The epimutacions User’s Guide
2022-06-21
Abstract
Epimutations are rare alterations in the methylation pattern at specific loci. Have been demonstrated that epimutations could be the causative factor of some genetic diseases. Nonetheless, no standard methods are available to detect and quantify these alterations. This vignette provides an introductory guide to theepimutacions package. The package contains several outlier detection methods to identify epimutations in genome-wide DNA methylation microarrays data. The areas covered in this document are: (1) package installation; (2) data loading and preprocessing; and (3) epimutation identification, annotation and visualization.
Package
epimutacions 1.0.3
1 Introduction
1.1 Background
Epimutations are rare alterations in the methylation pattern at specific loci. Have been demonstrated that epimutations could be the causative factor of some genetic diseases. For example, epimutations can lead to cancers, such as Lynch syndrome, rare diseases such as Prader-Willi syndrome, and are associated with common disorders, such as autism. Nonetheless, no standard methods are available to detect and quantify these alterations. Two methods for epimutations detection on methylation microarray data have been reported: (1) based on identifying CpGs with outlier values and then cluster them in epimutations (Barbosa et al. 2018); (2) define candidate regions with bumphunter and test their statistical significance with a MANOVA (Aref-Eshghi et al. 2019). However, the implementation of these methods is not publicly available, and these approaches have not been compared.
We have developed epimutacions R/Bioconductor package.
We have implemented those two methods
(called quantile and manova, respectively).
We implemented four additional approaches,
using a different distribution to detect CpG outliers (beta),
or a different model to
assess region significance (mlm, mahdist, and iForest).
The package epimutacions provides tools to raw DNA methylation microarray
intensities normalization and epimutations identification,
visualization and annotation.
The full epimutacions user´s guide is available in this vignette.
The main function to estimate the epimutations is called epimutations().
The name of the package is epimutacions
(pronounced ɛ pi mu ta 'sj ons) which means epimutations in Catalan,
a language from the northeast of Spain.
1.2 Methodology
The epimutacions package computes a genome-wide DNA methylation analysis
to identify the epimutations to be considered as biomarkers
for case samples with a suspected genetic disease.
The function epimutations() infers epimutations on a case-control design.
It compares a case sample against a reference panel (healthy individuals).
The package also includes leave-one-out approach
(epimutations_one_leave_out()).
It compares individual methylation profiles of a single sample
(regardless if they are cases or controls) against all
other samples from the same cohort.
The epimutacions package includes 6 outlier detection approaches
(figure 1):
(1) Multivariate Analysis of variance (manova),
(2) Multivariate Linear Model (mlm), (3) isolation forest (iForest),
(4) robust mahalanobis distance (mahdist) (5) quantile and (6) beta.
The approaches manova, mlm, iForest and mahdist
define the candidate regions (differentially methylated regions (DMRs))
using bumphunter method (Jaffe et al. 2012).
Then, those DMRs are tested to identify regions
with CpGs being outliers when comparing with the reference panel.
quantile uses the sliding window approach to
individually compare the methylation value in each proband
against the reference panel and then cluster them in epimutations.
Beta utilizes beta distribution to identify outlier CpGs.
We have defined an epimutation as a consecutive window of a minimum of 3 outliers CpGs with a maximum distance of 1kb between them (Barbosa et al. 2018).
Figure 1: Implementation of each outlier detection method
2 Setup
2.1 Installing the packages
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("epimutacions")if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("epimutacionsData")2.2 Loading libraries
library(epimutacions)2.3 Quick start
The workflow in figure 2 explains the main analysis
in the epimutacions package.
The package allows two different types of inputs:
- Case samples
IDATfiles (raw microarray intensities) together withRGChannelSetobject as reference panel. The reference panel can be supplied by the user or can be selected through the example datasets that the package provides (section 3).
- Case samples
GenomicRatioSetobject containing case and control samples.
The normalization (epi_preprocess()) converts the raw microarray
intensities into usable methylation measurement
(\(\beta\) values at CpG locus level).
As a result, we obtain a GenomicRatioSet object,
which can be used as epimutations() function input.
The data should contain information about values of CpG sites,
phenotype and feature data.
Figure 2: Allowed data formats, normalization and input types
3 Datasets
The package contains 3 example datasets adapted from Gene Expression Omnibus (GEO):
- 4 case samples IDAT files (GEO: GSE131350)
reference_panel: aRGChannelSetclass object containing 22 healthy individuals (GEO: GSE127824)
methy: aGenomicRatioSetobject which includes 49 controls (GEO: GSE104812) and 3 cases (GEO: GSE97362).
