How to analyse microarray data

5. How Does it Work ? - (cont.) The array is scanned to measure fluorescent label. The location of the bound sample is detected using the fluorescent reporter, and gene expression is determined. Non-hybridized DNA hybridized DNA Detection of labeled (hybridized) DNA fragments

11. Data Matrix … 0.71 0.15 0.62 0.83 0.34 goo Gene 10 0.81 0.54 0.35 0.51 0.72 gree Gene 9 0.00 0.22 0.76 0.95 0.91 glas Gene 8 0.89 0.93 0.47 0.49 0.28 glee Gene 7 0.05 0.30 0.49 0.18 0.07 gar Gene 6 0.86 0.84 0.42 0.84 0.44 groo Gene 5 0.41 0.21 0.11 0.26 0.60 bas Gene 4 0.56 0.10 0.48 0.60 0.99 blee Gene 3 0.09 0.67 0.06 0.92 0.73 bar Gene 2 0.50 0.87 0.32 0.22 0.78 foo Gene 1 Type III Type II Type III Type II Type I Exp. 5 Exp. 4 Exp. 3 Exp. 2 Exp. 1

15. Data Normalisation

16. Data Normalisation

24. Minkowski distance If q = 1 , d is Manhattan distance (semi-metric distance) If q = 2 , d is Euclidean distance (metric distance) Distance Metrics

25. Distance Metrics Pearson correlation coefficient (semi-metric distance) -1 <= d(i,j) <= +1 x 1 x 2

26. Distance Metrics Rank-ordered Pearson correlation coefficient -> Spearman (semi-metric distance)

27. Distance Metrics Other variations of Pearsons correlation coefficient: Uncentered Pearson correlation (semi-metric distance)

29. Clustering Techniques Hierarchical clustering:

30. Clustering Techniques Hierarchical clustering: phylogenetic tree 5 10 15 20 3 4 2 1 5 Euclidean distance

33. Clustering Techniques k-means clustering (continued): A reasonable number of cluster centers k can be estimated: k weighted squared distance of data points to their cluster center

34. Clustering Techniques k-means clustering (continued): The initial position of cluster centers can be estimated by the distribution of the data vectors: x 1 x 2

37. Visualisation Visualisation in clusters: 5 10 15 20 3 4 2 1 5 Euclidean distance

42. www. ebi .ac. uk/microarray/biology_intro . htm When the array surface is scanned with a laser, fluorescent labels attached to the complementary DNA reveal which probes are bound and their quantity. Gene Expression Matrix

43. What is functional genomics ? Microarray landmarks. Basic principles. Applications of DNA microarrays. Working with microarrays. Clustering analysis, example. Conclusions. Outline: Microarrays (“DNA Chips”) Clustering analysis

44. A a powerful set of tools which partition samples into well-separated and homogeneous groups, based on their behaviors or patterns. Example: cluster genes that have common expression patterns in certain experimental conditions. Analyze the vast amount of data in gene expression matrices, and discover meaningful common biological functions, regulatory elements and relationships among genes. Clustering - Algorithmic Challenge

45. How will you cluster the faces ? (“raw” state cases). http://149.170.199.144/multivar/ca. htm Cluster analysis is the tool used to group these faces in an objective manner. Clustering Methods The same data could be presented in numeric format: 0/1. 1 2 3 4 5 6 7 8 9 10 11 12

47. Simultaneous (Traditional global correlation) Mark Gerstein, Yale University, 2002. Clustering Algorithm- Identifies further (reasonable) types of expression relationships Inverted Time-shifted

50. Future Model for Cancer Treatment A patient arrives with any kind of cancer… “ Personal” drug cocktail will be formulated for the specific tumor type according to individual expression pattern. A biopsy of the tumor will be taken and undergo micro-array analysis for expression pattern.

52. Micro-Array Market <$50 million >$500 million Average annual rate of growth of 150%.