CLLmethylation
27 February 2020
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## aperm, apply, rowsumlibrary("ggplot2")1 Introduction
The resource CLLmethylation contains complete DNA methylation data for chronic lymphocytic leukemia (CLL) patient samples. The subset of this data (for only most variable CpG sites) and the rest of the datasets and analysis resulting from the PACE project is available in BloodCancerMultiOmics2017. All of the data mentioned above was used in the analysis, which results are included in:
S Dietrich*, M Oleś*, J Lu* et al. Drug-perturbation-based stratification of blood cancer
J. Clin. Invest. (2018); 128(1):427–445. doi:10.1172/JCI93801.
* equal contribution
The raw data from 450k DNA methylation arrays is stored in the European Genome-Phenome Archive (EGA) under accession number EGAS0000100174.
2 Example of analysis
This dataset in combination with the BloodCancerMultiOmics2017 package contain rich resource for nearly 200 CLL primary samples. Here we show simple principal component analysis for the DNA methylation data.
Obtain the data.
eh = ExperimentHub()## snapshotDate(): 2019-10-22query(eh, "CLLmethylation")## ExperimentHub with 1 record
## # snapshotDate(): 2019-10-22
## # names(): EH1071
## # package(): CLLmethylation
## # $dataprovider: European Molecular Biology Laboratory
## # $species: Homo sapiens
## # $rdataclass: RangedSummarizedExperiment
## # $rdatadateadded: 2018-02-02
## # $title: DNA methylation data of CLL primary samples
## # $description: The data was produced with the use of 450k and 850k methylat...
## # $taxonomyid: 9606
## # $genome: hg19
## # $sourcetype: IDAT
## # $sourceurl: https://wwwdev.ebi.ac.uk/ega/datasets/EGAD00010000948
## # $sourcesize: NA
## # $tags: c("ExperimentData", "DiseaseModel", "CancerData",
## # "LeukemiaCancerData")
## # retrieve record with 'object[["EH1071"]]'meth = eh[["EH1071"]] # extract the methylation data## see ?CLLmethylation and browseVignettes('CLLmethylation') for documentation## loading from cacheSubset most variable CpG sites.
methData = t(assay(meth))
#filter to only include top 5000 most variable sites
ntop = 5000
methData = methData[,order(apply(methData, 2, var, na.rm=TRUE),
decreasing=TRUE)[1:ntop]]Perform principal component analysis.
# principal component analysis
pcaMeth = prcomp(methData, center=TRUE, scale. = FALSE)Summary of components.
summary(pcaMeth)## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 9.797 4.32470 3.7835 3.31975 2.70291 2.48083 2.38076
## Proportion of Variance 0.189 0.03684 0.0282 0.02171 0.01439 0.01212 0.01116
## Cumulative Proportion 0.189 0.22589 0.2541 0.27579 0.29018 0.30230 0.31347
## PC8 PC9 PC10 PC11 PC12 PC13 PC14
## Standard deviation 2.29235 2.12396 1.99809 1.94241 1.85233 1.82499 1.78618
## Proportion of Variance 0.01035 0.00889 0.00786 0.00743 0.00676 0.00656 0.00628
## Cumulative Proportion 0.32382 0.33270 0.34057 0.34800 0.35476 0.36132 0.36760
## PC15 PC16 PC17 PC18 PC19 PC20 PC21
## Standard deviation 1.75348 1.72802 1.72478 1.69280 1.69024 1.67947 1.66557
## Proportion of Variance 0.00606 0.00588 0.00586 0.00564 0.00563 0.00556 0.00546
## Cumulative Proportion 0.37366 0.37954 0.38540 0.39104 0.39667 0.40222 0.40769
## PC22 PC23 PC24 PC25 PC26 PC27 PC28
## Standard deviation 1.65248 1.65090 1.6397 1.63538 1.6245 1.62047 1.61097
## Proportion of Variance 0.00538 0.00537 0.0053 0.00527 0.0052 0.00517 0.00511
## Cumulative Proportion 0.41307 0.41843 0.4237 0.42900 0.4342 0.43937 0.44448
## PC29 