MFMS: Maximal Frequent Module Set mining from multiple human gene expression datasets

Transcription

1 MFMS: Maximal Frequent Module Set mining from multiple human gene expression datasets Saeed Salem North Dakota State University Cagri Ozcaglar Amazon 8/11/2013

2 Introduction Gene expression analysis Use gene profile similarity for functional annotation of unknown genes Reduced to frequent subgraph mining problem Limits: Some genes with similar profiles do not have the same function Similarity in profiles due to perturbation of multiple biological pathways Solution: Analysis of multiple gene expression datasets from multiple experiments Sets of genes with similar expression profiles in a significant number of experiments Simultaneous analysis of multiple gene expression datasets 8/11/2013 MFMS: BioKDD

3 Motivation: Motivation and Goal Some genes with similar profiles do not have the same function Similar profiles due to perturbation of multiple biological pathways Goal: Analysis of multiple gene expression datasets from multiple experiments Sets of genes with similar expression profiles in a significant number of experiments Simultaneous analysis of multiple gene expression datasets 8/11/2013 MFMS: BioKDD

4 Outline 1. Introduction and motivation 2. Background: Gene expression analysis 3. MFMS algorithm 4. Results 5. Conclusion 8/11/2013 MFMS: BioKDD

5 Basics: Gene expression data Background Coexpression graph: G=(V,E) V: Genes E: Link between two genes if their expression profiles are correlated Edges can be weighted or unweighted (edges with small weights are pruned) Multiple gene expression datasets 8/11/2013 MFMS: BioKDD

6 Background Mining Frequent and Dense Subgraphs MULE: [Koyuturk et al., Bioinformatics, 2004] Efficient enumeration approach for mining frequent subgraphs from a set of graphs representing PPI networks for several species. Crochet: [Pei et al., KDD, 2005] Mines cross-all-graphs quasi-cliques Subgraphs which meet a density constraint and appear in all graphs. Crochet+: [Jiang et al., ACM TKDD, 2009] Same as Crochet, except that the density constraint is relaxed. The dense subgraphs appear in a subset of the graphs. 8/11/2013 MFMS: BioKDD

7 Background Gene expression analysis algorithms [Lee et al., Genome Research, 2004]: Summary graph with edges that occur in at least a number of graphs After building the summary graph, uses module discovery method (MCODE) to extract modules. CLOSECUT, SPLAT: [Yan et al., KDD, 2005] Mines relation graphs with connectivity constraints Input is multiple gene expression datasets, and output is a summarized set of frequent patterns CODENSE: [Hu et al., Bioinformatics, 2005] First step: Finds highly connected subgraphs from aggregate graph. Second step: Cluster the edges in the extracted subgraphs. [Huang et al., Bioinformatics, 2007] Builds a summary graph and represent it as a binary matrix of the form: Gene link x Graphs Biclustering on this matrix returns candidate frequent edgesets Connected components of each bicluster are returned as frequent patterns 8/11/2013 MFMS: BioKDD

8 Algorithm: MFMS MFMS: Algorithm for mining maximal frequent collections of: K-cliques Percolated k-cliques from graph representations of multiple gene expression datasets Edge mining problem Two steps: 1) Build the summary graph and generate the edge-attibuted matrix 2) Find MFMSs from the biclusters obtained from edge-attributed matrix: 2.1) Bicluster Edge-attributed matrix using GenMax algorithm [Gouda and Zaki, IEEE TKDD 2005] 2.2) For each bicluster, find highly connected components: k-cliques or k-percolated cliques Next: MFMS basic definitions 8/11/2013 MFMS: BioKDD

9 MFMS: Preliminaries Basic definitions and preliminaries: 8/11/2013 MFMS: BioKDD

10 Basic definitions and preliminaries: MFMS: Preliminaries In the edge attribute matrix, edges correspond to items and graph layers correspond to transactions. 8/11/2013 MFMS: BioKDD

11 FMS: Frequent Module Set: MFMS: Definitions Frequent Module Sets 8/11/2013 MFMS: BioKDD

12 Definitions: MFMS: Problem statement MFMS: Maximal Frequent Module Set MFMS problem statement: 8/11/2013 MFMS: BioKDD

13 MFMS: Flow diagram 8/11/2013 MFMS: BioKDD

14 MFMS: Algorithm 8/11/2013 MFMS: BioKDD

15 Results Human Gene Expression dataset: 52 Affymetrix microarray datasets [Huang et al., Bioinformatics, 2007] Summary graph: 9874 nodes, 49,817,037 edges, each appears in at least 1 graph. Many of these edges and genes appear in one or two datasets. Pruned edges that occur in < 7 graphs. Edge-attributed graph: Summary graph: 9784 nodes, edges Edge-attributed matrix: edges * 52 graphs 8/11/2013 MFMS: BioKDD

16 Results: Structural Topology analysis k-cliques An example of a maximal frequent edgesets and its induced subgraph and the associated collection of modules. Collection of 3-cliques Edge-induced subgraph Maximal Frequent edgeset RE = E in the edge-induced subgraph / E in the summary graph = 8 / 13 KRE = E in the collection / E in the summary graph = 6 /13 8/11/2013 MFMS: BioKDD

17 Results: Structural Topology analysis k-cliques An example of a maximal frequent edgesets and its induced subgraph and the associated collection of modules. Edge-attributed matrix Edge-induced subgraph Collection of 4-cliques 8/11/2013 MFMS: BioKDD

18 Results: Structural Topology analysis k-cliques k-cliques connected 8/11/2013 MFMS: BioKDD

19 Results: Structural Topology analysis percolated k-cliques Percolated k-cliques connected 8/11/2013 MFMS: BioKDD

20 Results: GO enrichment analysis GO enrichment analysis of human gene expression datasets Go-Miner tool with FDR-corrected p-values of ER: Percentage of modules enriched by at least one GO term. Module sets with complete gene symbol mapping only. Ratio of GO-enriched collections increase as α inreases. 8/11/2013 MFMS: BioKDD

21 Results: GO enrichment analysis Largest of these collections 4-cliques with 30 genes enriched with 111 GO terms 8/11/2013 MFMS: BioKDD

22 Results: GO enrichment analysis Top 20 GO terms enriched by 30 genes Top 4 GO terms: Mitotic phase of cell cycle GO: (M phase) GO: (mitotic cell cycle) GO: (mitosis) GO: (M phase of mitosis) Others include: GO: (DNA replication) GO: (Negative regulation cc) GO: (Cell division) 8/11/2013 MFMS: BioKDD

23 MFMS: Conclusion Two-step algorithm for mining collections of highly connected subnetworks from multiple gene expression datasets The first step is to construct edge-attributed graph, the second step is to find k-cliques and k-percolated cliques among the bicluster found on edge-attributed matrix. Experiments on Human gene expression dataset: MFMS on multiple Human gene expression datasets: Resulting gene sets are GO-enriched collections of genes: Gene sets are biologically significant. Gene sets are more likely to be independent of the conditions of microarray experiments, due to the support threshold used in MFMS. 8/11/2013 MFMS: BioKDD

24 Future work MFMS on other sets of multiple gene expression datasets: Yeast cell cycle dataset Time-course gene expression datasets Compare MFMS experimentally with existing algorithms: Molecular Hypergraphs [Rahman et al., ACM BCB 2012] CODENSE [Hu et al., Bioinformatics, 2005] ExpressCoExpress: Gene expression analysis using AWS A platform for applying various biclustering algorithms on gene expression datasets in the cloud 8/11/2013 MFMS: BioKDD

25 Thank you 8/11/2013 MFMS: BioKDD