Question: help with limma design, contrast matrices
0
5.8 years ago by
Lisa Cohen50
United States
Lisa Cohen50 wrote:
I have a set of microarray data (one-channel custom Agilent) that I'm trying to analyze for gene expression differences following an experiment: Sponges were acutely exposed to combinations of oil and dispersant treatments. There were 4 treatment groups: OD, OC, UD, UC, 6 sponge colonies each fragmented 12 times, with three replicates = 72 samples total. I found some examples from the limma user's guide and other materials, but I'm still having trouble. http://www.bioconductor.org/help/course- materials/2009/BioC2009/labs/limma/limma.pdf https://stat.ethz.ch/pipermail/bioconductor/2012-January/043154.html http://www.bioconductor.org/packages/2.12/bioc/vignettes/limma/inst/do c/usersguide.pdf In coding my contrast and design matrices, I'm confused and wondering if someone can help? Details and code below. Thank you in advance! Lisa This is the design matrix I set up: > v<-c(0,1) > mat<-cbind(c(rep(v[2],18),rep(v[1],54)), + c(rep(v[1],18),rep(v[2],18),rep(v[1],36)), + c(rep(v[1],36),rep(v[2],18),rep(v[1],18)), + c(rep(v[1],54),rep(v[2],18)), + c(rep(1:6,12))) > colnames(mat)<-c("UC","UD","OC","OD","Colony") > mat UC UD OC OD Colony [1,] 1 0 0 0 1 [2,] 1 0 0 0 2 [3,] 1 0 0 0 3 [4,] 1 0 0 0 4 [5,] 1 0 0 0 5 [6,] 1 0 0 0 6 [7,] 1 0 0 0 1 [8,] 1 0 0 0 2 [9,] 1 0 0 0 3 [10,] 1 0 0 0 4 [11,] 1 0 0 0 5 [12,] 1 0 0 0 6 [13,] 1 0 0 0 1 [14,] 1 0 0 0 2 [15,] 1 0 0 0 3 [16,] 1 0 0 0 4 [17,] 1 0 0 0 5 [18,] 1 0 0 0 6 [19,] 0 1 0 0 1 [20,] 0 1 0 0 2 [21,] 0 1 0 0 3 [22,] 0 1 0 0 4 [23,] 0 1 0 0 5 [24,] 0 1 0 0 6 [25,] 0 1 0 0 1 [26,] 0 1 0 0 2 [27,] 0 1 0 0 3 [28,] 0 1 0 0 4 [29,] 0 1 0 0 5 [30,] 0 1 0 0 6 [31,] 0 1 0 0 1 [32,] 0 1 0 0 2 [33,] 0 1 0 0 3 [34,] 0 1 0 0 4 [35,] 0 1 0 0 5 [36,] 0 1 0 0 6 [37,] 0 0 1 0 1 [38,] 0 0 1 0 2 [39,] 0 0 1 0 3 [40,] 0 0 1 0 4 [41,] 0 0 1 0 5 [42,] 0 0 1 0 6 [43,] 0 0 1 0 1 [44,] 0 0 1 0 2 [45,] 0 0 1 0 3 [46,] 0 0 1 0 4 [47,] 0 0 1 0 5 [48,] 0 0 1 0 6 [49,] 0 0 1 0 1 [50,] 0 0 1 0 2 [51,] 0 0 1 0 3 [52,] 0 0 1 0 4 [53,] 0 0 1 0 5 [54,] 0 0 1 0 6 [55,] 0 0 0 1 1 [56,] 0 0 0 1 2 [57,] 0 0 0 1 3 [58,] 0 0 0 1 4 [59,] 0 0 0 1 5 [60,] 0 0 0 1 6 [61,] 0 0 0 1 1 [62,] 0 0 0 1 2 [63,] 0 0 0 1 3 [64,] 0 0 0 1 4 [65,] 0 0 0 1 5 [66,] 0 0 0 1 6 [67,] 0 0 0 1 1 [68,] 0 0 0 1 2 [69,] 0 0 0 1 3 [70,] 0 0 0 1 4 [71,] 0 0 0 1 5 [72,] 0 0 0 1 6 What is the role of the contrast matrix? When I set up model.matrix(), there are too many comparisons: > design<-model.matrix(~factor(mat)) > design (Intercept) factor(mat)1 factor(mat)2 factor(mat)3 factor(mat)4 factor(mat)5 factor(mat)6 1 1 1 0 0 0 0 0 2 1 1 0 0 0 0 0 3 1 1 0 0 0 0 0 4 1 1 0 0 0 0 0 5 1 1 0 0 0 0 0 6 1 1 0 0 0 0 0 7 1 