PC30 PC31 PC32 PC33 PC34 PC35
## Standard deviation 1.60339 1.59524 1.58948 1.58488 1.58002 1.57605 1.56581
## Proportion of Variance 0.00506 0.00501 0.00498 0.00495 0.00492 0.00489 0.00483
## Cumulative Proportion 0.44954 0.45456 0.45953 0.46448 0.46940 0.47429 0.47912
## PC36 PC37 PC38 PC39 PC40 PC41 PC42
## Standard deviation 1.56470 1.5608 1.55914 1.55632 1.54959 1.54240 1.53450
## Proportion of Variance 0.00482 0.0048 0.00479 0.00477 0.00473 0.00469 0.00464
## Cumulative Proportion 0.48394 0.4887 0.49353 0.49830 0.50303 0.50771 0.51235
## PC43 PC44 PC45 PC46 PC47 PC48 PC49
## Standard deviation 1.53243 1.52707 1.52616 1.52263 1.52003 1.5109 1.51022
## Proportion of Variance 0.00463 0.00459 0.00459 0.00457 0.00455 0.0045 0.00449
## Cumulative Proportion 0.51697 0.52157 0.52616 0.53072 0.53527 0.5398 0.54426
## PC50 PC51 PC52 PC53 PC54 PC55 PC56
## Standard deviation 1.50507 1.49904 1.49716 1.49182 1.48680 1.48313 1.47911
## Proportion of Variance 0.00446 0.00443 0.00441 0.00438 0.00435 0.00433 0.00431
## Cumulative Proportion 0.54872 0.55315 0.55756 0.56195 0.56630 0.57063 0.57494
## PC57 PC58 PC59 PC60 PC61 PC62 PC63
## Standard deviation 1.47646 1.47397 1.46902 1.46321 1.4605 1.45694 1.45493
## Proportion of Variance 0.00429 0.00428 0.00425 0.00422 0.0042 0.00418 0.00417
## Cumulative Proportion 0.57924 0.58352 0.58777 0.59198 0.5962 0.60037 0.60453
## PC64 PC65 PC66 PC67 PC68 PC69 PC70
## Standard deviation 1.44915 1.44814 1.44175 1.43938 1.43670 1.43360 1.42897
## Proportion of Variance 0.00414 0.00413 0.00409 0.00408 0.00407 0.00405 0.00402
## Cumulative Proportion 0.60867 0.61280 0.61690 0.62098 0.62504 0.62909 0.63311
## PC71 PC72 PC73 PC74 PC75 PC76 PC77
## Standard deviation 1.4252 1.42318 1.41449 1.41432 1.40982 1.40837 1.40315
## Proportion of Variance 0.0040 0.00399 0.00394 0.00394 0.00391 0.00391 0.00388
## Cumulative Proportion 0.6371 0.64110 0.64504 0.64898 0.65290 0.65680 0.66068
## PC78 PC79 PC80 PC81 PC82 PC83 PC84
## Standard deviation 1.40071 1.39952 1.39412 1.39254 1.39100 1.38728 1.38393
## Proportion of Variance 0.00386 0.00386 0.00383 0.00382 0.00381 0.00379 0.00377
## Cumulative Proportion 0.66455 0.66840 0.67223 0.67605 0.67986 0.68365 0.68743
## PC85 PC86 PC87 PC88 PC89 PC90 PC91
## Standard deviation 1.38267 1.37672 1.37320 1.37188 1.36688 1.36467 1.35839
## Proportion of Variance 0.00377 0.00373 0.00371 0.00371 0.00368 0.00367 0.00363
## Cumulative Proportion 0.69119 0.69492 0.69864 0.70235 0.70603 0.70969 0.71333
## PC92 PC93 PC94 PC95 PC96 PC97 PC98
## Standard deviation 1.35657 1.35419 1.35001 1.34718 1.34345 1.34137 1.33888
## Proportion of Variance 0.00362 0.00361 0.00359 0.00357 0.00355 0.00354 0.00353
## Cumulative Proportion 0.71695 0.72056 0.72415 0.72773 0.73128 0.73483 0.73836
## PC99 PC100 PC101 PC102 PC103 PC104 PC105
## Standard deviation 1.3328 1.33178 1.33021 1.32766 1.32044 1.31707 1.31605
## Proportion of Variance 0.0035 0.00349 0.00349 0.00347 0.00343 0.00342 0.00341
## Cumulative Proportion 0.7419 0.74535 0.74884 0.75231 0.75574 0.75916 0.76257
## PC106 PC107 PC108 PC109 PC110 PC111 PC112
## Standard deviation 1.31225 1.30600 1.30497 1.30237 1.30046 1.29975 1.29727
## Proportion of Variance 0.00339 0.00336 0.00335 0.00334 0.00333 0.00333 0.00331
## Cumulative Proportion 0.76596 0.76932 0.77268 0.77602 0.77935 0.78267 0.78599
## PC113 PC114 PC115 PC116 PC117 PC118 PC119
## Standard deviation 1.29250 1.29130 1.28839 1.28462 1.27665 1.2744 1.2738
## Proportion of Variance 0.00329 0.00328 