Transcript Insulin.ppt
Expression Modules Brian S. Yandell (with slides from Steve Horvath, UCLA, and Mark Keller, UW-Madison) Weighted models for insulin Detected by scanone # transcripts that match weighted insulin model in each of 4 tissues: Detected by Ping’s multiQTL model tissue # transcripts Islet 1984 Adipose 605 Liver 485 Gastroc 404 Ping Wang insulin main effects Chr 2 Chr 9 Chr 12 Chr 16 Chr 17 Chr 19 Chr 14 How many islet transcripts show this same genetic dependence at these loci? Expression Networks Zhang & Horvath (2005) www.genetics.ucla/edu/labs/horvath/CoexpressionNetwork • organize expression traits using correlation • adjacency aij | cor ( xi , x j ) | , 6 • connectivity ki sum l (ail ) • topological overlap TOM ij aij sum l (ail a jl ) 1 aij min( ki , k j ) Using the topological overlap matrix (TOM) to cluster genes – modules correspond to branches of the dendrogram Genes correspond to rows and columns Hierarchical clustering dendrogram TOM plot TOM matrix Module: Correspond to branches module traits highly correlated • adjacency attenuates correlation • can separate positive, negative correlation • summarize module www.genetics.ucla/edu/labs/horvath/CoexpressionNetwork – eigengene – weighted average of traits • relate module – to clinical traits – map eigengene advantages of Horvath modules • emphasize modules (pathways) instead of individual genes – Greatly alleviates the problem of multiple comparisons – ~20 module comparisons versus 1000s of gene comparisons • intramodular connectivity ki finds key drivers (hub genes) – quantifies module membership (centrality) – highly connected genes have an increased chance of validation • module definition is based on gene expression data – no prior pathway information is used for module definition – two modules (eigengenes) can be highly correlated • unified approach for relating variables – compare data sets on same mathematical footing • scale-free: zoom in and see similar structure Ping Wang modules for 1984 transcripts with similar genetic architecture as insulin contains the insulin trait Islet – modules 17 2 16 14 19 12 9 chromosomes Insulin trait Islet – enrichment for modules Module BLUE GREEN PURPLE BLACK MAGENTA YELLOW RED BROWN TURQUOISE PINK Pvalue Qvalue Count Size 0.0005 0.0006 0.0009 0.0012 0.0008 0.0055 0.0056 0.0463 0.0470 0.0507 0.0593 0.0457 0.0970 0.0970 30 18 11 19 4 2 10 1068 511 241 590 76 20 707 0.0011 0.0078 2.54E-05 0.0001 0.0004 0.0005 0.0006 0.0009 0.0011 0.0012 0.0026 0.0026 0.0026 0.0017 0.0026 0.0057 0.0002 0.0003 0.0003 0.0004 0.0004 0.0092 0.0165 0.0138 0.0011 0.0040 0.0040 0.0040 0.0040 0.0041 0.0041 0.0041 0.0675 0.0675 0.0675 0.0619 0.0619 0.1442 0.0830 0.0830 0.0830 0.0830 0.0608 0.0612 7 2 7 5 5 5 5 5 4 4 7 7 7 2 5 4 17 10 7 40 2 4 2769 68 313 179 225 228 239 266 162 163 281 281 281 13 200 96 279 115 57 1021 14 384 Term biosynthetic process cellular lipid metabolic process lipid biosynthetic process lipid metabolic process phosphate transport intermediate filament-based process ion transport nucleobase, nucleoside, nucleotide and nucleic acid metabolic process sensory perception of sound cell cycle process microtubule-based process mitotic cell cycle M phase cell division cell cycle phase mitosis M phase of mitotic cell cycle cell projection organization and biogenesis cell part morphogenesis cell projection morphogenesis steroid hormone receptor signaling pathway reproductive process response to pheromone enzyme linked receptor protein signaling pathway morphogenesis of an epithelium morphogenesis of embryonic epithelium anatomical structure morphogenesis vesicle organization and biogenesis regulation of apoptosis Insulin chromosomes www.geneontology.org • ontologies – Cellular component (GOCC) – Biological process (GOBP) – Molecular function (GOMF) • hierarchy of classification – general to specific – based on extensive literature search, predictions • prone to errors, historical inaccuracies