limma: Linear Models for Microarray Data

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). 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.

2 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 .. 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.

3 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 .. 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.

4 PlotDensities .. 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. 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.

5 230. 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 .. 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.

6 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). Robust hyperparam- eter estimation protects against hypervariable genes and improves power to detect differential ex- pression. Annals of Applied Statistics 10, 946-963.

7 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. Statistical Applications in Genetics and Molecular Biology, Volume 3, Article 3. See Also , , , , , nel, , , , 7. Topic: Classes Defined by this Package Description This package defines the following data classes. RGList A class used to store raw intensities as they are read in from an image analysis output file, usually by MAList Intensities converted to M-values and A-values, , to with-spot and whole-spot contrasts on the log-scale.

8 Usually created from an RGList using or normalizeWithinArrays. Objects of this class contain one row for each spot. There may be more than one spot and therefore more than one row for each probe. EListRaw A class to store raw intensities for one-channel Microarray data. May or may not be background corrected. Usually created by EList A class to store normalized log2 expression values for one-channel Microarray data. Usu- ally created by normalizeBetweenArrays. MArrayLM Store the result of fitting gene-wise Linear Models to the normalized intensities or log- ratios. Usually created by lmFit. Objects of this class normally contain only one row for each unique probe. TestResults Store the results of testing a set of contrasts equal to zero for each probe. Usually created by decideTests. Objects of this class normally contain one row for each unique probe. All these data classes obey many analogies with matrices.

9 In the case of RGList, MAList, EListRaw and EList, rows correspond to spots or probes and columns to arrays. In the case of MarrayLM, rows correspond to unique probes and the columns to parameters or contrasts. The functions summary, dim, length, ncol, nrow, dimnames, rownames, colnames have methods for these classes. Objects of any of these classes may be subsetted. Multiple data objects may be combined by rows (to add extra probes) or by columns (to add extra arrays). Furthermore all of these classes may be coerced to actually be of class matrix using , although this entails loss of information. Fitted model objects of class MArrayLM can be coerced to class using The first three classes belong to the virtual class LargeDataObject. A show method is defined for LargeDataOjects which uses the utility function printHead. Author(s). Gordon Smyth See Also , , , , , , , , , , 8 Topic: Reading Microarray Data from Files Description This help page gives an overview of limma functions used to read data from files.

10 Reading Target Information The function readTargets is designed to help with organizing information about which RNA sam- ple is hybridized to each channel on each array and which files store information for each array. Reading Intensity Data The first step in a Microarray data analysis is to read into R the intensity data for each array provided by an image analysis program. This is done using the function optionally constructs quality weights for each spot using quality functions listed in QualityWeights. If the data is two-color, then produces an RGList object. If the data is one- color (single channel) then an EListRaw object is produced. In either case, stores only the information required from each image analysis output file. uses util- ity functions removeExt, and There are also a series of utility functions which read the header information from image output files including readGPRH eader, readImaGeneHeader and readGenericHeader.