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Berkeley STATISTICS 246 - Identifying differentially expressed sets of genes in microarray experiments

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Identifying differentially expressed sets of genes in microarray experimentsA cartoon version of microarraysLong lists of d.e. genes  biological understandingSets of genesThe Gene Ontology ConsortiumPowerPoint PresentationStructure of a GO annotationSlide 8Are sets of genes differentially expressed?GO and microarray gene setsIs a GO term is specific for a set?The multiple testing problemGOstat: Tool for finding significant GO terms in a list of genes http://gostat.wehi.edu.auThere are many similar toolsSlide 15Analyzing microarray data by functional gene sets defined a prioriPGC-1-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes149 gene setsTwo sample Kolmogorov-Smirnov testSlide 20Slide 21SimplificationMootha’s ts are approx normalOne sample z-testMootha’s dataResult from one sample z-testSimulation 1Slide 28Simulated dataSlide 30Slide 31Slide 32ConclusionAcknowledgements1Identifying differentially expressed sets of genes in microarray experimentsLecture 23, Statistics 246, April 15, 20042A cartoon version of microarraysA cartoon version of microarrayst ID Name-19.83 AA495790 ras homolog gene family-16.83 AA598794 connective tissue growth factor-15.22 AA488676 membrane attached signal protein 1-14.2AI014487 insulin-like growth factor binding protein 102-13.62 R77252 microtubule-associated protein 7-13.6 AA598601 insulin-like growth factor binding protein 31-13.57 R09561 decay accelerating factor for complement (CD55)-13.38 AA875933EGF-containing fibulin-like extracellular matrix protein 1-12.79 AA777187 cysteine-rich, angiogenic inducer-12.63 AA598601 insulin-like growth factor binding protein 3-12.01 AA055835 caveolin 1, caveolae protein, 22kD"-11.88 AA012944 insulin-like growth factor binding protein 102-10.86 AA936757 heparin-binding growth factor binding protein-10.86 AA995282 four and a half LIM domains 2-10.35 AA677403 glycoprotein hormones, alpha polypeptide-9.88 AA430032 pituitary tumor-transforming 1-9.32 AI935290 cysteine and glycine-rich protein 1-9.18 AA936757 heparin-binding growth factor binding protein2-9.06 AA424833 bone morphogenetic protein 6-9.02 AI985398 natriuretic peptide receptor C-8.51 AA630794 solute carrier family 3-8.38 H29897 phospholipase C, beta 42-8.22 W72207 cystatin A (stefin A)-7.99 H45668 Kruppel-like factor 4 (gut)-7.95 AA600217 activating transcription factor 4-7.8 AA149095 dual specificity phosphatase 1-7.68 W73874 cathepsin L-7.61 R09561 decay accelerating factor for complement-7.53 AW028846 trefoil factor 2 (spasmolytic protein 1)-7.16 N66177 microphthalmia-associated transcription factor-7.14 H03346 protease, serine, 22-7.06 AA169469 pyruvate dehydrogenase kinase, isoenzyme 4-6.96 AI989348 protein disulfide isomerase-related protein-6.94 H63077 annexin A1-6.92 AA610004 Homo sapiens putative oncogene protein-6.84 AA599145 ZW10 (Drosophila) homolog-6.78 AA521434 B-cell CLL/lymphoma 6-6.77 AA400128 general transcription factor II-6.68 T53298 insulin-like growth factor binding protein 7-6.67 T86983 complement component 1-6.6 AA027240 eukaryotic translation initiation factor 2-6.57 AA482117 Ras homolog enriched in brain 2-6.55 AA464849 thioredoxin reductase 1-6.55 AA400893 phosphodiesterase 1A, calmodulin-dependent-6.5 R91550 arginine-rich, mutated in early stage tumors-6.45 AA620433 dihydropyrimidinase-like 3-6.45 AA625628 accessory proteins BAP31/BAP29-6.41 T77733 tubulin, gamma 1List of differentially expressed genesLong list of d.e. genes3Long lists of d.e. genes  biological understandingWhat happens next? •Select some genes for validation?•Do follow-up experiments on some genes?•Publish a huge table with the results?•Try to learn about all the genes on the list (read 100s of papers)?•….Usually, some or all of the above will be done, and more. Can we help further at this4Sets of genesSets of genesThere are usually many sets of genes that might be of interest in a given microarray experiment. Examples include genes in biological (e.g. biochemical, metabolic, and signalling) pathways, genes associated with a particular location in the cell, or genes having a particular function or being involved in a particular process. We could even include sets of genes for which all of the preceding are unknown, but we have reason believe could be of interest, typically from previous experiments. In thinking like this, it is important to remember that many genes (that is, their protein products) can have multiple functions, or be involved in many processes, etc. There are many databases (EcoCyc, KEGG,..) of pathways, and it is not my intention to review them here. We will focus on the most important related concept: the GO.5The Gene Ontology ConsortiumAshburner et al. Nature Genetics 25: 25-29. http://www.geneontology.orgThe goal of the Gene Ontology TM (GO) Consortium is to produce acontrolled vocabulary that can be applied to all organisms even as knowledgeof gene and protein roles in cells is accumulating and changing. GO provides three structured networks of defined terms to describe gene product attributesMolecular Function Ontology (7304 terms as of April 5, 2004) : the tasks performed by individual gene products; examples are carbohydrate binding and ATPase activity Biological Process Ontology (8517 terms) broad biological goals, such as mitosis or purine metabolism, that are accomplished by ordered assemblies of molecular functionsCellular Component Ontology (1394 terms) subcellular structures, locations, and macromolecular complexes; examples include nucleus, telomere, and origin recognition complex6From the GO web site. The path back to each ontologyfrom a gene.We will call each term in apath a split.7Structure of a GO annotationStructure of a GO annotationEach gene can have several annotated GOs and each GO can have several splits. E.g. DNA topoisomerase II alpha has 8 GO annotations and 11 splits8Annotation of genes to a node in theontologyEach node is also connectedto many other related nodes.9Are sets of genes Are sets of genes differentially expressed?differentially expressed? The sets we refer to here are all the outcomes of analyses. Later we discuss sets specified a priori.Examples of sets. They could be the list of all genes whose differential expression (e.g. average M-value) exceeds a given threshold, typically a liberal one, which would not correspond to any real “significance”, e.g. 1.5-fold. They might be clusters. What do we mean by a set


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Berkeley STATISTICS 246 - Identifying differentially expressed sets of genes in microarray experiments

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