Discovering modules in expression profiles using a network

Transcription

1 Discovering modules in expression profiles using a network Igor Ulitsky 1

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3 Protein-protein interactions (PPIs) Low throughput measurements: accurate, scarce High throughput: more abundant, noisy Large, readily available resource 3

4 In fact, many resources 4

5 The hairball syndrome 5

6 Goal Challenge: Detect active functional modules: connected subnetwork of proteins whose genes are co-expressed Where is the action in the network in a particular experiment? 6

7 Ron Shamir, ABDBM RNA Antalia, Ron Shamir April 08 7

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9 I. Ulitsky R. Shamir BMC Systems Biology 07 Input: Expression data and a PPI network Output: a collection of modules Connected PPI subnetworks Correlated expression profiles Module Analysis via Topology of Interactions and Similarity SEts Interaction High expression similarity 10

10 Probabilistic model Event M ij : i,j are mates = highly co-expressed P(S ij M ij ) ~ N(µ m, σ 2 m) P(S ij M ij ) ~ N(µ n, σ 2 n) H 0 : U is a set of unrelated genes H 1 : U is a module = connected subnetwork with high internal similarity R i : gene i transcriptionally regulated β m : fraction of mates out of gene pairs that are in a module and transcriptionally regulated β m =P(M ij R i R j, H 1 ) p m : fraction of mates out of all gene pairs that are transcriptionally regulated 11

11 Probabilistic model (2) Is gene set U a module? Assuming pair indep: Define γ m ij= β m P(R i )P(R j ) Define γ n ij= p m P(R i )P(R j ). Likelihood ratio Pr(Data H 1 )/Pr(Data H 0 ) Taking log: sum of terms ij: 12

12 Probabilistic model Similarities: mixture of two Gaussians For a candidate group U, the likelihood ratio of originating from a module or from the background is W U PS ( ) PS ( ij H ) U U HM M = log = log = PS ( H ) PS ( H ) U U N (, i j) U U ij N (, i j) U U w ij Module score = Gene group likelihood ratio = sum over all the gene pairs Find connected subgraphs U with high W U 13

13 Complexity Finding heaviest connected subgraph: NP hard even without connectivity constraints (+/- edge weights) Devised a heuristic for the problem 14 14

14 MATISSE workflow All the variations follow: Seed generation Greedy optimization Significance filtering 15

15 Finding seeds Three seeding alternatives tested All alternatives build a seed and delete it from the network Building small seeds around single nodes: Best neighbors All neighbors Approximating the heaviest subgraph Delete low-degree nodes and record the heaviest subnetwork found 16

16 Greedy optimization Simultaneous optimization of all the seeds Keeps modules non-overlapping Keeps upper bound on module size The following steps are considered: Node addition Node removal Assignment change Module merge 17

17 Front vs. Back nodes Only a fraction of the genes (front nodes) have meaningful similarity values MATISSE can link them using other genes (back nodes). Back nodes correspond to: Unmeasured transcripts Post-translational regulation Partially regulated pathways 18

18 Test case: Yeast osmotic shock Network: 65,990 PPIs & PDIs among 6,246 genes Expression: 133 experimental conditions response of perturbed strains to osmotic shock (O Rourke & Herskowitz 04) Front nodes: 2,000 genes with the highest variance 19

19 Pheromone response subnetwork Back Front 20

20 Performance comparison % of modules with category enrichment at p< 10-3 % annotations enriched at p<10-3 in modules Co-clustering: Hanisch et al. Bioinfo 02 HC on combined distance matrix of network, similarity distance 21

21 GO and promoter analysis Subnetwork Size Front Enriched GO terms P-value TFs P-Value processing of 20S pre-rrna < Fhl1 4.82E-16 rrna processing < Rap1 2.89E-11 35S primary transcript processing < Sfp1 2.98E-08 ribosomal large subunit assembly and maintenan rrna modification < ribosome biogenesis translational elongation < Fhl1 1.03E processing of 20S pre-rrna < rrna processing S primary transcript processing ribosomal large subunit assembly and maintenan ribosomal large subunit biogenesis < signal transduction during filamentous growth 0.01 Ste E-13 conjugation with cellular fusion < Dig1 5.41E transcription from RNA polymerase III promoter < transcription from RNA polymerase I promoter ergosterol biosynthesis < hexose transport chromatin remodeling pseudohyphal growth 0.01 Msn2 3.17E-04 response to stress < Msn4 1.82E ubiquitin-dependent protein catabolism nuclear mrna splicing, via spliceosome < ubiquitin-dependent protein catabolism < Rpn4 6.44E response to stress < Msn4 1.74E-03 mitochondrial electron transport < nuclear mrna splicing, via spliceosome ABDBM 20 Ron 46Shamir 35 pyridoxine metabolism