1 0 0 0 0 0 8 1 1 0 0 0 0 0 9 1 1 0 0 0 0 0 10 1 1 0 0 0 0 0 11 1 1 0 0 0 0 0 12 1 1 0 0 0 0 0 13 1 1 0 0 0 0 0 14 1 1 0 0 0 0 0 15 1 1 0 0 0 0 0 16 1 1 0 0 0 0 0 17 1 1 0 0 0 0 0 18 1 1 0 0 0 0 0 19 1 0 0 0 0 0 0 20 1 0 0 0 0 0 0 21 1 0 0 0 0 0 0 22 1 0 0 0 0 0 0 23 1 0 0 0 0 0 0 24 1 0 0 0 0 0 0 25 1 0 0 0 0 0 0 26 1 0 0 0 0 0 0 27 1 0 0 0 0 0 0 28 1 0 0 0 0 0 0 29 1 0 0 0 0 0 0 30 1 0 0 0 0 0 0 31 1 0 0 0 0 0 0 32 1 0 0 0 0 0 0 33 1 0 0 0 0 0 0 34 1 0 0 0 0 0 0 35 1 0 0 0 0 0 0 36 1 0 0 0 0 0 0 37 1 0 0 0 0 0 0 38 1 0 0 0 0 0 0 39 1 0 0 0 0 0 0 40 1 0 0 0 0 0 0 41 1 0 0 0 0 0 0 42 1 0 0 0 0 0 0 43 1 0 0 0 0 0 0 44 1 0 0 0 0 0 0 45 1 0 0 0 0 0 0 46 1 0 0 0 0 0 0 47 1 0 0 0 0 0 0 48 1 0 0 0 0 0 0 49 1 0 0 0 0 0 0 50 1 0 0 0 0 0 0 51 1 0 0 0 0 0 0 52 1 0 0 0 0 0 0 53 1 0 0 0 0 0 0 54 1 0 0 0 0 0 0 55 1 0 0 0 0 0 0 56 1 0 0 0 0 0 0 57 1 0 0 0 0 0 0 58 1 0 0 0 0 0 0 59 1 0 0 0 0 0 0 60 1 0 0 0 0 0 0 61 1 0 0 0 0 0 0 62 1 0 0 0 0 0 0 63 1 0 0 0 0 0 0 64 1 0 0 0 0 0 0 65 1 0 0 0 0 0 0 66 1 0 0 0 0 0 0 67 1 0 0 0 0 0 0 68 1 0 0 0 0 0 0 69 1 0 0 0 0 0 0 70 1 0 0 0 0 0 0 71 1 0 0 0 0 0 0 72 1 0 0 0 0 0 0 73 1 0 0 0 0 0 0 74 1 0 0 0 0 0 0 75 1 0 0 0 0 0 0 76 1 0 0 0 0 0 0 77 1 0 0 0 0 0 0 78 1 0 0 0 0 0 0 79 1 0 0 0 0 0 0 80 1 0 0 0 0 0 0 81 1 0 0 0 0 0 0 82 1 0 0 0 0 0 0 83 1 0 0 0 0 0 0 84 1 0 0 0 0 0 0 85 1 0 0 0 0 0 0 86 1 0 0 0 0 0 0 87 1 0 0 0 0 0 0 88 1 0 0 0 0 0 0 89 1 0 0 0 0 0 0 90 1 0 0 0 0 0 0 91 1 1 0 0 0 0 0 92 1 1 0 0 0 0 0 93 1 1 0 0 0 0 0 94 1 1 0 0 0 0 0 95 1 1 0 0 0 0 0 96 1 1 0 0 0 0 0 97 1 1 0 0 0 0 0 98 1 1 0 0 0 0 0 99 1 1 0 0 0 0 0 100 1 1 0 0 0 0 0 101 1 1 0 0 0 0 0 102 1 1 0 0 0 0 0 103 1 1 0 0 0 0 0 104 1 1 0 0 0 0 0 105 1 1 0 0 0 0 0 106 1 1 0 0 0 0 0 107 1 1 0 0 0 0 0 108 1 1 0 0 0 0 0 109 1 0 0 0 0 0 0 110 1 0 0 0 0 0 0 111 1 0 0 0 0 0 0 112 1 0 0 0 0 0 0 113 1 0 0 0 0 0 0 114 1 0 0 0 0 0 0 115 1 0 0 0 0 0 0 116 1 0 0 0 0 0 0 117 1 0 0 0 0 0 0 118 1 0 0 0 0 0 0 119 1 0 0 0 0 0 0 120 1 0 0 0 0 0 0 121 1 0 0 0 0 0 0 122 1 0 0 0 0 0 0 123 1 0 0 0 0 0 0 124 1 0 0 0 0 0 0 125 1 0 0 0 0 0 0 126 1 0 0 0 0 0 0 127 1 0 0 0 0 0 0 128 1 0 0 0 0 0 0 129 1 0 0 0 0 0 0 130 1 0 0 0 0 0 0 131 1 0 0 0 0 0 0 132 1 0 0 0 0 0 0 133 1 0 0 0 0 0 0 134 1 0 0 0 0 0 0 135 1 0 0 0 0 0 0 136 1 0 0 0 0 0 0 137 1 0 0 0 0 0 0 138 1 0 0 0 0 0 0 139 1 0 0 0 0 0 0 140 1 0 0 0 0 0 0 141 1 0 0 0 0 0 0 142 1 0 0 0 0 0 0 143 1 0 0 0 0 0 0 144 1 0 0 0 0 0 0 145 1 0 0 0 0 0 0 146 1 0 0 0 0 0 0 147 1 0 0 0 0 0 0 148 1 0 0 0 0 0 0 149 1 0 0 0 0 0 0 150 1 0 0 0 0 0 0 151 1 0 0 0 0 0 0 152 1 0 0 0 0 0 0 153 1 0 0 0 0 0 0 154 1 0 0 0 0 0 0 155 1 0 0 0 0 0 0 156 1 0 0 0 0 0 0 157 1 0 0 0 0 0 0 158 1 0 0 0 0 0 0 159 1 0 0 0 0 0 0 160 1 0 0 0 0 0 0 161 1 0 0 0 0 0 0 162 1 0 0 0 0 0 0 163 1 0 0 0 0 0 0 164 1 0 0 0 0 0 0 165 