We also included a dataset specifying the 40,408 candidate regions in Illumina 450K array which could be epimutations (see section 3.2).
We created the epimutacionsData package in ExperimentHub.
It contains the reference panel, methy and the candidate epimutations datasets.
The package includes the IDAT files as external data.
To access the datasets we need to install the packages
by running the following commands:
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("ExperimentHub")Then, we need to load the package and create an ExperimentHub object:
library(ExperimentHub)
eh <- ExperimentHub()
query(eh, c("epimutacionsData"))ExperimentHub with 3 records
# snapshotDate(): 2022-04-26
# $dataprovider: GEO, Illumina 450k array
# $species: Homo sapiens
# $rdataclass: RGChannelSet, GenomicRatioSet, GRanges
# additional mcols(): taxonomyid, genome, description,
# coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
# rdatapath, sourceurl, sourcetype
# retrieve records with, e.g., 'object[["EH6690"]]'
title
EH6690 | Control and case samples
EH6691 | Reference panel
EH6692 | Candidate epimutations 3.1 IDAT files and reference panel
IDAT files directory in epimutacionsData package:
baseDir <- system.file("extdata", package = "epimutacionsData")The reference panel and methy dataset can be found in
EH6691 and EH6690 record of the eh object:
reference_panel <- eh[["EH6691"]]
methy <- eh[["EH6690"]]3.2 Candidate regions
In Illumina 450K array, probes are unequally distributed along the genome, limiting the number of regions that can fulfil the requirements to be considered an epimutation. Thus, we have computed a dataset containing all the regions that could be candidates to become an epimutation.
We used the clustering approach from bumphunter to define
the candidate epimutations.
We defined a primary dataset with all the CpGs from the Illumina 450K array.
Then, we run bumphunter and selected those regions with at
least 3 CpGs and a maximum distance of 1kb between them.
As a result, we found 40,408 candidate epimutations.
The function epimutation() filters the identified
epimutations using these candidate regions.
The following is the code used to identify the candidate epimutations in Illumina 450K array:
library(minfi)
# Regions 450K
library(IlluminaHumanMethylation450kanno.ilmn12.hg19)
data(Locations)
### Select CpGs (names starting by cg) in autosomic chromosomes
locs.450 <- subset(Locations, grepl("^cg", rownames(Locations)) & chr %in% paste0("chr", 1:22))
locs.450GR <- makeGRangesFromDataFrame(locs.450,
start.field = "pos",
end.field = "pos",
strand = "*")
locs.450GR <- sort(locs.450GR)
mat <- matrix(0, nrow = length(locs.450GR), ncol = 2,
dimnames = list(names(locs.450GR), c("A", "B")))
## Set sample B to all 1
mat[, 2] <- 1
## Define model matrix
pheno <- data.frame(var = c(0, 1))
model <- model.matrix(~ var, pheno)
## Run bumphunter
bumps <- bumphunter(mat, design = model, pos = start(locs.450GR),
chr = as.character(seqnames(locs.450GR)),
cutoff = 0.05)$table
bumps.fil <- subset(bumps, L >= 3)The candidate regions can be found in EH6692 record of the eh object:
candRegsGR <- eh[["EH6692"]]4 Preprocessing
The epi_preprocess() function allows calling the
6 preprocessing methods from minfi package:
| Method | Function | Description |
|---|---|---|
raw |
preprocessRaw |
Converts the Red/Green channel for an Illumina methylation array into methylation signal, without using any normalization |
illumina |
preprocessIllumina |
Implements preprocessing for Illumina methylation microarrays as used in Genome Studio |
swan |
preprocessSWAN |
Subset-quantile Within Array Normalisation (SWAN). It allows Infinium I and II type probes on a single array to be normalized together |
quantile |
preprocessQuantile |
Implements stratified quantile normalization preprocessing for Illumina methylation microarrays |
noob |
preprocessNoob |
Noob (normal-exponential out-of-band) is a background correction method with dye-bias normalization for Illumina Infinium methylation arrays |
funnorm |
preprocessFunnorm |
Functional normalization (FunNorm) is a between-array normalization method for the Illumina Infinium HumanMethylation450 platform |
Each normalization approach has some unique parameters
which can be modified through norm_parameters() function:
| parameters | Description |
|---|---|
| illumina | |
| bg.correct | Performs background correction |
| normalize | Performs controls normalization |
| reference | The reference array for control normalization |
| quantile | |
| fixOutliers | Low outlier Meth and Unmeth signals will be fixed |
| removeBadSamples | Remove bad samples |
| badSampleCutoff | The cutoff to label samples as ‘bad’ |
| quantileNormalize | Performs quantile normalization |