22 F. Müller, L. Laurent, D. Kostka, I. Ulitsky, R. Williams, C. Lu, I. Park, M. Rao, R. Shamir, P. Schwartz, N. Schmidt, J. Loring Nature 08 Application to stem cells ~150 human stem cell lines of diverse types profiled using microarrays Clustered profiles into groups Adjusted Matisse to seek subnetworks that characteristic to each group Focused analysis on pluripotent stem cells 23

23 Understanding differences between stem cells >200 human stem cell lines of diverse types profiled using microarrays Clustered into groups with similar profiles (some surprises) Focused analysis on pluripotent stem cells 24 Müller, Laurent, Kostka, Ulitsky et al. Nature 08

24 Pluripotent stem cells network Highlights the key protein machinery underlying pluripotency 25

25 Accounting for PPI confidence PPI-based analysis is made difficult by abundant false positive / negative interactions Various methods can assign confidence (probability) to individual edges Idea: seek modules that are connected with high probability Ron Shamir GE 26 Ulitsky & Shamir Bioinformatics, 2009

26 CEZANNE: (Co-Expression Zone ANalysis using NEtworks) Edge probability p(e) Edge weight log(1-p(e)) For any W U, 1 edge connects W with U\W with probability q (e.g. 0.95) The weight of the minimum cut of U is at least -log(1-q) Idea: among the subnets whose minimum cut exceeds -log(1-q) find the one with the maximum co-expression score P({A},{B,C,D})=1-0.3*0.3=0.91 Ron Shamir GE A B P({A,C,D},{B})=0.94 P({A,B,D},{C})=0.994 C 0.9 D P({A,B},{C,D})=

27 How to find confidently connected modules? Seed identification: Run MATISSE ignoring edge weights, then slice the modules using minimum cut, until all subnetworks are legal Greedy optimization (how to find legal moves?): Adding nodes is easy to test (positive edge weights) Merging modules is easy to test (Re)moving nodes: requires maintaining the set of crucial nodes in each module Solvable in minutes on real world examples Ron Shamir GE 30

28 DNA damage response in S. cerevisiae 47 DNA Damage Response expression profiles (Gasch et al., 01) Front nodes: 2,074 genes with at least two-fold expression change Network and confidence values: purification enrichment (PE) scores (Collins et al. 07) Ron Shamir GE 31

29 DNA damage response modules Module size GO biological process p-value GO-slim protein complexes p-value ribosome biogenesis and assembly ribosome translation eukaryotic 43S preinitiation complex rrna processing small nucleolar ribonucleoprotein complex S primary transcript processing Cytoplasmic ribosome biogenesis DNA-directed RNA polymerase III complex ribosome assembly exosome (RNase complex) ribosomal Suggests large subunit biogenesis SWS2 a novel DNA-directed RNA polymerase I complex rrna Novel modification pathway enriched Noc complex protein catabolism member proteasome complex (sensu Eukaryota) proteolysis with actin-localized Proteasome proteasome core complex (sensu Eukaryota) ubiquitin cycle histone proteins; acetylation Supported in histone acetyltransferase complex chromatin modification transcription other from datasets; RNA polymerase Similar II promoter translation Mitochondrial ribosome small ribosome subunit nuclear mrna splicing, via spliceosome deletion phenotypes -21 spliceosome complex Spliceosome small nuclear ribonucleoprotein complex barbed-end actin filament capping F-actin capping protein complex endocytosis Novel actin-localized pathway? cytoskeleton organization and biogenesis establishment and/or maintenance of chromatin architecture chromatin remodeling complex glycogen metabolism protein phosphatase type 1 complex sporulation (sensu Fungi) translation ribosome Mitochondrial ribosome large subunit trna processing ribonuclease P complex rrna processing Ribonuclease P 4 trehalose biosynthesis alpha,alpha-trehalose-phosphate synthase complex Trehalose biosynthesis (UDP-forming) ubiquitin-dependent protein catabolism pseudohyphal growth PKA camp-dependent protein kinase complex proteasome assembly protein folding Hsp Ron Shamir GE 32

30 Comparison with prior work Combined measure of sensitivity (% of annotations enriched) and specificity (% of modules enriched) with p<0.001 Expression similarity + confident network connectivity Ron Shamir GE Expression similarity + network connectivity Clustering of only expression data Clustering expression & network (Hanisch et al., 2002) 33

31 RECAP Algorithms using co-expression + networks to detect functionally coherent modules Key paradigm: connectivity Accommodate both sparse and dense subnetworks Subnetworks linked to osmotic shock and DNA damage in yeast A general framework for confident connectivity in PPI networks 35

32 A. Maron, R. Sharan Bioinformatics 03 A. Maron-Katz et al. BMC Bioinformatics 05 Ulitsky et al. Nature Protocols 10 EXPression ANalyzer and DisplayER Clustering Identify clusters of co-expressed genes CLICK, KMeans, SOM, hierarchical Function. enrichment GO, TANGO Promoter analysis Analyze TF binding sites of coregulated genes PRIMA Network analysis: MATISSE Biclustering Identify homogeneous submatrices SAMBA microrna function inference: FAME Ron Shamir ABDBM GE Ron Shamir 36