The wateRmelon User’s Guide
29 October 2024
Abstract
The comprehensive guide to using the wateRmelon package analysing DNA methylation data.
Package
wateRmelon 2.13.0
Contents
1 Introduction
The wateRmelon provides a set of tools for importing, quality control and normalizing Illumina DNA methylation microarray data. All of the functions described in this vignette are fully compatible with the minfi package, including any workflows or pipelines that involves it. Additionally wateRmelon extends the methylumi package to allow for MethyLumiSet representations of EPIC array data. If you are using many hundreds of arrays, you could consider using the related wateRmelon.
In addition to our own functions we provide implementations for a variety of age-prediction and cell-type estimations which work for both 450k and EPIC data.
1.1 Citing the wateRmelon R package
- If you use wateRmelon please cite (Pidsley et al. 2013) and our new publication
- If you use adjustedDasen or adjustedFunnorm please cite (Wang et al. 2020)
- If you use estimateSex please cite (article? {Wang2021.09.30.462546)
2 Installation
wateRmelon is designed to extend the methylumi and minfi R packages so there is quite the number of dependencies which need to be installed. This can be handled by simply running:
install.packages('BiocManager')
BiocManager::install('wateRmelon')
library(wateRmelon)Alternatively, wateRmelon can be installed directly from our github
install.packages('devtools')
devtools::install_github('schalkwyk/wateRmelon')3 Quick Start
The package contains a small subset of 450k array data that can be used to explore all the funtions quickly. The melon data set is a MethyLumiSet containing 12 samples (columns) and 3364 features (rows)
data(melon)
dim(melon)## Features Samples
## 3363 12# Quality filter using default thresholds
melon.pf <- pfilter(melon)## 0 samples having 1 % of sites with a detection p-value greater than 0.05 were removed
## Samples removed:
## 72 sites were removed as beadcount <3 in 5 % of samples
## 40 sites having 1 % of samples with a detection p-value greater than 0.05 were removed# Normalize using one of the many available methods
melon.dasen.pf <- dasen(melon.pf)
# Extract Betas for downstream analysis
norm_betas <- betas(melon.dasen.pf)4 Data Import
The IDAT reading provided in methylumi has been updated to handle EPIC arrays, this is provided within the readEPIC function. Ideally, you can then read in a phenotype information that correspond to the idat files you have loaded in.
Alternatively you can read in data using minfi and work from an RGChannelSet or MethylSet object, using the read_metharray functions.
mlumi <- readEPIC('path/to/idats')5 Quality Control
Quality control is a vital part of performing an EWAS, it is important that we are able to easily identify samples that are outliers and could lead to false positive results.
There are many ways to identify outliers. Our preferred method is to use the outlyx function which provides a robust and reproducible multivariate method that we feel works well for most data-types.
5.1 Outlier Detection
outliers <- outlyx(melon, plot=TRUE)
print(outliers)## iqr mv outliers
## 6057825008_R01C01 FALSE FALSE FALSE
## 6057825008_R01C02 FALSE FALSE FALSE
## 6057825008_R02C01 FALSE TRUE FALSE
## 6057825008_R02C02 FALSE FALSE FALSE
## 6057825008_R03C01 FALSE FALSE FALSE
## 6057825008_R03C02 FALSE FALSE FALSE
## 6057825008_R04C01 FALSE FALSE FALSE
## 6057825008_R04C02 FALSE TRUE FALSE
## 6057825008_R05C01 FALSE FALSE FALSE
## 6057825008_R05C02 FALSE FALSE FALSE
## 6057825008_R06C01 FALSE FALSE FALSE
## 6057825008_R06C02 FALSE FALSE FALSE# remove outliers with melon[,!outliers$out]Similarly it is important to check the quality control probes to measure how well the experiment performs. For this we provide bscon to quickly check the bisulfite conversion probes for each sample to estimate the percentage of DNA that has been successfully converted. Values above 90% are ideally but values above 80% are also acceptable.
