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 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,phenoData=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]]
>
>
>
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