| stratified | Performs quantile normalization within region strata |
| mergeManifest | Merged to the output the information in the associated manifest package |
| sex | Sex of the samples |
| noob | |
| offset | Offset for the normexp background correct |
| dyeCorr | Performs dye normalization |
| dyeMethod | Dye bias correction to be done |
| funnorm | |
| nPCs | The number of principal components from the control probes |
| sex | Sex of the samples |
| bgCorr | Performs NOOB background correction before functional normalization |
| dyeCorr | Performs dye normalization |
| keepCN | Keeps copy number estimates |
We can obtain the default settings for each method by invoking
the function norm_parameters() with no arguments:
norm_parameters()$illumina
$illumina$bg.correct
[1] TRUE
$illumina$normalize
[1] "controls" "no"
$illumina$reference
[1] 1
$quantile
$quantile$fixOutliers
[1] TRUE
$quantile$removeBadSamples
[1] FALSE
$quantile$badSampleCutoff
[1] 10.5
$quantile$quantileNormalize
[1] TRUE
$quantile$stratified
[1] TRUE
$quantile$mergeManifest
[1] FALSE
$quantile$sex
NULL
$noob
$noob$offset
[1] 15
$noob$dyeCorr
[1] TRUE
$noob$dyeMethod
[1] "single" "reference"
$funnorm
$funnorm$nPCs
[1] 2
$funnorm$sex
NULL
$funnorm$bgCorr
[1] TRUE
$funnorm$dyeCorr
[1] TRUE
$funnorm$keepCN
[1] FALSEHowever, we can modify the parameter(s)
as the following example for illumina approach:
parameters <- norm_parameters(illumina = list("bg.correct" = FALSE))
parameters$illumina$bg.correct[1] FALSEWe are going to preprocess the IDAT files and
reference panel (3).
We need to specify the IDAT files directory and the reference
panel in RGChannelSet format.
As a result, we will obtain a GenomicRatioSet
object containing the control and case samples:
GRset <- epi_preprocess(baseDir,
reference_panel,
pattern = "SampleSheet.csv")5 Epimutations
5.1 Epimutations detection
The function epimutations() includes 6 methods for epimutation identification:
(1) Multivariate Analysis of variance (manova),
(2) Multivariate Linear Model (mlm),
(3) isolation forest (iForest),
(4) robust mahalanobis distance (mahdist)
(5) quantile and (6) beta.
To illustrate the following examples we are
going to use the dataset methy (section ??).
We need to separate the case and control samples:
case_samples <- methy[,methy$status == "case"]
control_samples <- methy[,methy$status == "control"]We can specify the chromosome or region to analyze which helps to reduce the execution time:
epi_mvo <- epimutations(case_samples,
control_samples,
method = "manova")The function epimutations_one_leave_out()
compared individual methylation profiles of a single
sample (regardless if they are cases or controls)
against all other samples from the same cohort.
To use this function we do not need to split the dataset.
To ilustrate this example we are going to use
the GRset dataset available in epimutacions package:
#manova (default method)
data(GRset)
epi_mva_one_leave_out<- epimutations_one_leave_out(GRset)5.2 Unique parameters
The epi_parameters() function is useful to set the individual
parameters for each outliers detection approach.
The following table describes the arguments:
| parameters | Description |
|---|---|
| Manova, mlm | |
| pvalue_cutoff | The threshold p-value to select which CpG regions are outliers |
| iso.forest | |
| outlier_score_cutoff | The threshold to select which CpG regions are outliers |
| ntrees | The number of binary trees to build for the model |
| mahdist.mcd | |
| nsamp | The number of subsets used for initial estimates in the MCD |
| quantile | |
| window_sz | The maximum distance between CpGs to be considered in the same DMR |
| offset_mean/offset_abs | The upper and lower threshold to consider a CpG an outlier |
| beta | |
| pvalue_cutoff | The minimum p-value to consider a CpG an outlier |
| diff_threshold | The minimum methylation difference between the CpG and the mean methylation to consider a position an outlier |
epi_parameters() with no arguments,
returns a list of the default settings for each method:
epi_parameters()$manova
$manova$pvalue_cutoff
[1] 0.05
$mlm
$mlm$pvalue_cutoff
[1] 0.05
$iForest
$iForest$outlier_score_cutoff
[1] 0.7
$iForest$ntrees
[1] 100
$mahdist
$mahdist$nsamp
[1] "deterministic"
$quantile
$quantile$window_sz
[1] 1000
$quantile$offset_abs
[1] 0.15
$quantile$qsup
[1] 0.995
$quantile$qinf
[1] 0.005
$beta
$beta$pvalue_cutoff
[1] 1e-06
$beta$diff_threshold
[1] 0.1The set up of any parameter can be done as the following example for manova:
parameters <- epi_parameters(manova = list("pvalue_cutoff" = 0.01))
parameters$manova$pvalue_cutoff[1] 0.015.3 Results description
The epimutations function returns a data frame (tibble)
containing all the epimutations identified in the given case sample.
