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Estimating Gene Function With LS-NMF 41Fig. 2. The PattRun interface after setting of the parameters for running LS-NMF.The arrows show the location of parameters to set before running the simulations.In order to get comparable decomposed matrices, amplitude (A) and pattern(P) matrices need to be normalized. The normalization chosen herein sets eachrow of P to unit amplitude (i.e., the sum of all elements of the row is 1). Thecolumns of A matrix must then be scaled inversely to leave M unchanged(see Eq. 1). There are many freely available tools that can be used to interpretthe results from LS-NMF simulation, herein ClutrFree is used (14), which isdescribed in Chapter 1 in this work. For mouse and human data, WebGestalt(15) is a useful web-based system that is described in Chapter 2 in this work.The graphical version of LS-NMF generates output files appropriate to use withClutrFree (see Note 7).3.4.1. Choosing a Single Best Simulation for AnalysisPattRun with LS-NMF will generate a series of results stored in directories(folders) in the directory containing the original data. Each directorywill contain one simulation result. The <strong>View</strong>er button on the PattRun windowcan be used to view these results one at a time. When it is pushed the first
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Gene Function Analysis
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METHODS IN MOLECULAR BIOLOGYGene Fu
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PrefaceThis volume of Methods in Mo
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Prefaceixcolleagues demonstrate how
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xiiContentsPART III EXPERIMENTAL ME
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ICOMPUTATIONAL METHODS I
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4 BidautTable 1Input File Format Us
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6 BidautTable 2Folder Layout to Use
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8 Bidaut• alphaA: this is the num
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10 Bidautcomputing the maximum corr
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12 BidautFig. 3. The complete Clutr
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Page 58: 16 BidautFig. 4. This is a comparis
Page 62: 18 BidautReferences1. Hughes, T. R.
Page 66: 20 Kirov et al.way to associate gen
Page 70: 22 Kirov et al.based on a study ass
Page 74: 24 Kirov et al.1. Retrieve the gene
Page 78: 26Fig. 1. Functional associations f
Page 82: 28 Kirov et al.Fig. 2. Pathway anal
Page 86: 30 Kirov et al.3. Gene symbols usag
Page 90: 32 Kirov et al.9. OBO_Team, Open Bi
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Page 108: 42 Wang and OchsFig. 3. The PattRun
Page 112: 44 Wang and OchsFig. 4. The gene ta
Page 116: 46 Wang and Ochsresults posttreatme
Page 120: 4From Promoter Analysis to Transcri
Page 124: Prediction Using PAINT 51even in si
Page 128: Prediction Using PAINT 53Fig. 1. A
Page 132: Prediction Using PAINT 55first exon
Page 138: 58 Gonye et al.Fig. 3. A network vi
Page 142: 60 Gonye et al.exGeneList.txt) is a
Page 146: 62 Gonye et al.(http://www.tm4.org)
Page 150: 64 Gonye et al.does not span the en
Page 154: 66 Gonye et al.4.7. Interpreting th
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68 Gonye et al.18. Dozmorov, M. G.,
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70 Uversky et al.in protein functio
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72 Uversky et al.sequence space and
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Table 1 (Continued)Server name URL
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76 Uversky et al.1.5. When to Use t
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78 Uversky et al.elucidating compos
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Table 2Averaged Frequencies of Diff
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82 Uversky et al.the box Raw Output
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84 Uversky et al.peculiarities of t
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86 Uversky et al.Fig. 4. Prediction
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88 Uversky et al.13. Uversky, V. N.
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90 Uversky et al.50. Vucetic, S., O
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92 Uversky et al.84. Ritter, L. M.,
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6Sybil: Methods and Software for Mu
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Sybil: Multiple Genome Comparison a
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7Estimating Protein Function Using
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Estimating Protein Function Using P
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130 Davuluriinteracting proteins an
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Table 1Web URLs of Promoter, TF Dat
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134 DavuluriPWM-based models do not
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136 DavuluriTF-map alignments of or
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138 Davuluridiscussed which program
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140 DavuluriTable 2ER-a-Responsive
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Table 3Sample Data Matrix Represent
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Table 3 (Continued)Class MYCMAX MYC
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146 DavuluriFig. 3. (A) CART Tree:
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148 Davuluri11. Vlieghe, D., Sandel
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150 Davuluri44. Berezikov, E., Gury
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9Mining Biomedical Data Using MetaM
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Mining Biomedical Data Using MMTx a
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172 Ho et al.Fig. 1. Artificial exa
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174 Ho et al.allowing for cases whe
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176 Ho et al.A different measure is
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178 Ho et al.3.1.3. LA and Generali
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180 Ho et al.The ECF-statistic can
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182 Ho et al.In the special case of
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184 Ho et al.Fig. 5. An illustratio
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186 Ho et al.Fig. 7. The power curv
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188 Ho et al.this section were not
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190 Ho et al.References1. Schena, M
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IIIEXPERIMENTAL METHODS
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194 Caldwell et al.for sequences th
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196 Caldwell et al.query because it
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198 Caldwell et al.Fig. 1. (A) Prot
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200 Caldwell et al.outside primer o
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202 Caldwell et al.5. Targeting scr
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204 Caldwell et al.will allow the s
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206 Caldwell et al.3.1.6. Plasmid P
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208 Caldwell et al.PCR amplify the
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210 Caldwell et al.8. Thawing cells
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212 Zhang et al.Going one step beyo
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214 Zhang et al.Fig. 2. Generation
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216 Zhang et al.Perform PCR cycles,
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218 Zhang et al.Fig. 4. Schematic m
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220 Zhang et al.Fig. 5. Replacement
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13Construction of Simple and Effici
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DNA Vector-Based shRNA-Expression S
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244 Hust et al.overcome by two appr
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246 Hust et al.Fig. 1. Schematic de
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248 Hust et al.interaction during p
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250 Hust et al.3.4. Titering1. Inoc
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252 Hust et al.10. Shortly before u
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254 Hust et al.activity by preservi
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15A Bacterial/Yeast Merged Two-Hybr
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Screening in Yeast With a Bacterial
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16A Bacterial/Yeast Merged Two-Hybr
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Dual Bait-Compatible Bacterial Two-
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318 Thibodeau-Beganny and Joungbeen
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320 Thibodeau-Beganny and JoungFig.
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324 Thibodeau-Beganny and JoungTypi
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328 Thibodeau-Beganny and JoungPCR
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330 Thibodeau-Beganny and Joung16-1
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332 Thibodeau-Beganny and Joung2. P
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334 Thibodeau-Beganny and Joung11.
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336 IndexKknockin (gene knockin) 19
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