bsc <- bscon(melon)
hist(bsc, xlab = c(0, 100))
5.2 Probe Filtering
Poorly performing probes can be identified from the bead counts and detection p-values using the pfilter function
melon.pf <- pfilter(melon)## 0 samples having 1 % of sites with a detection p-value greater than 0.05 were removed
## Samples removed:
## 72 sites were removed as beadcount <3 in 5 % of samples
## 40 sites having 1 % of samples with a detection p-value greater than 0.05 were removed6 Phenotype Predition
wateRmelon now provides functions for three popular methods for generate phenotypic variables. Whether these are needed for quality control or identifying sample mismatch. Or could even been provided as a missing covariate for analyses
6.1 Epigenetic Ages
wateRmelon provides 5 epigenetic age predictors: Horvath, Horvath Skin & Blood, Hannum, PhenoAge and Lin! As an extra measure we provide the number of missing probes for each clock and this can be used to supply your own list of coefficients to calculate different linear age predictions. !!However!! any normalisation methods that the original age predictors use to predict ages so there will be slight differences compared to the original methods.
agep(melon, method='all')## horvath.horvath.age horvath.horvath.n_missing
## 6057825008_R01C01 36.68182 351
## 6057825008_R01C02 36.68789 351
## 6057825008_R02C01 36.72415 351
## 6057825008_R02C02 36.70512 351
## 6057825008_R03C01 36.47068 351
## 6057825008_R03C02 36.72860 351
## 6057825008_R04C01 36.57890 351
## 6057825008_R04C02 36.60815 351
## 6057825008_R05C01 36.66895 351
## 6057825008_R05C02 36.52764 351
## 6057825008_R06C01 36.53931 351
## 6057825008_R06C02 36.56719 351
## hannum.hannum.age hannum.hannum.n_missing
## 6057825008_R01C01 0 71
## 6057825008_R01C02 0 71
## 6057825008_R02C01 0 71
## 6057825008_R02C02 0 71
## 6057825008_R03C01 0 71
## 6057825008_R03C02 0 71
## 6057825008_R04C01 0 71
## 6057825008_R04C02 0 71
## 6057825008_R05C01 0 71
## 6057825008_R05C02 0 71
## 6057825008_R06C01 0 71
## 6057825008_R06C02 0 71
## phenoage.phenoage.age phenoage.phenoage.n_missing
## 6057825008_R01C01 60.77928 511
## 6057825008_R01C02 60.77094 511
## 6057825008_R02C01 60.77487 511
## 6057825008_R02C02 60.77373 511
## 6057825008_R03C01 60.77430 511
## 6057825008_R03C02 60.78078 511
## 6057825008_R04C01 60.77005 511
## 6057825008_R04C02 60.77078 511
## 6057825008_R05C01 60.77756 511
## 6057825008_R05C02 60.77066 511
## 6057825008_R06C01 60.77628 511
## 6057825008_R06C02 60.77336 511
## skinblood.skinblood.age skinblood.skinblood.n_missing
## 6057825008_R01C01 15.02028 389
## 6057825008_R01C02 14.91345 389
## 6057825008_R02C01 15.09054 389
## 6057825008_R02C02 15.04701 389
## 6057825008_R03C01 15.18590 389
## 6057825008_R03C02 15.13731 389
## 6057825008_R04C01 15.11105 389
## 6057825008_R04C02 15.06645 389
## 6057825008_R05C01 15.08693 389
## 6057825008_R05C02 15.16253 389
## 6057825008_R06C01 15.23390 389
## 6057825008_R06C02 15.10623 389
## lin.lin.age lin.lin.n_missing
## 6057825008_R01C01 16.05763 98
## 6057825008_R01C02 16.61655 98
## 6057825008_R02C01 15.88568 98
## 6057825008_R02C02 17.03739 98
## 6057825008_R03C01 16.49062 98
## 6057825008_R03C02 16.40962 98
## 6057825008_R04C01 15.58818 98
## 6057825008_R04C02 16.30653 98
## 6057825008_R05C01 16.76162 98
## 6057825008_R05C02 17.24285 98
## 6057825008_R06C01 15.69054 98
## 6057825008_R06C02 15.78848 98agep(melon, method='horvath')## horvath.age horvath.n_missing
## 6057825008_R01C01 36.68182 351
## 6057825008_R01C02 36.68789 351
## 6057825008_R02C01 36.72415 351
## 6057825008_R02C02 36.70512 351
## 6057825008_R03C01 36.47068 351
## 6057825008_R03C02 36.72860 351
## 6057825008_R04C01 36.57890 351
## 6057825008_R04C02 36.60815 351
## 6057825008_R05C01 36.66895 351
## 6057825008_R05C02 36.52764 351
## 6057825008_R06C01 36.53931 351
## 6057825008_R06C02 36.56719 3516.2 Sex
Sexes of samples can be determined like so:
estimateSex(betas(melon), do_plot=TRUE)## Normalize beta values by Z score...## Fishished Zscore normalization.## Warning in estimateSex(betas(melon), do_plot = TRUE): Missing 4036 probes!