If no epimutation is found,
the function returns a row containing the case sample
information and missing values for all other arguments.
The following table describes each argument:
| Column name | Description |
|---|---|
epi_id |
systematic name for each epimutation identified |
sample |
The name of the sample containing that epimutation |
chromosome
start
end |
The location of the epimutation |
sz |
The window’s size of the event |
cpg_ids |
The number of CpGs in the epimutation |
cpg_n |
The names of CpGs in the epimutation |
outlier_score |
For method manova it provides the approximation to F-test and the Pillai score, separated by /For method mlm it provides the approximation to F-test and the R2 of the model, separated by /For method iForest it provides the magnitude of the outlier score.For method beta it provides the mean p-value of all GpGs in that DMRFor methods quantile and mahdist it is filled with NA. |
pvalue |
For methods manova and mlm it provides the p-value obtained from the model.For method quantile, iForest, beta and mahdist it is filled with NA. |
outlier_direction |
Indicates the direction of the outlier with “hypomethylation” and “hypermethylation”. For manova, mlm, iForest, and mahdist it is computed from the values obtained from bumphunter.For beta is computed from the p value for each CpG using diff_threshold and pvalue_threshold arguments.For quantile it is computed from the location of the sample in the reference distribution (left vs. right outlier). |
adj_pvalue |
For methods manova and mlm it provides the adjusted p-value with
Benjamini-Hochberg based on the total number of regions detected by Bumphunter.For method quantile, iForest, mahdist and beta it is filled with NA. |
epi_region_id |
Name of the epimutation region as defined in candRegsGR. |
CRE |
cREs (cis-Regulatory Elements) as defined by ENCODE overlapping the epimutation region. |
CRE_type |
Type of cREs (cis-Regulatory Elements) as defined by ENCODE. |
As an example we are going to visualize the obtained
results with MANOVA method (epi_mvo):
dim(epi_mvo)[1] 51 16class(epi_mvo)[1] "tbl_df" "tbl" "data.frame"head(as.data.frame(epi_mvo), 1) epi_id sample chromosome start end sz cpg_n
1 epi_manova_1 GSM2562699 chr19 12777736 12777903 167 7
cpg_ids
1 cg20791841,cg25267526,cg03641858,cg25441478,cg14132016,cg23954461,cg03143365
outlier_score outlier_direction pvalue
1 302.558383959434/0.981008923520811 hypermethylation 3.441085e-33
adj_pvalue delta_beta epi_region_id CRE
1 2.236705e-31 0.2960902 chr19_12776725 EH38E1939817,EH38E1939818,EH38E1939819
CRE_type
1 pELS;pELS,CTCF-bound;PLS,CTCF-bound5.4 Epimutations annotations
The annotate_epimutations() function enriches the
identified epimutations.
It includes information about GENECODE gene names,
description of the regulatory feature provided by
methylation consortium, the location of the
CpG relative to the CpG island, OMIM accession
and description number and Ensembl region id, coordinates, type and tissue:
rst_mvo <- annotate_epimutations(epi_mvo)| Column name | Description |
|---|---|
GencodeBasicV12_NAME |
Gene names from the basic GENECODE build |
Regulatory_Feature_Group |
Description of the regulatory feature provided by the Methylation Consortium |
Relation_to_Island |
The location of the CpG relative to the CpG island |
OMIM_ACCOMIM_DESC |
OMIM accession and description number |
ensembl_reg_idensembl_reg_coordinatesensembl_reg_typeensembl_reg_tissues |
The Ensembl region id, coordinates, type and tissue |
5.5 Epimutation visualization
The plot_epimutations() function locates
the epimutations along the genome.