1 0 0 0 0 0 0 166 1 0 0 0 0 0 0 167 1 0 0 0 0 0 0 168 1 0 0 0 0 0 0 169 1 0 0 0 0 0 0 170 1 0 0 0 0 0 0 171 1 0 0 0 0 0 0 172 1 0 0 0 0 0 0 173 1 0 0 0 0 0 0 174 1 0 0 0 0 0 0 175 1 0 0 0 0 0 0 176 1 0 0 0 0 0 0 177 1 0 0 0 0 0 0 178 1 0 0 0 0 0 0 179 1 0 0 0 0 0 0 180 1 0 0 0 0 0 0 181 1 1 0 0 0 0 0 182 1 1 0 0 0 0 0 183 1 1 0 0 0 0 0 184 1 1 0 0 0 0 0 185 1 1 0 0 0 0 0 186 1 1 0 0 0 0 0 187 1 1 0 0 0 0 0 188 1 1 0 0 0 0 0 189 1 1 0 0 0 0 0 190 1 1 0 0 0 0 0 191 1 1 0 0 0 0 0 192 1 1 0 0 0 0 0 193 1 1 0 0 0 0 0 194 1 1 0 0 0 0 0 195 1 1 0 0 0 0 0 196 1 1 0 0 0 0 0 197 1 1 0 0 0 0 0 198 1 1 0 0 0 0 0 199 1 0 0 0 0 0 0 200 1 0 0 0 0 0 0 201 1 0 0 0 0 0 0 202 1 0 0 0 0 0 0 203 1 0 0 0 0 0 0 204 1 0 0 0 0 0 0 205 1 0 0 0 0 0 0 206 1 0 0 0 0 0 0 207 1 0 0 0 0 0 0 208 1 0 0 0 0 0 0 209 1 0 0 0 0 0 0 210 1 0 0 0 0 0 0 211 1 0 0 0 0 0 0 212 1 0 0 0 0 0 0 213 1 0 0 0 0 0 0 214 1 0 0 0 0 0 0 215 1 0 0 0 0 0 0 216 1 0 0 0 0 0 0 217 1 0 0 0 0 0 0 218 1 0 0 0 0 0 0 219 1 0 0 0 0 0 0 220 1 0 0 0 0 0 0 221 1 0 0 0 0 0 0 222 1 0 0 0 0 0 0 223 1 0 0 0 0 0 0 224 1 0 0 0 0 0 0 225 1 0 0 0 0 0 0 226 1 0 0 0 0 0 0 227 1 0 0 0 0 0 0 228 1 0 0 0 0 0 0 229 1 0 0 0 0 0 0 230 1 0 0 0 0 0 0 231 1 0 0 0 0 0 0 232 1 0 0 0 0 0 0 233 1 0 0 0 0 0 0 234 1 0 0 0 0 0 0 235 1 0 0 0 0 0 0 236 1 0 0 0 0 0 0 237 1 0 0 0 0 0 0 238 1 0 0 0 0 0 0 239 1 0 0 0 0 0 0 240 1 0 0 0 0 0 0 241 1 0 0 0 0 0 0 242 1 0 0 0 0 0 0 243 1 0 0 0 0 0 0 244 1 0 0 0 0 0 0 245 1 0 0 0 0 0 0 246 1 0 0 0 0 0 0 247 1 0 0 0 0 0 0 248 1 0 0 0 0 0 0 249 1 0 0 0 0 0 0 250 1 0 0 0 0 0 0 251 1 0 0 0 0 0 0 252 1 0 0 0 0 0 0 253 1 0 0 0 0 0 0 254 1 0 0 0 0 0 0 255 1 0 0 0 0 0 0 256 1 0 0 0 0 0 0 257 1 0 0 0 0 0 0 258 1 0 0 0 0 0 0 259 1 0 0 0 0 0 0 260 1 0 0 0 0 0 0 261 1 0 0 0 0 0 0 262 1 0 0 0 0 0 0 263 1 0 0 0 0 0 0 264 1 0 0 0 0 0 0 265 1 0 0 0 0 0 0 266 1 0 0 0 0 0 0 267 1 0 0 0 0 0 0 268 1 0 0 0 0 0 0 269 1 0 0 0 0 0 0 270 1 0 0 0 0 0 0 271 1 1 0 0 0 0 0 272 1 1 0 0 0 0 0 273 1 1 0 0 0 0 0 274 1 1 0 0 0 0 0 275 1 1 0 0 0 0 0 276 1 1 0 0 0 0 0 277 1 1 0 0 0 0 0 278 1 1 0 0 0 0 0 279 1 1 0 0 0 0 0 280 1 1 0 0 0 0 0 281 1 1 0 0 0 0 0 282 1 1 0 0 0 0 0 283 1 1 0 0 0 0 0 284 1 1 0 0 0 0 0 285 1 1 0 0 0 0 0 286 1 1 0 0 0 0 0 287 1 1 0 0 0 0 0 288 1 1 0 0 0 0 0 289 1 1 0 0 0 0 0 290 1 0 1 0 0 0 0 291 1 0 0 1 0 0 0 292 1 0 0 0 1 0 0 293 1 0 0 0 0 1 0 294 1 0 0 0 0 0 1 295 1 1 0 0 0 0 0 296 1 0 1 0 0 0 0 297 1 0 0 1 0 0 0 298 1 0 0 0 1 0 0 299 1 0 0 0 0 1 0 300 1 0 0 0 0 0 1 301 1 1 0 0 0 0 0 302 1 0 1 0 0 0 0 303 1 0 0 1 0 0 0 304 1 0 0 0 1 0 0 305 1 0 0 0 0 1 0 306 1 0 0 0 0 0 1 307 1 1 0 0 0 0 0 308 1 0 1 0 0 0 0 309 1 0 0 1 0 0 0 310 1 0 0 0 1 0 0 311 1 0 0 0 0 1 0 312 1 0 0 0 0 0 1 313 1 1 0 0 0 0 0 314 1 0 1 0 0 0 0 315 1 0 0 1 0 0 0 316 1 0 0 0 1 0 0 317 1 0 0 0 0 