## cg25813447, cg07779434, cg14959801, cg27263894, cg19605909, cg24888621, cg20851325, cg04369555, cg09192059, cg26055054, cg22069441, cg05422360, cg04836978, cg12594800, cg04596655, cg03590418, cg21159768, cg22417589, cg08130682, cg02285254, cg21258987, cg11185978, cg20439892, cg03672021, cg03370193, cg14022645, cg15906052, cg20015269, cg06302025, cg21113886, cg14332086, cg07381502, cg23277098, cg16594779, cg03460546, cg06936709, cg18102950, cg18011534, cg09070199, cg02213813, cg01674874, cg02354343, cg21142188, cg12166502, cg13115118, cg10256662, cg22750044, cg07865580, cg12030638, cg02770249, cg01062269, cg02869694, cg25910467, cg03332892, cg02161919, cg13443410, cg10670396, cg07256221, cg09523186, cg23696472, cg00735883, cg00416689, cg16697940, cg22238863, cg09055253, cg04225046, cg16315447, cg25790081, cg24978383, cg11663421, cg03905487, cg15848449, cg26628435, cg22332696, cg07388258, cg26552464, cg25930738, cg22569587, cg21604054, cg23299641, cg05073171, cg14783837, cg22474754, cg27548075, cg08785133, cg03554089, cg05032903, cg13616735, cg12165338, cg24559073, cg00264378, cg14510207, cg20497120, cg08955859, cg06720669, cg21019788, cg06939792, cg21988739, cg07748875, cg09791535, cg27124847, cg19505129, cg18395382, cg17339373, cg01187510, cg26583344, cg15858894, cg02169289, cg09086179, cg17676246, cg01166930, cg06408301, cg21456313, cg02871887, cg22522913, cg16414561, cg07629996, cg11308037, cg13786096, cg18377059, cg22569627, cg12377701, cg22850802, cg01924074, cg26359958, cg12865398, cg20775112, cg17878135, cg04026379, cg17558827, cg21016188, cg11111271, cg27513289, cg06172626, cg24550433, cg16115418, cg18159654, cg01913196, cg05558522, cg08882363, cg02265919, cg11647489, cg02971902, cg00723997, cg00583618, cg08902818, cg22527050, cg05799859, cg23554546, cg27054331, cg07867687, cg26327984, cg25622910, cg21932800, cg22823767, cg06544877, cg05835545, cg03517379, cg12440269, cg13613682, cg23612178, cg09146364, cg03941517, cg08965337, cg19180514, cg06098232, cg08609270, cg11179629, cg25063710, cg27488875, cg01758418, cg02725692, cg26059639, cg25225807, cg03959273, cg25364049, cg14308265, cg06409934, cg24392532, cg26738106, cg00810519, cg17832062, cg25697726, cg11902811, cg27260927, cg15755924, cg03104298, cg13918312, cg02821380, cg20971536, cg15874629, cg21595968, cg20792978, cg00142683, cg16722818, cg21362390, cg25514427, cg07828380, cg01293417, cg27296089, cg27375748, cg21887683, cg11005845, cg25987936, cg22575892, cg19415339, cg15024309, cg00140189, cg01996818, cg12058262, cg27080287, cg05223760, cg04811556, cg02805922, cg06139288, cg11704979, cg14349378, cg17195879, cg18990292, cg11272332, cg05803913, cg21156383, cg13839778, cg17036062, cg08195522, cg09890269, cg09100792, cg10649042, cg04128303, cg06152434, cg14430524, cg25587058, cg04025582, cg23408987, cg07187289, cg26104731, cg20979765, cg09867302, cg02239640, cg01825872, cg10174816, cg03422380, cg26510526, cg13182391, cg13569417, cg15841434, cg23967850, cg04675919, cg02859554, cg25237542, cg12537796, cg04924141, cg02510708, cg11441847, cg12591117, cg12935118, cg16510200, cg12419383, cg16617830, cg26376518, cg09310980, cg07368951, cg20498086, cg19911179, cg06462727, cg26333595, cg03705894, cg21880156, cg01600123, cg19548176, cg15517690, cg10293967, cg00936435, cg22069989, cg17292622, cg21080294, cg05511752, cg15867166, cg02120046, cg16680922, cg24341236, cg19513111, cg25881119, cg08405463, cg05134041, cg08422514, cg09034929, cg06097615, cg16248169, cg19481013, cg12823249, cg04665139, cg21501064, cg17865482, cg06783548, cg27470278, cg20540913, cg23137494, cg06558952, cg20457259, cg27151711, cg04791822, cg12813325, cg14230133, cg23503517, cg20350671, cg17562247, cg00920960, cg19137564, cg14261068, cg17503853, cg20359784, cg21480420, cg10403109, cg01555661, cg15454483, cg03983969, cg26342575, cg08358587, cg13185972, cg12228772, cg17155577, cg13974562, cg25614853, cg03670113, cg04702045, cg08479532, cg08757719, cg09072865, cg02012821, cg13571540, cg20546893, cg17033281, cg17192679, cg11434986, cg13012525, cg16107992, cg07806343, cg16809091, cg01108554, cg06657741, cg13674559, cg10070668, cg20081453, cg03679005, cg21142420, cg14463432, cg27350602, cg12584551, cg11509903, cg20442640, cg08591489, cg08757574, cg21509846, cg00767637, cg02921434, cg05418443, cg23722438, cg15904427, cg04700060, cg25165659, cg10274815, cg17152375, cg17565795, cg07796014, cg12763824, cg26032662, cg13915726, cg22682304, cg12709057, cg07428182, cg12938998, cg03161453, cg21202708, cg13214937, cg07674520, cg05445331, cg00267352, cg02161125, cg27453644, cg20074774, cg19859323, cg21602092, cg06551391, cg25452717, cg14873818, cg22343001, cg18834878, cg10510586, cg09261015, cg11854877, cg26909705, cg27123903, cg21010298, cg05892376, cg02864732, cg01817069, cg08615635, cg16857716, cg23356769, cg18016370, cg14460470, cg22618086, cg14470409, cg03939693, cg26927606, cg18885073, cg24183173, cg10519228, cg09965404, cg00466309, cg12707233, cg15661671, cg24054653, cg04031645, cg10950266, cg19718903, cg10549828, cg12536534, cg24748621, cg24052239, cg24000218, cg06411441, cg24536689, cg21978299, cg04032096, cg23726559, cg06438901, cg16158045, cg23443158, cg13208429, cg20396841, cg15067665, cg25528264, cg23896353, cg03202526, cg19218988, cg05127178, cg26004099, cg12039967, cg20958732, cg26674826, cg20589243, cg24925526, cg16626088, cg08447449, cg14004892, cg19616372, cg11901680, cg07253552, cg07374632, cg07822777, cg05424879, cg01043588, cg16161440, cg26695278, cg06538336, cg24831179, cg18414950, cg05045028, cg16429439, cg08464305, cg01828474, cg09521623, cg26121752, cg09990582, cg00745293, cg08221357, cg13582495, cg25911220, cg23417743, cg15757320, cg05673346, cg21491240, cg03575468, cg06775759, cg05143403, cg06915915, cg20332806, cg18769303, cg15356502, cg26936230, cg08848171, cg26382696, cg00374088, cg23527532, cg10047502, cg15706156, cg10401803, cg07653728, cg18102233, cg09210933, cg06779458, cg15802548, cg04484695, cg24597825, cg05184076, cg14113300, cg17914143, cg25075069, cg04703500, cg03576039, cg19797013, cg05941375, cg04189697, cg03050491, cg20121427, cg12622895, cg03706022, cg12194828, cg10873964, cg23907260, cg18717600, cg26381742, cg16967