0.00327 0.00325 0.00321 0.0032 0.0032
## Cumulative Proportion 0.78928 0.79256 0.79583 0.79908 0.80229 0.8055 0.8087
## PC120 PC121 PC122 PC123 PC124 PC125 PC126
## Standard deviation 1.27108 1.26869 1.26381 1.26235 1.26090 1.25997 1.25220
## Proportion of Variance 0.00318 0.00317 0.00315 0.00314 0.00313 0.00313 0.00309
## Cumulative Proportion 0.81187 0.81504 0.81819 0.82133 0.82446 0.82758 0.83067
## PC127 PC128 PC129 PC130 PC131 PC132 PC133
## Standard deviation 1.24923 1.24775 1.24538 1.24189 1.23873 1.23812 1.2339
## Proportion of Variance 0.00307 0.00307 0.00305 0.00304 0.00302 0.00302 0.0030
## Cumulative Proportion 0.83375 0.83681 0.83987 0.84290 0.84593 0.84895 0.8519
## PC134 PC135 PC136 PC137 PC138 PC139 PC140
## Standard deviation 1.22914 1.22539 1.22316 1.22287 1.2142 1.20960 1.20581
## Proportion of Variance 0.00298 0.00296 0.00295 0.00295 0.0029 0.00288 0.00286
## Cumulative Proportion 0.85492 0.85788 0.86083 0.86377 0.8667 0.86956 0.87242
## PC141 PC142 PC143 PC144 PC145 PC146 PC147
## Standard deviation 1.20109 1.19975 1.1932 1.1915 1.18824 1.18591 1.18275
## Proportion of Variance 0.00284 0.00284 0.0028 0.0028 0.00278 0.00277 0.00276
## Cumulative Proportion 0.87526 0.87810 0.8809 0.8837 0.88648 0.88925 0.89200
## PC148 PC149 PC150 PC151 PC152 PC153 PC154
## Standard deviation 1.18191 1.17804 1.17350 1.1703 1.16769 1.16514 1.16096
## Proportion of Variance 0.00275 0.00273 0.00271 0.0027 0.00269 0.00267 0.00265
## Cumulative Proportion 0.89475 0.89749 0.90020 0.9029 0.90558 0.90826 0.91091
## PC155 PC156 PC157 PC158 PC159 PC160 PC161
## Standard deviation 1.15883 1.15493 1.15319 1.1500 1.14370 1.14003 1.13519
## Proportion of Variance 0.00264 0.00263 0.00262 0.0026 0.00258 0.00256 0.00254
## Cumulative Proportion 0.91356 0.91618 0.91880 0.9214 0.92398 0.92654 0.92908
## PC162 PC163 PC164 PC165 PC166 PC167 PC168
## Standard deviation 1.13294 1.12480 1.12204 1.12100 1.11807 1.11314 1.10881
## Proportion of Variance 0.00253 0.00249 0.00248 0.00248 0.00246 0.00244 0.00242
## Cumulative Proportion 0.93161 0.93410 0.93658 0.93906 0.94152 0.94396 0.94638
## PC169 PC170 PC171 PC172 PC173 PC174 PC175
## Standard deviation 1.10253 1.09688 1.09550 1.09146 1.08897 1.08206 1.0800
## Proportion of Variance 0.00239 0.00237 0.00236 0.00235 0.00234 0.00231 0.0023
## Cumulative Proportion 0.94878 0.95115 0.95351 0.95586 0.95819 0.96050 0.9628
## PC176 PC177 PC178 PC179 PC180 PC181 PC182
## Standard deviation 1.07744 1.06860 1.06076 1.04951 1.04362 1.03643 1.02953
## Proportion of Variance 0.00229 0.00225 0.00222 0.00217 0.00215 0.00212 0.00209
## Cumulative Proportion 0.96508 0.96733 0.96955 0.97172 0.97386 0.97598 0.97807
## PC183 PC184 PC185 PC186 PC187 PC188 PC189
## Standard deviation 1.02162 1.0074 1.00170 0.98416 0.97791 0.96693 0.95399
## Proportion of Variance 0.00206 0.0020 0.00198 0.00191 0.00188 0.00184 0.00179
## Cumulative Proportion 0.98012 0.9821 0.98410 0.98600 0.98789 0.98973 0.99152
## PC190 PC191 PC192 PC193 PC194 PC195 PC196
## Standard deviation 0.95055 0.93319 0.9283 0.90959 0.89557 0.19769 4.351e-15
## Proportion of Variance 0.00178 0.00172 0.0017 0.00163 0.00158 0.00008 0.000e+00
## Cumulative Proportion 0.99330 0.99502 0.9967 0.99834 0.99992 1.00000 1.000e+00Visualize the components.
tmp = data.frame(pcaMeth$x)
ggplot(data=tmp, aes(x=PC1, y=PC2)) + geom_point() + theme_bw()
3 End of session
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## attached base packages:
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