How to: Extract probeset summaries (chip effects) as a data frame
Chip effects can be read as a data frame by calling:
data <- extractDataFrame(ces, addNames=TRUE)
The first column is always the units, and the last columns are always the arrays.
Note that this will load all data requested into memory.
Example: HG-U133_Plus_2
ces <- getChipEffectSet(plm)
print(ces)
## ChipEffectSet:
## Name: Affymetrix-HeartBrain
## Tags: RBC,QN,RMA
## Path: plmData/Affymetrix-HeartBrain,RBC,QN,RMA/HG-U133_Plus_2
## Platform: Affymetrix
## Chip type: HG-U133_Plus_2,monocell
## Number of arrays: 3
## Names: heart_A, heart_B, heart_C
## Time period: 2009-08-12 22:21:59 -- 2009-08-12 22:21:59
## Total file size: 1.73MB
## RAM: 0.01MB
## Parameters: (probeModel: chr "pm")
data <- extractDataFrame(ces, units=1401:1406, addNames=TRUE)
print(data)
## unitName groupName unit group cell heart_A heart_B heart_C
## 1 201811_x_at 1401 1 1401 50.35944 50.97249 44.04873
## 2 201812_s_at 1402 1 1402 1247.74109 1182.60449 1151.22278
## 3 201813_s_at 1403 1 1403 83.30342 81.10368 69.86592
## 4 201814_at 1404 1 1404 56.92453 65.28267 54.34821
## 5 201815_s_at 1405 1 1405 23.72381 31.87461 27.95008
## 6 201816_s_at 1406 1 1406 343.54056 326.27948 272.80453
Example: HuEx-1_0-st-v2
Illustration: Different number of exons (groups/probesets) per gene (unit).
ces <- getChipEffectSet(plm)
print(ces)
## ExonChipEffectSet:
## Name: Affymetrix-HeartBrain
## Tags: RBC,QN,RMA
## Path: plmData/Affymetrix-HeartBrain,RBC,QN,RMA/HuEx-1_0-st-v2
## Platform: Affymetrix
## Chip type: HuEx-1_0-st-v2,coreR3,A20071112,EP,monocell
## Number of arrays: 3
## Names: cerebellum_A, cerebellum_B, cerebellum_C
## Time period: 2009-10-05 23:54:34 -- 2009-10-05 23:54:34
## Total file size: 8.15MB
## RAM: 0.01MB
## Parameters: (probeModel: chr "pm", mergeGroups: logi FALSE)
data <- extractDataFrame(ces, units=101:103, addNames=TRUE)
print(data)
## unitName groupName unit group cell cerebellum_A cerebellum_B cerebellum_C
## 1 2323743 2323744 101 1 1679 4.9773 3.2216 4.6035
## 2 2323743 2323745 101 2 1680 28.0082 33.4840 24.9815
## 3 2323743 2323746 101 3 1681 81.8853 84.7152 90.6801
## 4 2323743 2323747 101 4 1682 59.3300 78.0417 101.1745
## 5 2323743 2323748 101 5 1683 11.2939 26.5029 22.1576
## 6 2323743 2323749 101 6 1684 23.0034 20.3482 19.2598
## 7 2323774 2323775 102 1 1685 9.7513 8.6798 8.3373
## 8 2323774 2323776 102 2 1686 126.7435 169.5813 157.2981
## 9 2323774 2323777 102 3 1687 76.9862 111.4743 79.5220
## 10 2323774 2323778 102 4 1688 75.9090 77.9127 56.6610
## 11 2323774 2323779 102 5 1689 39.8756 52.8681 47.4996
## 12 2323774 2323780 102 6 1690 21.5192 38.1660 36.6005
## 13 2323774 2323781 102 7 1691 69.1406 83.8591 97.5679
## 14 2323774 2323782 102 8 1692 68.3016 83.6700 80.9289
## 15 2323774 2323783 102 9 1693 3.0645 8.2662 8.9555
## 16 2323790 2323791 103 1 1694 8.0941 10.9351 10.2943
## 17 2323790 2323792 103 2 1695 45.5133 59.4548 85.2261
## 18 2323790 2323793 103 3 1696 13.2537 24.0905 11.6845
## 19 2323790 2323798 103 4 1697 12.4343 15.8386 12.4253
## 20 2323790 2323799 103 5 1698 6.8943 7.3441 5.2567
Example: GenomeWideSNP_6
Illustration: Each SNP (unit) has (thetaA, thetaB) groups and each CN probe (unit) has only a theta group.
ces <- getChipEffectSet(plm)
print(ces)
## CnChipEffectSet:
## Name: HapMap270
## Tags: 6.0,CEU,testSet,ACC,ra,-XY,BPN,-XY,AVG
## Path:
## plmData/HapMap270,6.0,CEU,testSet,ACC,ra,-XY,BPN,-XY,AVG/GenomeWideSNP_6
## Platform: Affymetrix
## Chip type: GenomeWideSNP_6,Full,monocell
## Number of arrays: 3
## Names: NA06985, NA06991, NA06993
## Time period: 2009-10-17 00:49:05 -- 2009-10-17 00:49:05
## Total file size: 80.85MB
## RAM: 0.01MB
## Parameters: (probeModel: chr "pm", mergeStrands: logi TRUE,
## combineAlleles: logi FALSE)
data <- extractDataFrame(ces, units=c(2101:2104,935590:935594), addNames=TRUE)
print(data)
## unitName groupName unit group cell NA06985 NA06991 NA06993
## 1 SNP_A-2228193 A 2101 1 3580 4518.85 4922.25 5225.94
## 2 SNP_A-2228193 T 2101 2 3581 522.05 522.58 725.81
## 3 SNP_A-4234307 A 2102 1 3582 9581.92 5727.58 1274.12
## 4 SNP_A-4234307 G 2102 2 3583 503.43 2966.96 6061.83
## 5 SNP_A-2229035 C 2103 1 3584 420.19 4732.66 8462.53
## 6 SNP_A-2229035 T 2103 2 3585 7162.17 4731.73 207.93
## 7 SNP_A-2229692 A 2104 1 3586 768.96 1477.57 1134.00
## 8 SNP_A-2229692 G 2104 2 3587 1117.02 358.71 419.36
## 9 CN_477984 935590 1 1876054 8724.15 7612.26 8564.70
## 10 CN_473963 935591 1 1876055 7947.55 5970.75 6425.55
## 11 CN_473964 935592 1 1876056 12863.29 12642.58 12712.78
## 12 CN_473965 935593 1 1876057 14373.10 11287.04 10031.57
## 13 CN_497981 935594 1 1876058 23424.00 26139.35 17312.84