1 0 318 1 0 0 0 0 0 1 319 1 1 0 0 0 0 0 320 1 0 1 0 0 0 0 321 1 0 0 1 0 0 0 322 1 0 0 0 1 0 0 323 1 0 0 0 0 1 0 324 1 0 0 0 0 0 1 325 1 1 0 0 0 0 0 326 1 0 1 0 0 0 0 327 1 0 0 1 0 0 0 328 1 0 0 0 1 0 0 329 1 0 0 0 0 1 0 330 1 0 0 0 0 0 1 331 1 1 0 0 0 0 0 332 1 0 1 0 0 0 0 333 1 0 0 1 0 0 0 334 1 0 0 0 1 0 0 335 1 0 0 0 0 1 0 336 1 0 0 0 0 0 1 337 1 1 0 0 0 0 0 338 1 0 1 0 0 0 0 339 1 0 0 1 0 0 0 340 1 0 0 0 1 0 0 341 1 0 0 0 0 1 0 342 1 0 0 0 0 0 1 343 1 1 0 0 0 0 0 344 1 0 1 0 0 0 0 345 1 0 0 1 0 0 0 346 1 0 0 0 1 0 0 347 1 0 0 0 0 1 0 348 1 0 0 0 0 0 1 349 1 1 0 0 0 0 0 350 1 0 1 0 0 0 0 351 1 0 0 1 0 0 0 352 1 0 0 0 1 0 0 353 1 0 0 0 0 1 0 354 1 0 0 0 0 0 1 355 1 1 0 0 0 0 0 356 1 0 1 0 0 0 0 357 1 0 0 1 0 0 0 358 1 0 0 0 1 0 0 359 1 0 0 0 0 1 0 360 1 0 0 0 0 0 1 attr(,"assign") [1] 0 1 1 1 1 1 1 attr(,"contrasts") attr(,"contrasts")$factor(mat) [1] "contr.treatment" This does not work" > fit<-lmFit(sponge_ExpressionSet,design) Error in lm.fit(design, t(M)) : incompatible dimensions > exprs(sponge_data_matrix)->spongeExprs Error in (function (classes, fdef, mtable) : unable to find an inherited method for function exprs for signature "matrix" My ExpressionSet is built from scratch: > sponge_ExpressionSet<-new("ExpressionSet",exprs=sponge_data_matrix,phe noData=pd,experimentData=experimentData,featureData=an) > sponge_ExpressionSet ExpressionSet (storageMode: lockedEnvironment) assayData: 15744 features, 72 samples element names: exprs protocolData: none phenoData sampleNames: 1_1 1_2 ... 9_8 (72 total) varLabels: Chip.Number File.Name ... percentlessthan0 (12 total) varMetadata: labelDescription featureData featureNames: 1 2 ... 15744 (15744 total) fvarLabels: Column Row ... X.1 (23 total) fvarMetadata: labelDescription experimentData: use 'experimentData(object)' Annotation: [[alternative HTML version deleted]] microarray limma • 927 views ADD COMMENTlink modified 5.8 years ago by Ryan C. Thompson7.3k • written 5.8 years ago by Lisa Cohen50 Answer: help with limma design, contrast matrices 0 5.8 years ago by The Scripps Research Institute, La Jolla, CA Ryan C. Thompson7.3k wrote: Your design should be a data frame with two columns: a "treatment" column that is a factor with 4 levels, and a "colony" column that is a factor with 6 levels. Let's call this data frame "df". You can then make your design matrix like this: design <- model.matrix(~ treatment + colony, df) assuming that you are treating "colony" as a blocking factor. You can try other formulas as well, but putting your experimental design as a data frame with two factor columns gives you the best representation of the design. -Ryan On Thu 31 Oct 2013 02:23:42 PM PDT, Lisa Cohen wrote: > I have a set of microarray data (one-channel custom