1. Andrey Alexeyenko andrej.alekseenko@scilifelab.se Functional analysis with pathways and networks

2. FunCoup: on-line interactome resource Andrey Alexeyenko and Erik L.L. Sonnhammer (2009) Global networks of functional coupling in eukaryotes from comprehensive data integration. Genome Research. http://FunCoup.sbc.su.se

3. Data integration in predicted networks

5. Species-specific networks: evolutionary conservation Mouse vs. human

6. Cancer-specific networks: links inferred from expression, methylation, mutations Functional coupling transcription  transcription transcription  methylation methylation  methylation mutation  methylation mutation  transcription mutation  mutation + mutated gene State-of-the-art method to beat: Reverse engeneering froma single source (usually transcriptome)

7. Do gene networks tell the story? Yellow diamonds : somatic mutations in prostate cancer Pink crosses: also mutated in glioblastome (TCGA) State-of-the-art method to beat: Frequency analysis of somatic mutations

8. Network analysis made statistically sound: compared to a reference and quantified N links_real = 12 N links_expected = 4.65 Standard deviation = 1.84 Z = (N links_observed – N links_expected ) / SD = 3.98 P-value = 0.0000344 FDR < 0.1 Actual network: observed pattern A random pattern Question: Is ANXA2 related to TGFbeta signaling?

9. Network enrichment analysis: what is the reference? We refer to a randomized network: preserves topology, lost biology Link swapping ( Perl ): (original algorithm by Maslov & Sneppen, 2003) – work with Simon Merid. De novo network generation ( C++ ): work with T. McCormack, O. Frings, E. Sonnhammer. Matrix permutation ( R ): work with Woojoo Lee, Yudi Pawitan.

10. Biological analysis of differential expression Functional set ? Our alternative: Network enrichment analysis Altered genes State-of-the-art method to beat: Gene set enrichment analysis

11. Network enrichment analysis: applications N links_real = 6 N links_expected = 1.00 Standard deviation = 1.25 Z = 3.97 P-value = 0.0000356 Question: Does gene expression in MAT230414 relate to “response to tumor cell”? Pathway characterizationDetection of driver mutationsCoherence of genome alterations N links_real = 55 N links_expected = 37.05 Standard deviation = 3.59 Z = 3.59 P-value = 0.00016 Question: Could copy number alteration in EHR in HOU501106 lead to changes of its transcriptome and proteome? N links_real = 0 N links_expected = 1.05 Standard deviation = 0.80 Z = -1.31 P-value = 0.905 Question: Are CNA in HOU501106 coherent? State-of-the-art method to beat: Observational science

12. Analysis of cancer-specific wiring Pathway network of normal vs. tumor tissues Edges connect pathways given a higher ( N>9; p 0 0.5 ) between them (seen as edge labels). Known pathways (circles) are classified as: signaling, metabolic, cancer, other disease. Blue lines: evidence from mRNA co- expression under normal conditions + ALL human & mouse data. Red lines: evidence from mRNA co- expression in expO tumor samples + ALL human data + mouse PPI. Node s i z e : number of pathway members in the network. Edge opacity : p 0. Edge t hi ck ne ss : number of gene- gene links.

13. Arrow of time: network prospective (work with J. Meyer, Duke Univ.) Alexeyenko et al. Dynamic zebrafish interactome reveals transcriptional mechanisms of dioxin toxicity PLoS One. 2010 Question: Routes of embryonic intoxication in fish? Answer: through cytochrome P4501A, calcium and iron metabolism, neuronal and retinal development

14. Genetic association of sequence variants near AGER/NOTCH4 and dementia. Bennet AM, Reynolds CA, Eriksson UK, Hong MG, Blennow K, Gatz M, Alexeyenko A, Pedersen NL, Prince JA. J Alzheimers Dis. 2011;24(3):475-84. Genome-wide pathway analysis implicates intracellular transmembrane protein transport in Alzheimer disease. Hong MG, Alexeyenko A, Lambert JC, Amouyel P, Prince JA. J Hum Genet. 2010 Oct;55(10):707-9. Epub 2010 Jul 29. Analysis of lipid pathway genes indicates association of sequence variation near SREBF1/TOM1L2/ATPAF2 with dementia risk. Reynolds CA, Hong MG, Eriksson UK, Blennow K, Wiklund F, Johansson B, Malmberg B, Berg S, Alexeyenko A, Grönberg H, Gatz M, Pedersen NL, Prince JA. Hum Mol Genet. 2010 May 15;19(10):2068-78. Epub 2010 Feb 18. Validation of candidate disease genes (work with Jonathan Prince, MEB, KI) Question: Is there extra evidence for GWAS- candidates to be involved? Answer: Yes, for some…

15. Mutations accumulated in somatic genomes of cancer cell lines (work with Pelin Akan, Joakim Lundeberg) Question: Do mutations within a cancer genome behave like a quasi-pathway? Answer: Yes. Question: How similar are mutation patterns in different cancers? Answer: A lot, but only for drivers and at the pathway level.

16. Pathway view on the set of toxicity-associated alleles The analysis detected more significantly enriched pathways than for the negative control gene sets of the same size (215 vs. 139; p0 < 0.001; FDR<0.05). More specifically, many thus found pathways were associated with cancer, apoptosis, cell division etc. Red node : list of top 50 genes with most contrast allele patterns Grey node : negative control list Yellow : enriched/depleted pathways Edge width : no. of gene- gene links in the network Edge opacity : confidence Green edges : enrichment Red edges : depletion Edges produced by less than 3 list genes are not shown

17. Experimental perturbations of syndecan-1 in cancer cells (work with T. Szatmari, K. Dobra, KI Huddinge) Lines: Red: depletion Blue: enrichment Question: Second-order downstream targets of syndecan modulation? Answer: Segments of cell cycle etc.

18. Network analysis: what’s in it for you? Prioritization among multiple “technical positives” Generalization “genes => pathways”, hence new, lower, dimension Analysis of individual genes “as if” pathways Testing biological hypotheses, validation Modular part of high-throughput analytic pipelines

19. Network analysis: how to succeed? Analyze prioritized candidates (from genotyping, DE, GWAS…) rather than any genes. Do not lean on single “interesting” network links. Employ statistics! i.e. “concrete questions” => “testable hypotheses” => “concrete answers” The amount of information in known gene networks is enormous. Let’s just use it!

20. Acknowledgements: Erik Sonnhammer’s bioinformatics group KICancer http://FunCoup.sbc.su.se Erik Sonnhammer, Thomas Schmitt, Oliver Frings, Andreas Tjärnberg, Dimitri Guala, Mun-Gwan Hong, Jonathan Prince, Rochellys Diaz Heijtz, Angelo de Milito, Meng Chen, Simin Merid, Yudi Pawitan, Woojoo Lee, Erik Aurell, Joakim Lundeberg, Pelin Akan, Joel Meyer, Katalin Dobra, Tunde Szatmari, Serhiy Souchelnytskyi, Ingemar Ernberg,

21. Gene network discovery: getting rid of spurious links 0.7 0.5 0.4 Data processing inequality: “Direct links convey more information than indirect ones” PCIT algorithm: Partial Correlation & Information Theory