668, cg03157806, cg05088151, cg12373280, cg16412513, cg04216286, cg13797960, cg06650776, cg26555756, cg11049774, cg10869159, cg25656978, cg14812623, cg08836298, cg12152167, cg07109010, cg13628616, cg21290550, cg22625568, cg00412010, cg05091873, cg04029282, cg08963265, cg22044840, cg03213202, cg24275475, cg15579650, cg16641060, cg18536496, cg12125741, cg21762935, cg12472218, cg21030483, cg13532816, cg09411587, cg01758988, cg01142317, cg26778358, cg13190238, cg07929406, cg23860088, cg09285672, cg18796341, cg25896901, cg16832275, cg02773050, cg16998810, cg21400640, cg09778422, cg19800913, cg12290635, cg21294096, cg03759948, cg27264374, cg06323885, cg23181142, cg25933726, cg09610589, cg19041137, cg08798116, cg02799972, cg13663390, cg11825763, cg07946630, cg03100923, cg20767561, cg19238394, cg06104959, cg14970569, cg06614969, cg15236057, cg26580465, cg06295352, cg00116265, cg09186478, cg11844737, cg16314146, cg01039990, cg06140460, cg19741073, cg16529483, cg18107314, cg05414241, cg12308243, cg12454245, cg21905818, cg10956264, cg02179438, cg12047536, cg06899582, cg00525383, cg17399684, cg19787517, cg06444329, cg19944582, cg08786860, cg23954206, cg19168249, cg03683587, cg14306994, cg10914789, cg04763286, cg26457165, cg05600581, cg09227616, cg12592455, cg01081720, cg01999076, cg00116709, cg05526804, cg00829575, cg26746069, cg11442732, cg25698940, cg27326620, cg26482893, cg18091964, cg05288642, cg16250105, cg00818649, cg05184682, cg14541939, cg12054566, cg12842316, cg01813294, cg11654517, cg01314643, cg14541448, cg21183872, cg09513996, cg09407917, cg08817443, cg12687215, cg12689022, cg03210912, cg06746884, cg08983668, cg11764747, cg21040569, cg23623404, cg22975791, cg12614178, cg08539065, cg00621925, cg15410402, cg15043283, cg04144603, cg02615131, cg05085049, cg10418630, cg15855671, cg20104776, cg13431666, cg20455959, cg22858728, cg25402895, cg11165479, cg17571782, cg18641697, cg14295696, cg08348649, cg02480419, cg11703139, cg24126759, cg07452499, cg02055404, cg2726843## Warning in estimateSex(betas(melon), do_plot = TRUE): Missing 281 probes!
## cg02842889, cg15281205, cg15345074, cg26983535, cg01900066, cg01073572, cg24183504, cg05230942, cg05544622, cg17837162, cg25538674, cg11898347, cg06065495, cg13252613, cg27611726, cg00975375, cg00243321, cg10799208, cg17741448, cg08053115, cg02012379, cg18077436, cg04448376, cg05621349, cg10835413, cg02839557, cg07731488, cg14526044, cg10422744, cg27578568, cg02577797, cg04958669, cg10338539, cg00214611, cg03441493, cg02004872, cg00271873, cg14741114, cg26488634, cg05128824, cg24393100, cg24837623, cg27433982, cg02011394, cg06479204, cg06322277, cg10213302, cg00543493, cg21106100, cg12456573, cg02522936, cg18032798, cg10267609, cg04023335, cg03767353, cg08673225, cg01311227, cg20474581, cg10620659, cg14492024, cg13808036, cg24016855, cg01215343, cg05480730, cg03750315, cg03258315, cg05890011, cg01911472, cg14671357, cg02050847, cg25705492, cg02606988, cg04042030, cg20864678, cg13765957, cg11684211, cg13365400, cg05098815, cg25640065, cg03827298, cg00213748, cg00212031, cg13268984, cg10646950, cg02056