Lent 2010 MPhil course \\\'Network biology\\\' - lecture 2

1. Exploratory analysis of phenotyping screens: enrichment, clustering, ranking Network Biology - lecture 2

2. High-dimensional phenotypes by microscopy or molecular profiling Low-dimensional phenotypes A- Time Size

3. A challenge for computation and statistics

5. High-throughput phenotyping Weak Strong Phenotypes of 100s to 10.000s perturbed genes Hits

6. Gene Ontology (GO) www.geneontology.org A GO Term with a gene set annotated to it

7. GO over-representation Hyper-geometric test Hits Weak Strong Phenotype GO All genes All Hits Hits in GO term Genes in GO term

8. Hyper-geometric distribution N genes altogether n hits k hits in GO term m genes in GO term Probability to observe k hits in GO term Number of possibilities to choose n hits out of N genes k hits fall into the GO term of size m The other n-k hits are all genes outside the GO term

9. Hyper-geometric test: example pvalue = phyper ( k , m , N-m , n , lower.tail = FALSE ) Hold these fixed : N = 10 000 m = 200 See what happens if we vary: k = 1,2,…, 30 n = 50, 100, 200, 300, 400 k or more! N n k m Number k of hits in GO term p-value [log10] 50 100 200 300 400

10. Gene Set Enrichment Analysis Not restricted to GO , could be any collection of gene sets, e.g. MSigDB at http://www.broad.mit.edu/gsea/ Subramanian et al. (2005) Weak Strong Phenotype GO Weak Strong Phenotype No significant trend Significant trend

12. GSEA: construction Weak Strong Phenotype Nr. non- hits before i Nr. all non- hits i Nr. hits before i Nr. all hits if p = 0

13. GSEA: examples

15. Map phenotypes to network Where do the hits fall in the network and what are they connected to?

16. Sub-networks rich in hits

17. Sub-networks with highly correlated phenotypes

18. Predicting phenotypes Guilt by association Use known phenotypes in the network Use edge weights if possible Success depends on quality and coverage of linkage in the network ? ? ?

19. Which networks? Networks from large-scale experiments Networks from analyzing the experimental literature Networks from probabilistic data integration

20. Cluster first, think later Perturbed Genes Phenotypic profiles Features = Expression profiles, parameters of cell shape, protein concentration or localization Genes with similar phenotypic profiles often have similar molecular functions or act in the same pathways. Principle for function prediction: Guilt by association

21. From data to distances Phenotypic Profiles Perturbed genes D [i,j] = dist( M [i,] , M [j,] ) M D D [j,i] = D [i,j] D [i,i] = 0 for all i dist(. , .) What distance measure should we use? Distance or dissimilarity matrix i j i j j i

22. Examples of distances how is this related to correlation? a b Euclidean distance dist( a,b ) = a b dist( a,b ) = Manhattan distance  a b dist( a,b ) = Cosine distance

23. Linkage: distances to clusters dist(C1, C2) = max { dist( i , j ) : i in C1, j in C2 } complete linkage = min { dist( i , j ) : i in C1, j in C2 } single linkage = mean { dist( i , j ) : i in C1, j in C2 } average linkage D Phenotype 2 Phenotype 1 1 2 3 6 5 4 D[3,4] 1 2 3 6 5 4 Phenotype 2 Phenotype 1 D[ (2,3) ,4] = ??? Distances between individual genes 3

24. Hierarchical clustering Ingredients: data matrix , distance function, linkage function Phenotype 2 Phenotype 1 1 2 3 6 5 4 1 2 3 6 5 4 Dendrogram

25. Phenotypic Data 332 knock-downs in 5 conditions Correlation matrix Dendrogram Data by Klaas Mulder, CRI

32. Network Biology - lecture 2 ≥ 3 Questions !