It plots the methylation values of the case
sample in red, the control samples in dashed
black lines and population mean in blue:
plot_epimutations(as.data.frame(epi_mvo[1,]), methy)
If we set the argument gene_annot == TRUE the plot includes the gene annotations:
plot_epimutations(as.data.frame(epi_mvo[1,]), methy, genes_annot = TRUE)To plot the chromatin marks H3K4me3,
H3K27me3 and H3K27ac we need to specify the argument regulation = TRUE:
- H3K4me3: commonly associated with the activation of transcription of nearby genes.
- H3K27me3: is used in epigenetics to look for inactive genes.
- H3K27ac: is associated with the higher activation of transcription and therefore defined as an active enhancer mark
plot_epimutations(as.data.frame(epi_mvo[1,]), methy, regulation = TRUE)6 Acknowledgements
The authors would like to thank the team who collaborated in the initial design of the package in the European BioHackathon 2020: Lordstrong Akano, James Baye, Alejandro Caceres, Pavlo Hrab, Raquel Manzano and Margherita Mutarelli. The authors also want to thank the organization of European BioHackathon 2020 for its support.
All the team members of Project #5 for the contribution to this package:
| Name | Surname | ORCID | Affiliation | Team |
|---|---|---|---|---|
| Leire | Abarrategui | 0000-0002-1175-038X | Faculty of Medical Sciences, Newcastle University, Newcastle-Upon-Tyne, UK; Autonomous University of Barcelona (UAB), Barcelona, Spain | Development |
| Lordstrong | Akano | 0000-0002-1404-0295 | College of Medicine, University of Ibadan | Development |
| James | Baye | 0000-0002-0078-3688 | Wellcome/MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge CB2 0AW, UK; Department of Physics, University of Cambridge, Cambridge CB2 3DY, UK | Development |
| Alejandro | Caceres | - | ISGlobal, Barcelona Institute for Global Health, Dr Aiguader 88, 08003 Barcelona, Spain; Centro de Investigación Biomédica en Red en Epidemiología y Salud Pública (CIBERESP), Madrid, Spain | Development |
| Carles | Hernandez-Ferrer | 0000-0002-8029-7160 | Centro Nacional de Análisis Genómico (CNAG-CRG), Center for Genomic, Regulation; Barcelona Institute of Science and Technology (BIST), Barcelona, Catalonia, Spain | Development |
| Pavlo | Hrab | 0000-0002-0742-8478 | Department of Genetics and Biotechnology, Biology faculty, Ivan Franko National University of Lviv | Validation |
| Raquel | Manzano | 0000-0002-5124-8992 | Cancer Research UK Cambridge Institute; University of Cambridge, Cambridge, United Kingdom | Reporting |
| Margherita | Mutarelli | 0000-0002-2168-5059 | Institute of Applied Sciences and Intelligent Systems (ISASI-CNR) | Validation |
| Carlos | Ruiz-Arenas | 0000-0002-6014-3498 | Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain | Reporting |
7 Session Info
sessionInfo()R version 4.2.0 (2022-04-22)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.4 LTS
Matrix products: default
BLAS: /home/biocbuild/bbs-3.15-bioc/R/lib/libRblas.so
LAPACK: /home/biocbuild/bbs-3.15-bioc/R/lib/libRlapack.so
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
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] BSgenome.Hsapiens.UCSC.hg19_1.4.3 BSgenome_1.64.0
