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

More documents

Recommendations

Info

BRB-ArrayToolsAn Integrated Software Tool forDNA Microarray Analysis• Developed under the direction of Dr. Richard Simon of theBiometrics Research Branch, NCI.• Software was developed with the purpose of deployingpowerful statistical tools for use by biologists.• Analyses are launched from user-friendly Excel interface.Also requires installation of a free software called R forrunning back-end programs. Current requirement for R is v2.12.0. Publicly available from BRB website:http://linus.nci.nih.gov/BRB-ArrayTools.html4
Features of BRB-ArrayTools• Capability to collate (sort into an expression data matrix) microarray datafrom a set of experiments, and apply filtering and normalization.Compute RMA/GC-RMA/MAS5.0 RMA/MAS5.0 probeset summaries andnormalization. BRB-ArrayTools was designed to analyze a set of arraysrather than a single array.• The focus of the software has been the implementation of statisticalicalmethodology which utilizes the sample descriptors (supervised analysis).alysis).• Scatterplots, hierarchical clustering, and multidimensional scaling analysesalso provide powerful visualization tools.• Gene annotations are integrated into analysis output to inform the tanalysis results. Also, includes analyses using Biocarta,KEGG andBroad/MIT pathways.• Advanced users may program their own plugin analysis tools within n BRB-ArrayTools.5
Page 1 and 2: Microarray Data AnalysisUsing BRB-A
Page 3: Part I:What is BRB-ArrayTools?3
Page 7 and 8: New to ArrayToolsv4.1• Affy ST ar
Page 9 and 10: Full Installer• Also available is
Page 11 and 12: Downloading BRB-ArrayTools• After
Page 13 and 14: Installing BRB-ArrayTools13
Page 15 and 16: Installing BRB-ArrayTools• Select
Page 17 and 18: Installing BRB-ArrayTools• Procee
Page 19 and 20: Installing BRB-ArrayTools• Instal
Page 21: Excel 2007- loading the add-in• 1
Page 24 and 25: Part II:Getting your data intoBRB-A
Page 26 and 27: Expression data(one or more files)E
Page 28 and 29: Expression dataHorizontally aligned
Page 30 and 31: Gene identifiers• A gene identifi
Page 32 and 33: Expression data(one or more files)E
Page 34 and 35: Experiment descriptorsDescribes the
Page 36 and 37: Gene lists• Genelists are used fo
Page 38 and 39: Gene listsUser-defined text files38
Page 40 and 41: 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 92 and 93:
[Hands-on instructions][Significanc
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 106 and 107:
Page 108 and 109:
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
Page 134 and 135:
134
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
show all

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

Create successful ePaper yourself

Delete template?

Save as template?