Simulated Data for TcGSA
data_simu_TcGSA.RdSimulated data for 5 gene sets of 50 genes each. Gene expression is simulated at 5 time points for 10 patients.
Usage
data(data_simu_TcGSA)Value
- expr_1grp
See Details.
- expr_2grp
See Details.
- design
a data frame with 5 variables:
Patient_ID: a factor that contains the patient ID.TimePoint: a numeric vector or a factor that contains the time points at which gene expression was measured.sample_name: a character vector with the names of the sample (corresponding to the names of the columns ofexpr_1grpand ofexpr_2grp).group.var: a factor that indicates to which of the 2 treatment groups each sample belongs to.Group_paired_IDa random paired identifier for paired couples (one in each of the 2 treatment groups) of patients.
- gmt_sim
a gmt object containing the gene sets definition. See
GSA.read.gmtand GMT definition on www.broadinstitute.org.
Details
In expr_1grp all patients belong to the same unique treatment group. The first 2 gene sets are simulated under the null hypothesis. The gene sets 3 and 4 are simulated under the alternative hypothesis that there is a significant homogeneous time trend within the gene set. The gene set 5 is simulated under the alternative hypothesis that there are significant heterogeneous time trends within the gene set.
In expr_2grp all patients belong to 2 treatment groups. The 5 first patients belong to the treatment group 'T', The 5 other patients belong to the treatment group 'C'. The first 2 gene sets are simulated under the null hypothesis that there is no difference in the time trend between the 2 treatment groups. The gene sets 3 and 4 are simulated under the alternative hypothesis that there are significantly different homogeneous time trends within the gene set between the 2 treatment groups. The gene set 5 is simulated under the alternative hypothesis that there are significantly different heterogeneous time trends between the 2 treatment groups within the gene set.
Examples
data(data_simu_TcGSA)
summary(expr_1grp)
#> 1 2 3 4
#> Min. : 3.205 Min. : 3.309 Min. : 3.367 Min. : 3.330
#> 1st Qu.: 5.251 1st Qu.: 5.299 1st Qu.: 5.184 1st Qu.: 5.090
#> Median : 6.256 Median : 6.300 Median : 6.172 Median : 6.156
#> Mean : 6.420 Mean : 6.481 Mean : 6.413 Mean : 6.407
#> 3rd Qu.: 7.524 3rd Qu.: 7.600 3rd Qu.: 7.604 3rd Qu.: 7.674
#> Max. :11.231 Max. :10.798 Max. :10.278 Max. :11.288
#> 5 6 7 8
#> Min. : 3.234 Min. : 3.363 Min. : 3.399 Min. : 3.427
#> 1st Qu.: 4.977 1st Qu.: 5.233 1st Qu.: 5.270 1st Qu.: 5.201
#> Median : 6.048 Median : 6.279 Median : 6.247 Median : 6.149
#> Mean : 6.400 Mean : 6.483 Mean : 6.442 Mean : 6.468
#> 3rd Qu.: 7.927 3rd Qu.: 7.734 3rd Qu.: 7.689 3rd Qu.: 7.761
#> Max. :10.522 Max. :10.278 Max. :10.181 Max. :10.172
#> 9 10 11 12
