Transcription of limma: Linear Models for Microarray Data - Bioconductor
1 Package limma '. February 15, 2022. Version Date 2021-10-24. Title Linear Models for Microarray Data Description Data analysis, Linear Models and differential expression for Microarray data. Author Gordon Smyth [cre,aut], Yifang Hu [ctb], Matthew Ritchie [ctb], Jeremy Sil- ver [ctb], James Wettenhall [ctb], Davis McCarthy [ctb], Di Wu [ctb], Wei Shi [ctb], Be- linda Phipson [ctb], Aaron Lun [ctb], Natalie Thorne [ctb], Alicia Oshlack [ctb], Car- olyn de Graaf [ctb], Yunshun Chen [ctb], Mette Langaas [ctb], Egil Ferkingstad [ctb], Mar- cus Davy [ctb], Francois Pepin [ctb], Dongseok Choi [ctb]. Maintainer Gordon Smyth License GPL (>=2).
2 Depends R (>= ). Imports grDevices, graphics, stats, utils, methods Suggests affy, AnnotationDbi, BiasedUrn, Biobase, ellipse, , gplots, illuminaio, locfit, MASS, , splines, statmod (>= ), vsn URL biocViews ExonArray, GeneExpression, Transcription, AlternativeSplicing, DifferentialExpression, DifferentialSplicing, GeneSetEnrichment, DataImport, Bayesian, Clustering, Regression, TimeCourse, Microarray , MicroRNAA rray, mRNAM icroarray, OneChannel, ProprietaryPlatforms, TwoChannel, Sequencing, RNASeq, BatchEffect, MultipleComparison, Normalization, Preprocessing, QualityControl, BiomedicalInformatics, CellBiology, Cheminformatics, Epigenetics, FunctionalGenomics, Genetics, ImmunoOncology, Metabolomics, Proteomics, SystemsBiology, Transcriptomics git_url git_branch RELEASE_3_14.
3 Git_last_commit 657b19b git_last_commit_date 2021-10-26. Date/Publication 2022-02-15. 1. 2 R topics documented: R topics documented: .. 5.. 7.. 8.. 9.. 10.. 11.. 13.. 14.. 15.. 16.. 17. alias2 Symbol .. 18.. 20. arrayWeights .. 21. arrayWeightsQuick .. 23.. 24.. 25.. 26. asMatrixWeights .. 27. auROC .. 28. avearrays .. 29. avedups .. 30. avereps .. 31. backgroundCorrect .. 32. barcodeplot .. 34. beadCountWeights .. 38. blockDiag .. 40. bwss .. 41.. 42. camera .. 43. cbind .. 45. changeLog .. 47. chooseLowessSpan .. 48. classifyTestsF .. 49. contrastAsCoef .. 51.. 52. controlStatus .. 54. coolmap.
4 55. cumOverlap .. 57. decideTests .. 58. designI2M .. 60. detectionPValues .. 61. diffSplice .. 63. dim .. 64. dimnames .. 65. dupcor .. 66. R topics documented: 3. eBayes .. 68. EList-class .. 72.. 74. fitFDist .. 75. fitGammaIntercept .. 77. fitmixture .. 78.. 79. genas .. 80. geneSetTest .. 82. getEAWP .. 85. getLayout .. 86. getSpacing .. 87.. 88. goana .. 90. gridr .. 95. head .. 95. heatdiagram .. 96. helpMethods .. 98. ids2indices .. 99. imageplot .. 100. imageplot3by2 .. 101. intraspotCorrelation .. 102.. 104. isNumeric .. 105. kooperberg .. 106. LargeDataObject-class .. 107. limmaUsersGuide.