Agilent) that I'm > trying to analyze for gene expression differences following an experiment: > > Sponges were acutely exposed to combinations of oil and dispersant > treatments. There were 4 treatment groups: OD, OC, UD, UC, 6 sponge > colonies each fragmented 12 times, with three replicates = 72 samples total. > > I found some examples from the limma user's guide and other materials, but > I'm still having trouble. > http://www.bioconductor.org/help/course- materials/2009/BioC2009/labs/limma/limma.pdf > https://stat.ethz.ch/pipermail/bioconductor/2012-January/043154.html > http://www.bioconductor.org/packages/2.12/bioc/vignettes/limma/inst/ doc/usersguide.pdf > > In coding my contrast and design matrices, I'm confused and wondering if > someone can help? > > Details and code below. > > Thank you in advance! > > Lisa > > > > > This is the design matrix I set up: > >> v<-c(0,1) >> mat<-cbind(c(rep(v[2],18),rep(v[1],54)), > + c(rep(v[1],18),rep(v[2],18),rep(v[1],36)), > + c(rep(v[1],36),rep(v[2],18),rep(v[1],18)), > + c(rep(v[1],54),rep(v[2],18)), > + c(rep(1:6,12))) >> colnames(mat)<-c("UC","UD","OC","OD","Colony") > >> mat > UC UD OC OD Colony > [1,] 1 0 0 0 1 > [2,] 1 0 0 0 2 > [3,] 1 0 0 0 3 > [4,] 1 0 0 0 4 > [5,] 1 0 0 0 5 > [6,] 1 0 0 0 6 > [7,] 1 0 0 0 1 > [8,] 1 0 0 0 2 > [9,] 1 0 0 0 3 > [10,] 1 0 0 0 4 > [11,] 1 0 0 0 5 > [12,] 1 0 0 0 6 > [13,] 1 0 0 0 1 > [14,] 1 0 0 0 2 > [15,] 1 0 0 0 3 > [16,] 1 0 0 0 4 > [17,] 1 0 0 0 5 > [18,] 1 0 0 0 6 > [19,] 0 1 0 0 1 > [20,] 0 1 0 0 2 > [21,] 0 1 0 0 3 > [22,] 0 1 0 0 4 > [23,] 0 1 0 0 5 > [24,] 0 1 0 0 6 > [25,] 0 1 0 0 1 > [26,] 0 1 0 0 2 > [27,] 0 1 0 0 3 > [28,] 0 1 0 0 4 > [29,] 0 1 0 0 5 > [30,] 0 1 0 0 6 > [31,] 0 1 0 0 1 > [32,] 0 1 0 0 2 > [33,] 0 1 0 0 3 > [34,] 0 1 0 0 4 > [35,] 0 1 0 0 5 > [36,] 0 1 0 0 6 > [37,] 0 0 1 0 1 > [38,] 0 0 1 0 2 > [39,] 0 0 1 0 3 > [40,] 0 0 1 0 4 > [41,] 0 0 1 0 5 > [42,] 0 0 1 0 6 > [43,] 0 0 1 0 1 > [44,] 0 0 1 0 2 > [45,] 0 0 1 0 3 > [46,] 0 0 1 0 4 > [47,] 0 0 1 0 5 > [48,] 0 0 1 0 6 > [49,] 0 0 1 0 1 > [50,] 0 0 1 0 2 > [51,] 0 0 1 0 3 > [52,] 0 0 1 0 4 > [53,] 0 0 1 0 5 > [54,] 0 0 1 0 6 > [55,] 0 0 0 1 1 > [56,] 0 0 0 1 2 > [57,] 0 0 0 1 3 > [58,] 0 0 0 1 4 > [59,] 0 0 0 1 5 > [60,] 0 0 0 1 6 > [61,] 0 0 0 1 1 > [62,] 0 0 0 1 2 > [63,] 0 0 0 1 3 > [64,] 0 0 0 1 4 > [65,] 0 0 0 1 5 > [66,] 0 0 0 1 6 > [67,] 0 0 0 1 1 > [68,] 0 0 0 1 2 > [69,] 0 0 0 1 3 > [70,] 0 0 0 1 4 > [71,] 0 0 0 1 5 > [72,] 0 0 0 1 6 > > What is the role of the contrast matrix? > > When I set up model.matrix(), there are too many comparisons: > >> design<-model.matrix(~factor(mat)) >> design > (Intercept) factor(mat)1 factor(mat)2 factor(mat)3 factor(mat)4 > factor(mat)5 factor(mat)6 > 1 1 1 0 0 > 0 0 0 > 2 1 1 0 0 > 0 0 0 > 3 1 1 0 0 > 0 0 0 > 4 1 1 0 0 > 0 0 0 > 5 1 1 0 0 > 0 0 0 > 6 1 1 0 0 > 0 0 0 > 7 1 1 0 0 > 0 0 0 > 8 1 1 0 0 > 0 0 0 > 9 1 1 0 0 > 0 0 0 > 10 1 1 0 0 > 0 0 0 > 11 1 1 0 0 > 0 0 0 > 12 1 1 0 0 > 0 0 0 > 13 1 1 0 0 > 0 0 0 > 14 1 1 0 0 > 0 0 0 > 15 1 1 0 0 > 0 0 0 > 16 1 1 0 0 > 0 0 0 > 17 1 1 0 0 > 0 0 0 > 18 1 1 0 0 > 0 0 0 > 19 1 0 0 0 > 0 0 0 > 20 1 0 0 0 > 0 0 0 > 21 1 0 0 0 > 0 0 0 > 22 1 0 0 0 > 0 0 0 > 23 1 0 0 0 > 0 0 0 > 24 1 0 0 0 > 0 0 0 > 25 1 0 0 0 > 0 0 0 > 26 1 0 0 0 > 0 0 0 > 27 1 0 0 0 > 0 0 0 > 28 1 0 0 0 > 0 0 0 > 29 1 0 0 0 > 0 0 0 > 30 1 0 0 0 > 0 0 0 > 31 1 0 0 0 > 0 0 0 > 32 1 0 0 0 > 0 0 0 > 33 1 0 0 0 > 0 0 0 > 34 1 0 0 0 > 0 0 0 > 35 1 0 0 0 > 0 0 0 > 36 1 0 0 0 > 0 0 0 > 37 1 0 0 0 > 0 0 0 > 38 1 0 0 0 > 0 0 0 > 39 1 0 0 0 > 0 0 0 > 40 1 0 0 0 > 0 0 0 > 41 1 0 0 0 > 0 0 0 > 42 1 0 0 0 > 0 0 0 > 43 1 0 0 0 > 0 0 0 > 44 1 0 0 0 > 0 0 0 > 45 1 0 0 0 > 0 0 0 > 46 1 0 0 0 > 0 0 0 > 47 1 0 0 0 > 0 0 0 > 48 1 0 0 0 > 0 0 0 > 49 1 0 0 0 > 0 0 0 > 50 1 0 0 0 > 0 0 0 > 51 1 0 0 0 > 0 0 0 > 52 1 0 0 0 > 0 0 0 > 53 1 0 0 0 > 0 0 0 > 54 1 0 0 0 > 0 0 0 > 55 1 0 0 0 > 0 0 0 > 56 1 0 0 0 > 0 0 0 > 57 1 0 0 0 > 0 0 0 > 58 1 0 0 0 > 0 0 0 > 59 1 0 0 0 > 0 0 0 > 60 1 0 0 0 > 0 0 0 > 61 1 0 0 0 > 0 0 0 > 62 1 0 0 0 > 0 0 0 > 63 1 0 0 0 > 0 0 0 > 64 1 0 0 0 > 0 0 0 > 65 1 0 0 0 > 0 0 0 > 66 1 0 0 0 > 0 0 0 > 67 1 0 0 0 > 0 0 0 > 68 1 0 0 0 > 0 0 0 > 69 1 0 0 0 > 0 0 0 > 70 1 0 0 0 > 0 0 0 > 71 1 0 0 0 > 0 0 0 > 72 1 0 0 0 > 0 0 0 > 73 1 0 0 0 > 0 0 0 > 74 1 0 0 0 > 0 0 0 > 75 1 0 0 0 > 0 0 0 > 76 1 0 0 0 > 0 0 0 > 77 1 0 0 0 > 0 0 0 > 78 1 0 0 0 > 0 0 0 > 79 1 0 0 0 > 0 0 0 > 80 1 0 0 0 > 0 0 0 > 81 1 0 0 0 > 0 0 0 > 82 1 0 0 0 > 0 0 0 > 83 1 0 0 0 > 0 0 0 > 84 1 0 0 0 > 0 0 0 > 85 1 0 0 0 > 0 0 0 > 86 1 0 0 0 > 0 0 0 > 87 1 0 0 0 > 0 0 0 > 88 1 0 0 0 > 0 0 0 > 89 1 0 0 0 > 0 0 0 > 90 1 0 0 0 > 0 0 0 > 91 1 1 0 0 > 0 0 0 > 92 1 1 0 0 > 0 0 0 > 93 1 1 0 0 > 0 0 0 > 94 1 1 0 0 > 0 0 0 > 95 1 1 0 0 > 0 0 0 > 96 1 1 0 0 > 0 0 0 > 97 1 1 0 0 > 0 0 0 > 98 1 1 0 0 > 0 0 0 > 99 1 1 0 0 > 0 0 0 > 100 1 1 0 0 > 0 0 0 > 101 1 1 0 0 > 0 0 0 > 102 1 1 0 0 > 0 0 0 > 103 1 1 0 0 > 0 0 0 > 104 1 1 0 0 > 0 0 0 > 105 1 1 0 0 > 0 0 0 > 106 1 1 0 0 > 0 0 0 > 107 1 1 0 0 > 0 0 0 > 108 1 1 0 0 > 0 0 0 > 109 1 0 0 0 > 0 0 0 > 110 1 0 0 0 > 0 0 0 > 111 1 0 0 0 > 0 0 0 > 112 1 0 0 0 > 0 0 0 > 113 1 0 0 0 > 0 0 0 > 114 1 0 0 0 > 0 0 0 > 115 1 0 0 0 > 0 0 0 > 116 1 0 0 0 > 0 0 0 > 117 1 0 0 0 > 0 0 0 > 118 1 0 0 0 > 0 0 0 > 119 1 0 0 0 > 0 0 0 > 120 1 0 0 0 > 0 0 0 > 121 1 0 0 0 > 0 0 0 > 122 1 0 0 0 > 0 0 0 > 123 1 0 0 0 > 0 0 0 > 124 1 0 0 0 > 0 0 0 > 125 1 0 0 0 > 0 0 0 > 126 1 0 0 0 > 0 0 0 > 127 1 0 0 0 > 0 0 0 > 128 1 0 0 0 > 0 0 0 > 129 1 0 0 0 > 0 0 0 > 130 1 0 0 0 > 0 0 0 > 131 1 0 0 0 > 0 0 0 > 132 1 0 0 0 > 0 0 0 > 133 1 0 0 0 > 0 0 0 > 134 1 0 0 0 > 0 0 0 > 135 1 0 0 0 > 0 0 0 > 136 1 0 0 0 > 0 0 0 > 137 1 0 0 0 > 0 0 0 > 138 1 0 0 0 > 0 0 0 > 139 1 0 0 0 > 0 0 0 > 140 1 0 0 0 > 0 0 0 > 141 1 0 0 0 > 0 0 0 > 142 1 0 0 0 > 0 0 0 > 143 1 0 0 0 > 0 0 0 > 144 1 0 0 0 > 0 0 0 > 145 1 0 0 0 > 0 0 0 > 146 1 0 0 0 > 0 0 0 > 147 1 0 0 0 > 0 0 0 > 148 1 0 0 0 > 0 0 0 > 149 1 0 0 0 > 0 0 0 > 150 1 0 0 0 > 0 0 0 > 151 1 0 0 0 > 0 0 0 > 152 1 0 0 0 > 0 0 0 > 153 1 0 0 0 > 0 0 0 > 154 1 0 0 0 > 0 0 0 > 155 1 0 0 0 > 0 0 0 > 156 1 0 0 0 > 0 0 0 > 157 1 0 0 0 > 0 0 0 > 158 1 0 0 0 > 0 0 0 > 159 1 0 0 0 > 0 0 0 > 160 1 0 0 0 > 0 0 0 > 161 1 0 0 0 > 0 0 0 > 162 1 0 0 0 > 0 0 0 > 163 1 0 0 0 > 0 0 0 > 164 1 0 0 0 > 0 0 0 > 165 1 0 0 0 > 0 0 0 > 166 1 0 0 0 > 0 0 0 > 167 1 0 0 0 > 0 0 0 > 168 1 0 0 0 > 0 0 0 > 169 1 0 0 0 > 0 0 0 > 170 1 0 0 0 > 0 0 0 > 171 1 0 0 0 > 0 0 0 > 172 1 0 0 0 > 0 0 0 > 173 1 0 0 0 > 0 0 0 > 174 1 0 0 0 > 0 0 0 > 175 1 0 0 0 > 0 0 0 > 176 1 0 0 0 > 0 0 0 > 177 1 0 0 0 > 0 0 0 > 178 1 0 0 0 > 0 0 0 > 179 1 0 0 0 > 0 0 0 > 180 1 0 0 0 > 0 0 0 > 181 1 1 0 0 > 0 0 0 > 182 1 1 0 0 > 0 0 0 > 183 1 1 0 0 > 0 0 0 > 184 1 1 0 0 > 0 0 0 > 185 1 1 0 0 > 0 0 0 > 186 1 1 0 0 > 0 0 0 > 187 1 1 0 0 > 0 0 0 > 188 1 1 0 0 > 0 0 0 > 189 1 1 0 0 > 0 0 0 > 190 1 1 0 0 > 0 0 0 > 191 1 1 0 0 > 0 0 0 > 192 1 1 0 0 > 0 0 0 > 193 1 1 0 0 > 0 0 0 > 194 1 1 0 0 > 0 0 0 > 195 1 1 0 0 > 0 0 0 > 196 1 1 0 0 > 0 0 0 > 197 1 1 0 0 > 0 0 0 > 198 1 1 0 0 > 0 0 0 > 199 1 0 0 0 > 0 0 0 > 200 1 0 0 0 > 0 0 0 > 201 1 0 0 0 > 0 0 0 > 202 1 0 0 0 > 0 0 0 > 203 1 0 0 0 > 0 0 0 > 204 1 0 0 0 > 0 0 0 > 205 1 0 0 0 > 0 0 0 > 206 1 0 0 0 > 0 0 0 > 207 1 0 0 0 > 0 0 0 > 208 1 0 0 0 > 0 0 0 > 209 1 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0 0 > 255 1 0 0 0 > 0 0 0 > 256 1 0 0 0 > 0 0 0 > 257 1 0 0 0 > 0 0 0 > 258 1 0 0 0 > 0 0 0 > 259 1 0 0 0 > 0 0 0 > 260 1 0 0 0 > 0 0 0 > 261 1 0 0 0 > 0 0 0 > 262 1 0 0 0 > 0 0 0 > 263 1 0 0 0 > 0 0 0 > 264 1 0 0 0 > 0 0 0 > 265 1 0 0 0 > 0 0 0 > 266 1 0 0 0 > 0 0 0 > 267 1 0 0 0 > 0 0 0 > 268 1 0 0 0 > 0 0 0 > 269 1 0 0 0 > 0 0 0 > 270 1 0 0 0 > 0 0 0 > 271 1 1 0 0 > 0 0 0 > 272 1 1 0 0 > 0 0 0 > 273 1 1 0 0 > 0 0 0 > 274 1 1 0 0 > 0 0 0 > 275 1 1 0 0 > 0 0 0 > 276 1 1 0 0 > 0 0 0 > 277 1 1 0 0 > 0 0 0 > 278 1 1 0 0 > 0 0 0 > 279 1 1 0 0 > 0 0 0 > 280 1 1 0 0 > 0 0 0 > 281 1 1 0 0 > 0 0 0 > 282 1 1 0 0 > 0 0 0 > 283 1 1 0 0 > 0 0 0 > 284 1 1 0 0 > 0 0 0 > 285 1 1 0 0 > 0 0 0 > 286 1 1 0 0 > 0 0 0 > 287 1 1 0 0 > 0 0 0 > 288 1 1 0 0 > 0 0 0 > 289 1 1 0 0 > 0 0 0 > 290 1 0 1 0 > 0 0 0 > 291 1 0 0 1 > 0 0 0 > 292 1 0 0 0 > 1 0 0 > 293 1 0 0 0 > 0 1 0 > 294 1 0 0 0 > 0 0 1 > 295 1 1 0 0 > 0 0 0 > 296 1 0 1 0 > 0 0 0 > 297 1 0 0 1 > 0 0 0 > 298 1 0 0 0 > 1 0 0 > 299 1 0 0 0 > 0 1 0 > 300 1 0 0 