#> Min. : 3.395 Min. : 3.172 Min. : 3.395 Min. : 3.442
#> 1st Qu.: 5.177 1st Qu.: 5.033 1st Qu.: 5.193 1st Qu.: 5.198
#> Median : 6.121 Median : 5.998 Median : 6.218 Median : 6.246
#> Mean : 6.458 Mean : 6.455 Mean : 6.368 Mean : 6.406
#> 3rd Qu.: 7.800 3rd Qu.: 7.858 3rd Qu.: 7.610 3rd Qu.: 7.705
#> Max. :10.271 Max. :10.351 Max. :10.004 Max. :10.384
#> 13 14 15 16
#> Min. : 3.415 Min. : 3.416 Min. : 3.251 Min. : 3.349
#> 1st Qu.: 5.162 1st Qu.: 5.021 1st Qu.: 4.867 1st Qu.: 5.401
#> Median : 6.094 Median : 6.102 Median : 5.923 Median : 6.287
#> Mean : 6.373 Mean : 6.380 Mean : 6.357 Mean : 6.468
#> 3rd Qu.: 7.667 3rd Qu.: 7.707 3rd Qu.: 7.802 3rd Qu.: 7.617
#> Max. :11.174 Max. :10.410 Max. :10.164 Max. :10.382
#> 17 18 19 20
#> Min. : 3.375 Min. : 2.718 Min. : 3.398 Min. : 3.334
#> 1st Qu.: 5.348 1st Qu.: 5.275 1st Qu.: 5.187 1st Qu.: 5.168
#> Median : 6.310 Median : 6.253 Median : 6.227 Median : 6.171
#> Mean : 6.438 Mean : 6.430 Mean : 6.413 Mean : 6.434
#> 3rd Qu.: 7.617 3rd Qu.: 7.609 3rd Qu.: 7.705 3rd Qu.: 7.755
#> Max. :10.239 Max. :10.525 Max. :10.353 Max. :10.352
#> 21 22 23 24
#> Min. : 3.210 Min. : 3.281 Min. : 3.260 Min. : 3.319
#> 1st Qu.: 5.263 1st Qu.: 5.274 1st Qu.: 5.151 1st Qu.: 5.096
#> Median : 6.347 Median : 6.321 Median : 6.239 Median : 6.206
#> Mean : 6.498 Mean : 6.518 Mean : 6.481 Mean : 6.480
#> 3rd Qu.: 7.884 3rd Qu.: 7.891 3rd Qu.: 7.708 3rd Qu.: 7.939
#> Max. :10.680 Max. :10.096 Max. :11.141 Max. :10.521
#> 25 26 27 28
#> Min. : 3.189 Min. : 3.317 Min. : 3.372 Min. : 3.359
#> 1st Qu.: 5.051 1st Qu.: 5.362 1st Qu.: 5.279 1st Qu.: 5.269
#> Median : 6.078 Median : 6.388 Median : 6.294 Median : 6.240
#> Mean : 6.480 Mean : 6.476 Mean : 6.447 Mean : 6.465
#> 3rd Qu.: 7.916 3rd Qu.: 7.702 3rd Qu.: 7.682 3rd Qu.: 7.721
#> Max. :10.652 Max. :10.468 Max. :10.251 Max. :10.418
#> 29 30 31 32
#> Min. : 3.429 Min. : 3.287 Min. : 3.339 Min. : 3.350
#> 1st Qu.: 5.197 1st Qu.: 5.055 1st Qu.: 5.223 1st Qu.: 5.212
#> Median : 6.136 Median : 6.078 Median : 6.158 Median : 6.258
#> Mean : 6.442 Mean : 6.440 Mean : 6.364 Mean : 6.388
#> 3rd Qu.: 7.759 3rd Qu.: 7.826 3rd Qu.: 7.456 3rd Qu.: 7.498
#> Max. :10.677 Max. :11.181 Max. :10.621 Max. :10.629
#> 33 34 35 36
#> Min. : 3.412 Min. : 3.371 Min. : 3.219 Min. : 3.243
#> 1st Qu.: 5.181 1st Qu.: 5.007 1st Qu.: 5.070 1st Qu.: 5.306
#> Median : 6.171 Median : 6.093 Median : 5.971 Median : 6.283
#> Mean : 6.381 Mean : 6.335 Mean : 6.360 Mean : 6.441
#> 3rd Qu.: 7.606 3rd Qu.: 7.603 3rd Qu.: 7.612 3rd Qu.: 7.579
#> Max. :10.165 Max. :10.578 Max. :10.541 Max. :10.652
#> 37 38 39 40
#> Min. :3.354 Min. : 3.299 Min. : 3.321 Min. : 3.269
#> 1st Qu.:5.313 1st Qu.: 5.223 1st Qu.: 5.158 1st Qu.: 5.138
#> Median :6.191 Median : 6.219 Median : 6.185 Median : 6.067
#> Mean :6.432 Mean : 6.449 Mean : 6.459 Mean : 6.456
