Package 'limma' - Bioconductor

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40 changeLog Value An RGList, MAList, EList or EListRaw object holding data from all the arrays and all genes from the individual objects. Author(s) See Also Gordon Smyth cbind in the base package. 03.ReadingData gives an overview of data input and manipulation functions in LIMMA. Examples M
classifyTests 41 See Also 01.Introduction Examples changeLog() classifyTests Multiple Testing Genewise Across Contrasts Description Usage For each gene, classify a series of related t-statistics as up, down or not significant. classifyTestsF(object, cor.matrix=NULL, df=Inf, p.value=0.01, fstat.only=FALSE) classifyTestsT(object, t1=4, t2=3) classifyTestsP(object, df=Inf, p.value=0.05, method="holm") FStat(object, cor.matrix=NULL) Arguments object cor.matrix df p.value fstat.only t1 t2 method numeric matrix of t-statistics or an MArrayLM object from which the t-statistics may be extracted. covariance matrix of each row of t-statistics. Defaults to the identity matrix. numeric vector giving the degrees of freedom for the t-statistics. May have length 1 or length equal to the number of rows of tstat. numeric value between 0 and 1 giving the desired size of the test logical, if TRUE then return the overall F-statistic as for FStat instead of classifying the test results first critical value for absolute t-statistics second critical value for absolute t-statistics character string specifying p-value adjustment method. See p.adjust for possible values. Details Note that these functions do not adjust for multiple testing across genes. The adjustment for multiple testing is across the contrasts rather than the more usual control across genes. The functions described here are called by decideTests. Most users should use decideTests rather than using these functions directly. These functions implement multiple testing procedures for determining whether each statistic in a matrix of t-statistics should be considered significantly different from zero. Rows of tstat correspond to genes and columns to coefficients or contrasts.
Page 1 and 2: Package ‘limma’ May 22, 2014 Ve
Page 3 and 4: R topics documented: 3 heatdiagram
Page 5 and 6: 01.Introduction 5 voom . . . . . .
Page 7 and 8: 03.ReadingData 7 03.ReadingData Top
Page 9 and 10: 05.Normalization 9 05.Normalization
Page 11 and 12: 06.LinearModels 11 Assessing Differ
Page 13 and 14: 09.Diagnostics 13 Gene Set Tests Co
Page 15 and 16: 11.RNAseq 15 11.RNAseq Topic: Analy
Page 17 and 18: arrayWeights 17 Arguments Details o
Page 19 and 20: arrayWeightsQuick 19 See Also An ov
Page 21 and 22: as.MAList 21 Value A data.frame. Au
Page 23 and 24: asMatrixWeights 23 asMatrixWeights
Page 25 and 26: avearrays 25 Examples auROC(c(1,1,0
Page 27 and 28: avereps 27 Author(s) Gordon Smyth S
Page 29 and 30: ackgroundCorrect 29 printer a list
Page 31 and 32: arcodeplot 31 Arguments index index
Page 33 and 34: eadCountWeights 33 Details Value Th
Page 35 and 36: wss 35 Examples a
Page 37 and 38: camera 37 camera Competitive Gene S
Page 39: cbind 39 Examples y
Page 43 and 44: contrastAsCoef 43 contrastAsCoef Re
Page 45 and 46: contrasts.fit 45 Warning. For effic
Page 47 and 48: decideTests 47 Author(s) Gordon Smy
Page 49 and 50: diffSplice 49 Details Value If desi
Page 51 and 52: dim 51 dim Retrieve the Dimensions
Page 53 and 54: dupcor 53 dupcor Correlation Betwee
Page 55 and 56: ebayes 55 ## End(Not run) ebayes Em
Page 57 and 58: ebayes 57 F F.p.value numeric vecto
Page 59 and 60: exprs.MA 59 See Also 02.Classes giv
Page 61 and 62: fitGammaIntercept 61 References Smy
Page 63 and 64: genas 63 Author(s) Gordon Smyth See
Page 65 and 66: geneSetTest 65 Examples # Simulate
Page 67 and 68: geneSetTest 67 There are four possi
Page 69 and 70: getLayout 69 Author(s) Gordon Smyth
Page 71 and 72: gls.series 71 Details "rows" means
Page 73 and 74: gridr 73 Author(s) Gordon Smyth See
Page 75 and 76: helpMethods 75 Details Value Users
Page 77 and 78: imageplot 77 zlim mar legend numeri
Page 79 and 80: intraspotCorrelation 79 intraspotCo
Page 81 and 82: isNumeric 81 Author(s) Gordon Smyth
Page 83 and 84: LargeDataObject-class 83 Author(s)
Page 85 and 86: lm.series 85 lm.series Fit Linear M
Page 87 and 88: lmFit 87 Details Value This functio
Page 89 and 90: lmscFit 89 # Suppose two side-by-si
Page 91 and 92:
loessFit 91 Arguments y x weights s
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ma3x3 93 ma3x3 Two dimensional Movi
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makeUnique 95 Examples makeContrast
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MArrayLM-class 97 MArrayLM-class Mi
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merge 99 See Also An overview of di
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mergeScans 101 Value are affected b
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modifyWeights 103 Author(s) Gordon
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nec 105 Details This is a utility f