550, cg08258654, cg10959847, cg27509967, cg03052502, cg15935877, cg08739478, cg07795413, cg07607525, cg04303809, cg14442616, cg17939569, cg25914522, cg00272582, cg25059696, cg00762184, cg10841270, cg26475999, cg04493908, cg13861458, cg00576139, cg25427172, cg00455876, cg07851521, cg04559508, cg15059553, cg15422579, cg14029254, cg02126249, cg08596608, cg14778208, cg09732580, cg17651935, cg23834181, cg14005657, cg15462332, cg15516537, cg17834650, cg09898573, cg26046487, cg05672930, cg26517491, cg10252249, cg15810474, cg08820785, cg04016144, cg02730008, cg16552926, cg25704368, cg25518695, cg00676506, cg01644972, cg08680991, cg05964935, cg04576441, cg04831594, cg09748856, cg03769088, cg09228985, cg07765982, cg27545697, cg08242338, cg25012987, cg11225091, cg05954446, cg03359666, cg15662272, cg14180491, cg04790916, cg08702825, cg14933403, cg05367916, cg03683899, cg14931215, cg14248084, cg03443143, cg05408674, cg06237805, cg14972466, cg03155755, cg01984154, cg06576965, cg13805219, cg05051262, cg09408193, cg05530472, cg06636270, cg03535417, cg27248959, cg15329860, cg02129146, cg15700967, cg15849038, cg08816194, cg27254225, cg17660627, cg05865243, cg03278611, cg25667057, cg01426558, cg08265308, cg11131351, cg02402208, cg02272584, cg03695421, cg17560699, cg08593141, cg03416979, cg10691859, cg25815185, cg06587955, cg03515816, cg15746461, cg03601053, cg27049643, cg03430010, cg18168924, cg10363397, cg06231362, cg10239257, cg15197499, cg26928789, cg25071634, cg10172760, cg01828798, cg18163559, cg13419214, cg15429127, cg08160949, cg14303457, cg09829904, cg27325772, cg13618458, cg06855731, cg05378695, cg10698069, cg20256738, cg01523029, cg18058072, cg05999368, cg10067523, cg15682993, cg18085787, cg20764275, cg06865724, cg26497631, cg04193779, cg00599377, cg14273923, cg00308367, cg11021362, cg02105393, cg25363292, cg27355713, cg22051787, cg17972491, cg14151065, cg03123709, cg09460641, cg09350919, cg03533500, cg04351468, cg26983430, cg13851368, cg02107461, cg13845521, cg05725925, cg08528516, cg14742615, cg09546548, cg10811597, cg15682806, cg08357313, cg09081202, cg09804407, cg16626452, cg01757887, cg25443613, cg03266527, cg02616328, cg05678960, cg10051237, cg11816202, cg07747963, cg02233190, cg15431336, cg26058907, cg19244032, cg01463110, cg00789540, cg26198148, cg02340092, cg00639218, cg14133106, cg01209756, cg14157445, cg03905640
## X Y predicted_sex
## 6057825008_R01C01 -0.05116010 0.12343032 Male
## 6057825008_R01C02 -0.03852021 0.13125424 Male
## 6057825008_R02C01 -0.04605816 0.11486779 Male
## 6057825008_R02C02 -0.04661608 0.11587946 Male
## 6057825008_R03C01 0.16004834 -0.05563472 Female
## 6057825008_R03C02 0.17358788 -0.04939172 Female
## 6057825008_R04C01 0.14628952 -0.07222119 Female
## 6057825008_R04C02 -0.05122662 0.11274543 Male
## 6057825008_R05C01 0.16784195 -0.04123302 Female
## 6057825008_R05C02 -0.06365946 0.10793467 Male
## 6057825008_R06C01 -0.06126409 0.11465433 Male
## 6057825008_R06C02 0.15132332 -0.06155400 Female6.3 Cell-type proportions
wateRmelon is able to compute cell counts for bulk blood (currently only bulk blood).