#> 3rd Qu.:7.684 3rd Qu.: 7.678 3rd Qu.: 7.720 3rd Qu.: 7.751
#> Max. :9.953 Max. :10.027 Max. :10.336 Max. :10.254
#> 41 42 43 44
#> Min. : 3.125 Min. : 3.188 Min. : 3.235 Min. : 3.218
#> 1st Qu.: 5.149 1st Qu.: 5.192 1st Qu.: 5.143 1st Qu.: 5.098
#> Median : 6.191 Median : 6.185 Median : 6.113 Median : 6.052
#> Mean : 6.381 Mean : 6.407 Mean : 6.377 Mean : 6.401
#> 3rd Qu.: 7.664 3rd Qu.: 7.656 3rd Qu.: 7.758 3rd Qu.: 7.809
#> Max. :10.375 Max. :10.268 Max. :10.265 Max. :10.283
#> 45 46 47 48
#> Min. : 3.223 Min. : 3.190 Min. : 3.264 Min. : 3.237
#> 1st Qu.: 4.916 1st Qu.: 5.212 1st Qu.: 5.264 1st Qu.: 5.102
#> Median : 6.037 Median : 6.260 Median : 6.234 Median : 6.174
#> Mean : 6.363 Mean : 6.378 Mean : 6.391 Mean : 6.404
#> 3rd Qu.: 7.856 3rd Qu.: 7.489 3rd Qu.: 7.482 3rd Qu.: 7.660
#> Max. :10.220 Max. :10.504 Max. :10.623 Max. :10.459
#> 49 50
#> Min. : 3.252 Min. : 3.247
#> 1st Qu.: 5.080 1st Qu.: 5.075
#> Median : 6.113 Median : 6.002
#> Mean : 6.397 Mean : 6.364
#> 3rd Qu.: 7.733 3rd Qu.: 7.839
#> Max. :10.596 Max. :10.508
summary(design)
#> Patient_ID TimePoint Sample_name group.var Group_ID_paired
#> P1 : 5 Min. :1 1 : 1 C:25 cpl1:10
#> P10 : 5 1st Qu.:2 10 : 1 T:25 cpl2:10
#> P2 : 5 Median :3 11 : 1 cpl3:10
#> P3 : 5 Mean :3 12 : 1 cpl4:10
#> P4 : 5 3rd Qu.:4 13 : 1 cpl5:10
#> P5 : 5 Max. :5 14 : 1
#> (Other):20 (Other):44
gmt_sim
#> $genesets
#> $genesets[[1]]
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
#> [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
#>
#> $genesets[[2]]
#> [1] 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69
#> [20] 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88
#> [39] 89 90 91 92 93 94 95 96 97 98 99 100
#>
#> $genesets[[3]]
#> [1] 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119
#> [20] 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138
#> [39] 139 140 141 142 143 144 145 146 147 148 149 150
#>
#> $genesets[[4]]
#> [1] 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169
#> [20] 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188
#> [39] 189 190 191 192 193 194 195 196 197 198 199 200
#>
#> $genesets[[5]]
#> [1] 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219
#> [20] 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238
#> [39] 239 240 241 242 243 244 245 246 247 248 249 250
#>
#>
#> $geneset.names
#> [1] "Gene set 1" "Gene set 2" "Gene set 3" "Gene set 4" "Gene set 5"
#>
#> $geneset.descriptions
#> [1] "This is a simulated gene set under H0"
#> [2] "This is a simulated gene set under H0"
#> [3] "This is a simulated gene set under H1 (homogeneous)"
#> [4] "This is a simulated gene set under H1 (homogeneous)"
#> [5] "This is a simulated gene set under H1 (heterogeneous)"
#>
#> attr(,"class")
#> [1] "GSA.genesets"