1. Introduction to DNA Microarray Technology Steen Knudsen Uma Chandran

2. MBG404 Overview Data Generation Processing Storage Mining Pipelining Microarray

3. DNA Microarrays consist of 100 - 1 million DNA probes attached to a surface of 1 cm by 1 cm (chip). By hybridisation, they can detect DNA or RNA: If the hybridised DNA or RNA is labelled fluorescently it can be quantified by scanning of the chip.

4. DNA microarrays can be manufactured by: Photolitography (Affymetrix, Febit, Nimblegen) Inkjet (Agilent, Canon) Robot spotting (many providers)

5. Affymetrix photolitography Each probe 25 bp long 22-40 probes per gene Perfect Match (PM) as well as MisMatch (MM) probes

6. Febit/NimbleGen photolitography

7. Robot Spotting

8. InkJet (HP/Canon) technology

9. Summary

10. Image Analysis 1.Gridding: identify spots (automatic, semiautomatic, manual) 2.Segmentation: separate spots from background. Fixed circle (B), Adaptive circle C, Adaptive shape (D), Histogram 3.Intensity extraction: mean or median of pixels in spot 4.Background correction: local or global

11. Microarray analysis – Data Preprocessing Objective –Convert image of thousands of signals to a a signal value for each gene or probe set Multiple step –Image analysis –Background and noise subtraction –Normalization –Expression value for a gene or probe set Image analysis and bkg, noise usually done by proprietary software Gene 1100 Gene 2150 Gene 375. Gene10000500

12. Normalization Corrects for variation in hybridization etc Assumption that no global change in gene expression Without normalization –Intensity value for gene will be lower on Chip B –Many genes will appear to be downregulated when in reality they are not Gene 1 100 Gene 2 150 Gene 3 75. Gene10000 500 50 75 32 250 TreatedControl

13. Data Analysis Part 2- Data analysis –Class discovery –Class comparison –Class prediction –Biological annotation –Pathway analysis

14. Class Discovery Objective? –Can data tell us which classes are similar? –Are there subgroups? –Do T-ALL, T-LL, B-ALL fall into distinct groups? Methods –Hierarchical clustering –K-means, SOM etc –These are Unsupervised Methods Class Ids are not known to the algorithm –For example, does not know which one is cancer or non cancer –Do the expression values differentiate, does it discover new classes

15. Hierarchical Clustering Eisen Cluster and Treeview Import data Filter –Filter or not to filter, %P calls, SD etc Accept filter Adjust data –Log transform (important), center, normalize Clustering –Cluster array or genes –Gene computationally intensive –Choose distance metric.cdt file created –Open with Treeview

16. Experimental Design – Very important!!! Sample size –How many samples in test and control Will depend on many factors such as whether tissue culture or tissue sample Power analysis Replicates –Technical v biological Biological replicates is more important for more heterogenous samples Need replicates for statistical analysis To pool or not to pool –Depends on objective Sample acquistion or extraction –Laser captered or gross dissected All experimental steps from sample acquisition to hybridization –Microarray experiments are very expensive. So, plan experiments carefully

17. Venn Diagram http://www.pangloss.com/seidel/Protocols/venn.cgi http://ncrr.pnl.gov/software/VennDiagramPlotter.stm

18. Conclusion Other analysis –Class prediction –Gene list from class comparison can be used in pathway analysis –HSLS pathway workshops on Ingenuity, DAVID, Pathway Architect –Future: Integrate expression data with other data such as snp or microRNA GEO has some data analysis features

19. End Theory I 5 min Mindmapping 10 min Break

20. Theory II

21. Microarrays Gene Expression: –We see difference between cells because of differential gene expression, –Gene is expressed by transcribing DNA intosingle-stranded mRNA, –mRNA is later translated into a protein, –Microarrays measure the level of mRNA expression

22. Microarrays Gene Expression: –mRNA expression represents dynamic aspects of cell, –mRNA is isolated and labeled using a fluorescent material, –mRNA is hybridized to the target; level of hybridization corresponds to light emission which is measured with a laser

23. Microarrays

26. Processing Microarray Data Differentiating gene expression: –R = G  not differentiated –R > G  up-regulated –R < G  down regulated

27. Processing Microarray Data Problems: –Extract data from microarrays, –Analyze the meaning of the multiple arrays.

28. Processing Microarray Data

29. Problems: –Extract data from microarrays, –Analyze the meaning of the multiple arrays.

30. Processing Microarray Data Microarray data:

31. Processing Microarray Data Clustering: –Find classes in the data, –Identify new classes, –Identify gene correlations, –Methods: K-means clustering, Hierarchical clustering, Self Organizing Maps (SOM)

32. Processing Microarray Data Distance Measures: –Euclidean Distance: –Manhattan Distance:

33. Processing Microarray Data K-means Clustering: –Break the data into K clusters, –Start with random partitioning, –Improve it by iterating.

34. Processing Microarray Data Agglomerative Hierarchical Clustering:

35. Processing Microarray Data Self-Organizing Feature Maps: –by Teuvo Kohonen, –a data visualization technique which helps to understand high dimensional data by reducing the dimensions of data to a map.

36. Processing Microarray Data Self-Organizing Feature Maps: –humans simply cannot visualize high dimensional data as is, –SOM help us understand this high dimensional data.

37. Processing Microarray Data Self-Organizing Feature Maps: –Based on competitive learning, –SOM helps us by producing a map of usually 1 or 2 dimensions, –SOM plot the similarities of the data by grouping –similar data items together.

38. Processing Microarray Data Self-Organizing Feature Maps:

39. Processing Microarray Data Self-Organizing Feature Maps: Input vector, synaptic weight vector x = [x 1, x 2, …, x m ] T w j =[w j1, w j2, …, w jm ] T, j = 1, 2,3, l Best matching, winning neuron i(x) = arg min ||x-w j ||, j =1,2,3,..,l Weights w i are updated.

40. Figure 2. Output map containing the distributions of genes from the alpha30 database. Chavez-Alvarez R, Chavoya A, Mendez-Vazquez A (2014) Discovery of Possible Gene Relationships through the Application of Self-Organizing Maps to DNA Microarray Databases. PLoS ONE 9(4): e93233. doi:10.1371/journal.pone.0093233 http://www.plosone.org/article/info:doi/10.1371/journal.pone.0093233

41. Figure 5. Color-coded output maps representing the final weight of neurons from the samples of the alpha30 database. Chavez-Alvarez R, Chavoya A, Mendez-Vazquez A (2014) Discovery of Possible Gene Relationships through the Application of Self-Organizing Maps to DNA Microarray Databases. PLoS ONE 9(4): e93233. doi:10.1371/journal.pone.0093233 http://www.plosone.org/article/info:doi/10.1371/journal.pone.0093233

42. End Theory II 5 min mindmapping 10 min break

43. Practice I

44. Microarray http://www.biolab.si/supp/bi- visprog/dicty/dictyExample.htmhttp://www.biolab.si/supp/bi- visprog/dicty/dictyExample.htm Use the data as described on the page