1. Dimension reduction : PCA and Clustering Agnieszka S. Juncker Slides: Christopher Workman and Agnieszka S. Juncker Center for Biological Sequence Analysis DTU

2. Sample Preparation Hybridization Sample Preparation Hybridization Array design Probe design Array design Probe design Question Experimental Design Question Experimental Design Buy Chip/Array Statistical Analysis Fit to Model (time series) Statistical Analysis Fit to Model (time series) Expression Index Calculation Expression Index Calculation Advanced Data Analysis ClusteringPCA Classification Promoter Analysis Meta analysisSurvival analysisRegulatory Network Advanced Data Analysis ClusteringPCA Classification Promoter Analysis Meta analysisSurvival analysisRegulatory Network Normalization Image analysis The DNA Array Analysis Pipeline Comparable Gene Expression Data Comparable Gene Expression Data

3. Motivation: Multidimensional data Pat1Pat2Pat3Pat4Pat5Pat6Pat7Pat8Pat9 209619_at7758470553427443874749337950503152937546 32541_at280387392238385329337163225288 206398_s_at1050835126817231377804184611802521512 219281_at391593298265491517334387285507 207857_at1425977202711849398146585936591318 211338_at37272838331636233130 213539_at1241974541161621139797160149 221497_x_at1208617599115808311966113 213958_at179225449174185203186185157215 210835_s_at203144197314250353173285325215 209199_s_at75812348331449769111098763811331326 217979_at57056397279686949467310136651568 201015_s_at533343325270691460563321261331 203332_s_at649354494554710455748392418505 204670_x_at5577321653234423577133744328351520723061 208788_at6483271057746541270361774590679 210784_x_at142151144173148145131146147119 204319_s_at298172200298196104144110150341 205049_s_at3294135120802066372613962244214212481974 202114_at83367473312988623718865017341409 213792_s_at646375370436738497546406376442 203932_at19771016243618561917822118910926232190 203963_at976377136857491616692 203978_at315279221260227222232141123319 203753_at146811053811154980141912535541045481 204891_s_at787115274127576615370108 209365_s_at472519365349756528637828720273 209604_s_at7727413021610831180235177191 211005_at495812970567761617572 219686_at694342345502960403535513258386 38521_at775604305563542543725587406906 217853_at36716810716028726427311389363 217028_at4926266735425163468332814822397827023977 201137_s_at4733284618345471507923303345146023173109 202284_s_at60018231657117797223031574173110472054 201999_s_at8979598008082971014998663491613 221737_at265200130245192246227228108394 205456_at636410060826553737181 201540_at8211296165185861311441549146218132112 219371_s_at147721078371534240711041688295612331313 205297_s_at4183942937784053084471005709201 208650_s_at1025455685872718884534863219846 210031_at288162205155194150185184141206 203675_at2683883182564132792392461098532 205255_x_at677308679540398447428333197417 202598_at176342298174174413352323459311 201022_s_at251193116106155285221242377217 218205_s_at102812662085179010962302192511487872700 207820_at634353971025475483075 202207_at77217241674413184748372130

4. PCA

5. Principal Component Analysis (PCA) Numerical method Dimensionality reduction technique Primarily for visualization of arrays/samples Performs a rotation of the data that maximizes the variance in the new axes Projects high dimensional data into a low dimensional sub-space (visualized in 2-3 dims) Often captures much of the total data variation in a few dimensions (< 5) Exact solutions require a fully determined system (matrix with full rank) –i.e. A “square” matrix with independent rows

6. Principal components 1 st Principal component (PC1) –Direction along which there is greatest variation 2 nd Principal component (PC2) –Direction with maximum variation left in data, orthogonal to PC1

7. Principal components General about principal components –summary variables –linear combinations of the original variables –uncorrelated with each other –capture as much of the original variance as possible

8. Principal components - Variance

9. Singular Value Decomposition

10. Requirements: –No missing values –“Centered” observations, i.e. normalize data such that each gene has mean = 0

11. PCA of ALPS patients vs. healthy controls

12. PCA of leukemia patients

13. PCA of treated cell lines 4 conditions, 3 batches

14. PCA projections (as XY-plot)

15. Eigenvectors (eigenarrays, rows)

16. PCA of cell cycle data - based on only 500 cell cycle regulated genes

17. PCA of cell cycle data

18. PCA of cell cycle data broken scanner (sample 12-16)

19. Clustering

20. Why do we cluster? Organize observed data into meaningful structures Summarize large data sets Used when we have no a priori hypotheses

21. Many types of clustering methods Method: –K-class –Hierarchical, e.g. UPGMA Agglomerative (bottom-up) Divisive (top-down) –Graph theoretic

22. Hierarchical clustering Representation of all pair-wise distances Parameters: none (distance measure) Results: –One large cluster –Hierarchical tree (dendrogram) Deterministic

23. Hierarchical clustering – UPGMA Algorithm Assign each item to its own cluster Join the nearest clusters Re-estimate the distance between clusters Repeat for 1 to n

24. Hierarchical clustering

26. Hierarchical Clustering Data with clustering order and distances Dendrogram representation 2D data is a special (simple) case!

27. Hierarchical clustering example: leukemia patients (based on all genes)

28. Hierarchical clustering example: leukemia data, significant genes

29. K-mean clustering Partition data into K clusters Parameter: Number of clusters (K) must be chosen Randomilized initialization: –different clusters each time

30. K-mean - Algorithm Assign each item a class in 1 to K (randomly) For each class 1 to K –Calculate the centroid (one of the K- means) –Calculate distance from centroid to each item Assign each item to the nearest centroid Repeat until no items are re-assigned (convergence)

31. K-mean clustering, K=3

34. Self Organizing Maps (SOM) Partitioning method (similar to the K-means method) Clusters are organized in a two-dimensional grid Size of grid must be specified –(eg. 2x2 or 3x3) SOM algorithm finds the optimal organization of data in the grid

35. SOM - example

40. K-means clustering Cell cycle data

41. K-means clustering example: Cluster profiles, treated cell lines

42. Comparison of clustering methods Hierarchical clustering –Distances between all variables –Time consuming with a large number of gene –Advantage to cluster on selected genes K-means clustering –Faster algorithm –Does only show relations between all variables SOM –Machine learning algorithm

43. Distance measures Euclidian distance Vector angle distance Pearsons distance

44. Comparison of distance measures

45. Summary Dimension reduction important to visualize data Methods: –Principal Component Analysis –Clustering Hierarchical K-means Self organizing maps (distance measure important)

46. Coffee break Next: Exercises in PCA and clustering