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normalizeBetweenArrays 107 Referenc
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normalizeBetweenArrays 109 same emp
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normalizeCyclicLoess 111 Details Th
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normalizeForPrintorder 113 Details
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normalizeQuantiles 115 Examples M
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normalizeVSN 117 Value Numeric vect
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normalizeWithinArrays 119 Usage nor
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normexp.fit 121 normexp.fit Fit Nor
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normexp.fit.control 123 Usage norme
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normexp.fit.detection.p 125 Details
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plot.MArrayLM 127 References Ritchi
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plotDensities 129 References See Al
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plotFB 131 plotFB FB-Plot Descripti
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plotMA 133 plotMA MA-Plot Descripti
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plotMA3by2 135 # Same as above attr
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plotMDS 137 Details Value This func
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plotRLDF 139 plotRLDF Plot of regul
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plotSplice 141 Arguments fit xlab y
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predFCm 143 Value A list with compo
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printHead 145 # fit a linear model
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printorder 147 printorder Identify
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printtipWeights 149 Details Value T
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propTrueNull 151 References Shi, W,
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protectMetachar 153 # True pi0 160/
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QualityWeights 155 See Also qqnorm
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ankSumTestWithCorrelation 157 Detai
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ead.idat 159 Author(s) Gordon Smyth
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ead.ilmn 161 ctrlpath probeid annot
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ead.maimages 163 Author(s) Wei Shi
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ead.maimages 165 Value ArrayVision
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eadGAL 167 readGAL Read a GAL file
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eadImaGeneHeader 169 Value A list w
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eadTargets 171 Arguments file path
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emoveExt 173 Arguments x batch batc
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RGList-class 175 RGList-class Red,
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oast 177 array.weights weights bloc
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omer 179 References Goeman, JJ, and
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omer 181 down test. This improves t
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squeezeVar 183 See Also An overview
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strsplit2 185 Sartor MA, Tomlinson
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summary 187 Details i,j may take an
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targetsA2C 189 Value list of intege
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tmixture 191 Author(s) See Also Gor
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topSplice 193 Examples # See romer
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toptable 195 lfc Details confint mi
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tricubeMovingAverage 197 tricubeMov
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trimWhiteSpace 199 trimWhiteSpace T
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venn 201 Value A matrix containing
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volcanoplot 203 volcanoplot Volcano
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vooma 205 Value counts across all s
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weighted.median 207 weighted.median
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write.fit 209 Value A list of numer
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zscore 211 Arguments q, x numeric v
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Index ∗Topic IO controlStatus, 46
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INDEX 215 ma3x3, 93 tricubeMovingAv
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INDEX 217 mergeScans, 100 mergeScan
Page 219:
INDEX 219 weighted.median, 207 weig
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