Microarray Data Analysis Using BRB-ArrayTools Version 4.2.0 ...

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[Hands-on instructions][Significance Analysis of Microarrays – Pomeroy data]1. Still using the Pomeroy data, run the SAM tool by clicking onArrayTools Class comparison Significance Analysis ofMicroarrays (SAM).2. Again, select the Medulo vs Glio variable as the columndefining the classes, select the 90 th percentile option, and leaveall other parameters at default levels.3. Check the option to perform Gene ontology Observed vsExpected analysis.4. Now click OK to exit the options dialog, and click OK on themain dialog to launch the analysis.92
Gene set ExpressionComparison• Allows users to find significant sets of genes rather than just significantgenes.• For the Gene Ontology comparison, , all Gene Ontology classes thatare represented in the data are tested for significance.• For Pathway Comparison, , all the pathways that are represented in thedata are tested. For Human, the BioCarta or KEGG pathways are testedand for mouse, the BioCarta pathways are compared. Additionally,Broad/MIT pathways can be downloaded to be used in analyses.• For the User Gene Lists comparison, , the user can select specificgenelists that the user would like to test for significance.• Transcription factor target gene lists and microRNA target genelistshave been added to the Gene List comparison tool.• New to v3.8, the ability to handle multiple probe sets that correspondto the same gene either using the average intensity (single channel nel dataonly) or inter quartile range.93
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Microarray Data AnalysisUsing BRB-A
Page 3 and 4:
Part I:What is BRB-ArrayTools?3
Page 5 and 6:
Features of BRB-ArrayTools• Capab
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New to ArrayToolsv4.1• Affy ST ar
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Full Installer• Also available is
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Downloading BRB-ArrayTools• After
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Installing BRB-ArrayTools13
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Installing BRB-ArrayTools• Select
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Installing BRB-ArrayTools• Procee
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Installing BRB-ArrayTools• Instal
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Excel 2007- loading the add-in• 1
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Part II:Getting your data intoBRB-A
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Expression data(one or more files)E
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Expression dataHorizontally aligned
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Gene identifiers• A gene identifi
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Expression data(one or more files)E
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Experiment descriptorsDescribes the
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Gene lists• Genelists are used fo
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Gene listsUser-defined text files38
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Specify data using thecollate dialo
Page 42 and 43: Affymetrix CEL file importation•
Page 44 and 45: Part III:The collated projectworkbo
Page 46 and 47: [Hands-on instructions][Importing P
Page 52 and 53: • Click “OK”[Hands-on instruc
Page 54 and 55: Collated project workbookOverview
Page 56 and 57: Expression data(one or more files)E
Page 58 and 59: The collated project workbookGene i
Page 60 and 61: The collated project workbookGene a
Page 62 and 63: [Hands-on instructions][Data filter
Page 64 and 65: Data filtering optionsNormalization
Page 66 and 67: Data filtering optionsGene filters:
Page 68 and 69: Data filtering optionsGene subsets
Page 70 and 71: Scatterplot tools• Scatterplot of
Page 72 and 73: [Optional: Hands-on instructions][S
Page 74 and 75: Analysis Wizard• Finding GenesFin
Page 76 and 77: Tools for finding Genes/Genesetscom
Page 78 and 79: Class Comparison78
Page 80 and 81: Class comparison tool1. Enter the c
Page 82 and 83: Average over replicates• If multi
Page 84 and 85: Find genes lists determined by:84
Page 86 and 87: [Hands-on instructions][Class compa
Page 88 and 89: Class comparison toolMultivariate p
Page 90 and 91: Gene ontology analysis• In the cl
Page 94 and 95: Gene Set ExpressionComparison• Co
Page 96 and 97: Goeman’s s Global test• Tests t
Page 98 and 99: Quantitative trait tool• Selects
Page 100 and 101: Survival Gene Set analysis• This
Page 102 and 103: Hierarchical clustering tools• Cl
Page 104 and 105: [Hands-on instructions - cont’d][
Page 110 and 111: Multidimensional scaling• Rotatin
Page 112 and 113: Analysis Wizard- Prediction• Clas
Page 114 and 115: C1. Gene Selection Criteria• Sele
Page 116 and 117: C2. Class prediction Methods• Six
Page 118 and 119: Permutation test• Use a permutati
Page 120 and 121: Linear classifiers for two classesl
Page 122 and 123: Diagonal linear discriminant analys
Page 124 and 125: K-nearest neighbor• May be used f
Page 126 and 127: Support vector machines(V. Vapnik)
Page 128 and 129: Prediction Analysis MicroarrayPAM
Page 130 and 131: [Hands-on instructions ][Class pred
Page 132 and 133: Included plugins• Analysis of Var
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Page 136 and 137: Further help• We hope this class
Page 138 and 139: Technical support• For questions
Page 140 and 141: Feedback on this class• Please fi
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