estimateCellCounts.wateRmelon(melon, referencePlatform = "IlluminaHumanMethylation450k")
estimateCellCounts.wateRmelon(melon, referencePlatform = "IlluminaHumanMethylationEPIC") # change reference7 Normalization & Preprocessing
| Norm Method | Short Description | Extra Details |
|---|---|---|
| dasen | Best performing normalisation method according to Pidsley 2013 | |
| nasen | Simpler implemenation of dasen that does not correct for sentrix positions | Can be used on EPIC data without any consequences. |
| adjusted_dasen | dasen with our interpolatedXY method | offset_fit = FALSE performs adjusted_nasen instead |
| adjusted_funnorm | funnorm with our interpolatedXY method | Only Available to RGChannelSet |
| SWAN | Popular method for normalisation |
As such they can be used as such:
dasen.melon <- dasen(melon) # Use whichever method you would like to use. 7.1 Normalization Violence
Normalization aims to align the data across samples to make it ready for analysis. The degree of normalization across samples is variable, where samples undergo a more dramatic transformation to resemble the rest of the data. This can be an indicator that a sample has addition-al noise or is an outlier. The qual function provides an estimate of vio-lence that has been introduced through preprocessing and normalization.
das <- dasen(melon)
qu <- qual(betas(melon), betas(das))
plot(qu[,1], qu[,2])
8 Performance Metrics
TODO… ## Genomic Imprinting
dmrse_row(melon.pf)## 223 iDMR data rows found## [1] 0.005428861dmrse_row(melon.dasen.pf) # Slightly better standard errores## 223 iDMR data rows found## [1] 0.0020863818.1 SNP Genotypes
Not available for minfi objects
genki(melon.pf)## 65SNP data rows found## NULL## [1] 8.129585e-05 2.020173e-04 7.819409e-05genki(melon.dasen.pf)## 65SNP data rows found## NULL## [1] 5.074810e-05 1.255207e-04 4.529896e-058.2 XCI
seabi(melon.pf, sex=pData(melon.pf)$sex, X=fData(melon.pf)$CHR=='X')## [1] 0.2597014seabi(melon.dasen.pf, sex=pData(melon.dasen.pf)$sex, X=fData(melon.dasen.pf)$CHR=='X')## [1] 0.10102689 Statistical Analysis
Although we cannot predict what type of analysis you are expecting to perform we have a couple of recommendations that should be considered before you perform statistical testing. Firstly we recommend a final sweep of the normalized data using pwod
bet <- betas(melon)
pwod_bet <- pwod(bet)## 47 probes detected.# Statistical Analysis using pwod_betSession info
## R Under development (unstable) (2024-10-21 r87258)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] wateRmelon_2.13.0
## [2] illuminaio_0.49.0
## [3] IlluminaHumanMethylation450kanno.ilmn12.hg19_0.6.1
## [4] ROC_1.83.0
## [5] lumi_2.59.0
## [6] methylumi_2.53.0
## [7] minfi_1.53.0
## [8] bumphunter_1.49.0
## [9] locfit_1.5-9.10
## [10] iterators_1.0.14
## [11] foreach_1.5.2
## [12] Biostrings_2.75.0
## [13] XVector_0.47.0
## [14] SummarizedExperiment_1.37.0
## [15] MatrixGenerics_1.19.0
## [16] FDb.InfiniumMethylation.hg19_2.2.0
## [17] org.Hs.eg.db_3.20.0
## [18] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
## [19] GenomicFeatures_1.59.0
