similar to that of TANGO and PRIMA Currently uses TargetScan5 predictions Main parameters: –Number of random iterations (random graphs created) –Enrichment direction: over- or under- representation –Multiple testing correction –The use of the context score weights is optional

36. Input data Functional enrichment (TANGO) Normalization/ Filtering Promoter signals (PRIMA) Visualization utilities Location enrichment miRNA Targets enrichment (FAME) Links to public annotation databases Grouping (Clustering/ Biclustering/ Network based clustering)

37. Location analysis Goal: Detect genes that are located in the same area and are co-expressed. Search for over represented chromosomal areas within gene groups. Statistical test. Redundancy filter Ignoring known gene clusters

38. Location analysis visualization Enrichment analysis visualization Positions view with color assignments

39. KEGG pathway analysis Searches for KEGG pathways that are over-represented in gene groups (I.e. in a target set with respect to a background set). Uses hyper geometric test. Multiple testing correction (Bonferroni) Enrichment results visualization (same as other group analysis results).

40. Custom enrichment analysis Loads an annotation file supplied by the user (provides genes with custom annotations). Searches for annotations (features) that are over-represented in gene groups (I.e. in a target set with respect to a background set). Uses hyper geometric test. Multiple testing correction (Bonferroni) Enrichment results visualization (same as other group analysis results).

41. Hands-on part II (3-11)

43. SPIKE…

44. Expression Data – Input File probes conditions

45. ID Conversion File

46. Gene Groups File

47. Normalization: Box plots Log (Intensity) Median intensity Upper quartile Lower quartile

48. Standardization of Expression Levels After standardization Before standardization

49. Cluster Analysis: Visualization (I)

50. BeforeAfter Cluster I Cluster II Cluster III Cluster Analysis - Visualization (II)

51. Positions visualization