0 > 0 0 1 > 301 1 1 0 0 > 0 0 0 > 302 1 0 1 0 > 0 0 0 > 303 1 0 0 1 > 0 0 0 > 304 1 0 0 0 > 1 0 0 > 305 1 0 0 0 > 0 1 0 > 306 1 0 0 0 > 0 0 1 > 307 1 1 0 0 > 0 0 0 > 308 1 0 1 0 > 0 0 0 > 309 1 0 0 1 > 0 0 0 > 310 1 0 0 0 > 1 0 0 > 311 1 0 0 0 > 0 1 0 > 312 1 0 0 0 > 0 0 1 > 313 1 1 0 0 > 0 0 0 > 314 1 0 1 0 > 0 0 0 > 315 1 0 0 1 > 0 0 0 > 316 1 0 0 0 > 1 0 0 > 317 1 0 0 0 > 0 1 0 > 318 1 0 0 0 > 0 0 1 > 319 1 1 0 0 > 0 0 0 > 320 1 0 1 0 > 0 0 0 > 321 1 0 0 1 > 0 0 0 > 322 1 0 0 0 > 1 0 0 > 323 1 0 0 0 > 0 1 0 > 324 1 0 0 0 > 0 0 1 > 325 1 1 0 0 > 0 0 0 > 326 1 0 1 0 > 0 0 0 > 327 1 0 0 1 > 0 0 0 > 328 1 0 0 0 > 1 0 0 > 329 1 0 0 0 > 0 1 0 > 330 1 0 0 0 > 0 0 1 > 331 1 1 0 0 > 0 0 0 > 332 1 0 1 0 > 0 0 0 > 333 1 0 0 1 > 0 0 0 > 334 1 0 0 0 > 1 0 0 > 335 1 0 0 0 > 0 1 0 > 336 1 0 0 0 > 0 0 1 > 337 1 1 0 0 > 0 0 0 > 338 1 0 1 0 > 0 0 0 > 339 1 0 0 1 > 0 0 0 > 340 1 0 0 0 > 1 0 0 > 341 1 0 0 0 > 0 1 0 > 342 1 0 0 0 > 0 0 1 > 343 1 1 0 0 > 0 0 0 > 344 1 0 1 0 > 0 0 0 > 345 1 0 0 1 > 0 0 0 > 346 1 0 0 0 > 1 0 0 > 347 1 0 0 0 > 0 1 0 > 348 1 0 0 0 > 0 0 1 > 349 1 1 0 0 > 0 0 0 > 350 1 0 1 0 > 0 0 0 > 351 1 0 0 1 > 0 0 0 > 352 1 0 0 0 > 1 0 0 > 353 1 0 0 0 > 0 1 0 > 354 1 0 0 0 > 0 0 1 > 355 1 1 0 0 > 0 0 0 > 356 1 0 1 0 > 0 0 0 > 357 1 0 0 1 > 0 0 0 > 358 1 0 0 0 > 1 0 0 > 359 1 0 0 0 > 0 1 0 > 360 1 0 0 0 > 0 0 1 > attr(,"assign") > [1] 0 1 1 1 1 1 1 > attr(,"contrasts") > attr(,"contrasts")$factor(mat) > [1] "contr.treatment" > > This does not work" > >> fit<-lmFit(sponge_ExpressionSet,design) > Error in lm.fit(design, t(M)) : incompatible dimensions >> exprs(sponge_data_matrix)->spongeExprs > Error in (function (classes, fdef, mtable) : > unable to find an inherited method for function ?exprs? for signature > ?"matrix"? > > > My ExpressionSet is built from scratch: > >> > sponge_ExpressionSet<-new("ExpressionSet",exprs=sponge_data_matrix,p henoData=pd,experimentData=experimentData,featureData=an) >> sponge_ExpressionSet > ExpressionSet (storageMode: lockedEnvironment) > assayData: 15744 features, 72 samples > element names: exprs > protocolData: none > phenoData > sampleNames: 1_1 1_2 ... 9_8 (72 total) > varLabels: Chip.Number File.Name ... percentlessthan0 (12 total) > varMetadata: labelDescription > featureData > featureNames: 1 2 ... 15744 (15744 total) > fvarLabels: Column Row ... X.1 (23 total) > fvarMetadata: labelDescription > experimentData: use 'experimentData(object)' > Annotation: > > [[alternative HTML version deleted]] > > > > _______________________________________________ > Bioconductor mailing list > Bioconductor at r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor
ADD COMMENTlink written 5.8 years ago by Ryan C. Thompson7.3k
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