## [20] AnnotationDbi_1.69.0
## [21] GenomicRanges_1.59.0
## [22] GenomeInfoDb_1.43.0
## [23] IRanges_2.41.0
## [24] S4Vectors_0.45.0
## [25] ggplot2_3.5.1
## [26] reshape2_1.4.4
## [27] scales_1.3.0
## [28] matrixStats_1.4.1
## [29] limma_3.63.0
## [30] Biobase_2.67.0
## [31] BiocGenerics_0.53.0
## [32] BiocStyle_2.35.0
##
## loaded via a namespace (and not attached):
## [1] RColorBrewer_1.1-3 jsonlite_1.8.9
## [3] magrittr_2.0.3 magick_2.8.5
## [5] rmarkdown_2.28 BiocIO_1.17.0
## [7] zlibbioc_1.53.0 vctrs_0.6.5
## [9] multtest_2.63.0 memoise_2.0.1
## [11] Rsamtools_2.23.0 DelayedMatrixStats_1.29.0
## [13] RCurl_1.98-1.16 askpass_1.2.1
## [15] tinytex_0.53 htmltools_0.5.8.1
## [17] S4Arrays_1.7.0 curl_5.2.3
## [19] Rhdf5lib_1.29.0 SparseArray_1.7.0
## [21] rhdf5_2.51.0 sass_0.4.9
## [23] KernSmooth_2.23-24 nor1mix_1.3-3
## [25] bslib_0.8.0 plyr_1.8.9
## [27] cachem_1.1.0 GenomicAlignments_1.43.0
## [29] lifecycle_1.0.4 pkgconfig_2.0.3
## [31] Matrix_1.7-1 R6_2.5.1
## [33] fastmap_1.2.0 GenomeInfoDbData_1.2.13
## [35] digest_0.6.37 colorspace_2.1-1
## [37] siggenes_1.81.0 reshape_0.8.9
## [39] RSQLite_2.3.7 base64_2.0.2
## [41] fansi_1.0.6 mgcv_1.9-1
## [43] httr_1.4.7 abind_1.4-8
## [45] compiler_4.5.0 beanplot_1.3.1
## [47] rngtools_1.5.2 bit64_4.5.2
## [49] withr_3.0.2 BiocParallel_1.41.0
## [51] DBI_1.2.3 highr_0.11
## [53] HDF5Array_1.35.0 MASS_7.3-61
## [55] openssl_2.2.2 DelayedArray_0.33.0
## [57] rjson_0.2.23 tools_4.5.0
## [59] rentrez_1.2.3 quadprog_1.5-8
## [61] glue_1.8.0 restfulr_0.0.15
## [63] nlme_3.1-166 rhdf5filters_1.19.0
## [65] grid_4.5.0 generics_0.1.3
## [67] gtable_0.3.6 tzdb_0.4.0
## [69] preprocessCore_1.69.0 tidyr_1.3.1
## [71] hms_1.1.3 data.table_1.16.2
## [73] xml2_1.3.6 utf8_1.2.4
## [75] pillar_1.9.0 stringr_1.5.1
## [77] genefilter_1.89.0 splines_4.5.0
## [79] dplyr_1.1.4 lattice_0.22-6
## [81] survival_3.7-0 rtracklayer_1.67.0
## [83] bit_4.5.0 GEOquery_2.75.0
## [85] annotate_1.85.0 tidyselect_1.2.1
## [87] knitr_1.48 bookdown_0.41
## [89] xfun_0.48 scrime_1.3.5
## [91] statmod_1.5.0 stringi_1.8.4
## [93] UCSC.utils_1.3.0 yaml_2.3.10
## [95] evaluate_1.0.1 codetools_0.2-20
## [97] tibble_3.2.1 BiocManager_1.30.25
## [99] affyio_1.77.0 cli_3.6.3
## [101] xtable_1.8-4 munsell_0.5.1
## [103] jquerylib_0.1.4 Rcpp_1.0.13
## [105] png_0.1-8 XML_3.99-0.17
## [107] readr_2.1.5 blob_1.2.4
## [109] mclust_6.1.1 doRNG_1.8.6
## [111] sparseMatrixStats_1.19.0 bitops_1.0-9
## [113] affy_1.85.0 nleqslv_3.3.5
## [115] purrr_1.0.2 crayon_1.5.3
## [117] rlang_1.1.4 KEGGREST_1.47.0
Pidsley, R., C. C. Y Wong, M. Volta, K. Lunnon, J. Mill, and L. C. Schalkwyk. 2013. “A Data-Driven Approach to Preprocessing Illumina 450K Methylation Array Data.” Journal Article. BMC Genomics 14: 293. https://doi.org/10.1186/1471-2164-14-293.
Wang, Yucheng, Eilis Hannon, Olivia A Grant, Tyler J Gorrie-Stone, Meena Kumari, Jonathan Mill, Xiaojun Zhai, Klaus D McDonald-Maier, and Leonard C Schalkwyk. 2020. “DNA Methylation-Based Sex Classifier to Predict Sex and Identify Sex Chromosome Aneuploidy.” bioRxiv. https://doi.org/10.1101/2020.10.19.345090.