5 108.. 109. lmFit .. 110. lmscFit .. 113. loessFit .. 114. logcosh .. 117. logsumexp .. 118. ma3x3 .. 119. makeContrasts .. 120. makeUnique .. 121. MAList-class .. 122. MArrayLM-class .. 123. mdplot .. 124. merge .. 125. mergeScans .. 126. modelMatrix .. 128. modifyWeights .. 130. mrlm .. 131. nec .. 132. normalizeBetweenArrays .. 134. normalizeCyclicLoess .. 137. normalizeForPrintorder .. 138. 4 R topics documented: normalizeMedianAbsValues .. 140. normalizeQuantiles .. 141. normalizeRobustSpline .. 142. normalizeVSN .. 144. normalizeWithinArrays .. 145.. 147.. 149.. 151.. 153. plotDensities.
6 154. plotExonJunc .. 155. plotExons .. 157. plotFB .. 158. plotlines .. 159. plotMA .. 160. plotMA3by2 .. 162. plotMD .. 164. plotMDS .. 166. plotPrintTipLoess .. 169. plotRLDF .. 170. plotSA .. 172. plotSplice .. 173. plotWithHighlights .. 174. poolVar .. 176. predFCm .. 178. printHead .. 179. PrintLayout .. 180. printorder .. 181. printtipWeights .. 182. propexpr .. 184. propTrueNull .. 186. protectMetachar .. 188. qqt .. 189. QualityWeights .. 190. rankSumTestWithCorrelation .. 191.. 193.. 194.. 196.. 198.. 199. readGAL .. 203. readHeader .. 204. readImaGeneHeader .. 205. readSpotTypes .. 206.
7 ReadTargets .. 207. removeBatchEffect .. 208. removeExt .. 210.. 211. 5. RGList-class .. 211. roast .. 212. romer .. 218. selectModel .. 220. squeezeVar .. 221. strsplit2 .. 223. subsetting .. 224. summary .. 226. targetsA2C .. 227. TestResults-class .. 228. tmixture .. 229. topGO .. 230. topRomer .. 231. topSplice .. 232. topTable .. 233. tricubeMovingAverage .. 236. trigammaInverse .. 238. trimWhiteSpace .. 239. uniquegenelist .. 239. unwrapdups .. 240. venn .. 241. volcanoplot .. 243. voom .. 244. vooma .. 247. voomWithQualityWeights .. 249.. 250. weightedLowess .. 251.. 254. wsva .. 256. zscore.
8 257. zscoreT .. 258. Index 261. Introduction to the limma Package Description limma is a library for the analysis of gene expression Microarray data, especially the use of Linear Models for analysing designed experiments and the assessment of differential expression. limma . provides the ability to analyse comparisons between many RNA targets simultaneously in arbitrary complicated designed experiments. Empirical Bayesian methods are used to provide stable results even when the number of arrays is small. The Linear model and differential expression functions apply to all gene expression technologies, including microarrays, RNA-seq and quantitative PCR.
9 6 Details There are three types of documentation available: 1. The limma User's Guide can be reached through the "User Guides and Package Vignettes". links at the top of the limma contents page. The function limmaUsersGuide gives the file location of the User's Guide. 2. An overview of limma functions grouped by purpose is contained in the numbered chapters at the foot of the limma package index page, of which this page is the first. 3. The limma contents page gives an alphabetical index of detailed help topics. The function changeLog displays the record of changes to the package. Author(s). Gordon Smyth, with contributions from many colleagues References Phipson, B, Lee, S, Majewski, IJ, Alexander, WS, and Smyth, GK (2016).
10 Robust hyperparam- eter estimation protects against hypervariable genes and improves power to detect differential ex- pression. Annals of Applied Statistics 10, 946-963. 1469199900. Ritchie, ME, Phipson, B, Wu, D, Hu, Y, Law, CW, Shi, W, and Smyth, GK (2015). limma pow- ers differential expression analyses for RNA-sequencing and Microarray studies. Nucleic Acids Research 43, e47. Law, CW, Chen, Y, Shi, W, and Smyth, GK (2014). Voom: precision weights unlock Linear model analysis tools for RNA-seq read counts. Genome Biology 15, R29. 2014/15/2/R29. Smyth, G. K. (2004). Linear Models and empirical Bayes methods for assessing differential ex- pression in Microarray experiments.