[3] rtracklayer_1.56.0 kableExtra_1.3.4
[5] minfi_1.42.0 bumphunter_1.38.0
[7] locfit_1.5-9.5 iterators_1.0.14
[9] foreach_1.5.2 Biostrings_2.64.0
[11] XVector_0.36.0 SummarizedExperiment_1.26.1
[13] Biobase_2.56.0 MatrixGenerics_1.8.0
[15] matrixStats_0.62.0 GenomicRanges_1.48.0
[17] GenomeInfoDb_1.32.2 IRanges_2.30.0
[19] S4Vectors_0.34.0 ExperimentHub_2.4.0
[21] AnnotationHub_3.4.0 BiocFileCache_2.4.0
[23] dbplyr_2.2.0 BiocGenerics_0.42.0
[25] epimutacions_1.0.3 epimutacionsData_1.0.0
[27] BiocStyle_2.24.0
loaded via a namespace (and not attached):
[1] utf8_1.2.2
[2] tidyselect_1.1.2
[3] htmlwidgets_1.5.4
[4] RSQLite_2.2.14
[5] AnnotationDbi_1.58.0
[6] grid_4.2.0
[7] BiocParallel_1.30.3
[8] munsell_0.5.0
[9] codetools_0.2-18
[10] preprocessCore_1.58.0
[11] colorspace_2.0-3
[12] OrganismDbi_1.38.1
[13] filelock_1.0.2
[14] highr_0.9
[15] knitr_1.39
[16] rstudioapi_0.13
[17] robustbase_0.95-0
[18] labeling_0.4.2
[19] GenomeInfoDbData_1.2.8
[20] farver_2.1.0
[21] bit64_4.0.5
[22] rhdf5_2.40.0
[23] vctrs_0.4.1
[24] generics_0.1.2
[25] xfun_0.31
[26] biovizBase_1.44.0
[27] R6_2.5.1
[28] illuminaio_0.38.0
[29] AnnotationFilter_1.20.0
[30] bitops_1.0-7
[31] rhdf5filters_1.8.0
[32] cachem_1.0.6
[33] reshape_0.8.9
[34] DelayedArray_0.22.0
[35] assertthat_0.2.1
[36] Homo.sapiens_1.3.1
[37] promises_1.2.0.1
[38] BiocIO_1.6.0
[39] scales_1.2.0
[40] nnet_7.3-17
[41] gtable_0.3.0
[42] ensembldb_2.20.2
[43] rlang_1.0.2
[44] genefilter_1.78.0
[45] systemfonts_1.0.4
[46] splines_4.2.0
[47] lazyeval_0.2.2
[48] GEOquery_2.64.2
[49] dichromat_2.0-0.1
[50] checkmate_2.1.0
[51] BiocManager_1.30.18
[52] yaml_2.3.5
[53] reshape2_1.4.4
[54] backports_1.4.1
[55] GenomicFeatures_1.48.3
[56] httpuv_1.6.5
[57] Hmisc_4.7-0
[58] RBGL_1.72.0
[59] tools_4.2.0
[60] bookdown_0.27
[61] nor1mix_1.3-0
[62] ggplot2_3.3.6
[63] ellipsis_0.3.2
[64] jquerylib_0.1.4
[65] RColorBrewer_1.1-3
[66] siggenes_1.70.0
[67] Rcpp_1.0.8.3
[68] plyr_1.8.7
[69] base64enc_0.1-3
[70] sparseMatrixStats_1.8.0
[71] progress_1.2.2
[72] zlibbioc_1.42.0
[73] purrr_0.3.4
[74] RCurl_1.98-1.7
[75] prettyunits_1.1.1
[76] rpart_4.1.16
[77] openssl_2.0.2
[78] cluster_2.1.3
[79] ggrepel_0.9.1
[80] magrittr_2.0.3
[81] magick_2.7.3
[82] data.table_1.14.2
[83] ProtGenerics_1.28.0
[84] hms_1.1.1
[85] mime_0.12
[86] evaluate_0.15
[87] xtable_1.8-4
[88] XML_3.99-0.10
[89] jpeg_0.1-9
[90] mclust_5.4.10
[91] gridExtra_2.3
[92] compiler_4.2.0
[93] biomaRt_2.52.0
[94] tibble_3.1.7
[95] crayon_1.5.1
[96] htmltools_0.5.2
[97] later_1.3.0
[98] tzdb_0.3.0
[99] Formula_1.2-4
[100] tidyr_1.2.0
[101] DBI_1.1.3
[102] MASS_7.3-57
[103] rappdirs_0.3.3
[104] Matrix_1.4-1
[105] readr_2.1.2
[106] cli_3.3.0
[107] quadprog_1.5-8
[108] Gviz_1.40.1
[109] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
[110] pkgconfig_2.0.3
[111] GenomicAlignments_1.32.0
[112] foreign_0.8-82
[113] xml2_1.3.3
[114] svglite_2.1.0
[115] annotate_1.74.0
[116] bslib_0.3.1
[117] rngtools_1.5.2
[118] multtest_2.52.0
[119] beanplot_1.3.1
[120] webshot_0.5.3
[121] rvest_1.0.2
[122] doRNG_1.8.2
[123] scrime_1.3.5
[124] VariantAnnotation_1.42.1
[125] stringr_1.4.0
[126] digest_0.6.29
[127] graph_1.74.0
[128] rmarkdown_2.14
[129] base64_2.0
[130] htmlTable_2.4.0
[131] DelayedMatrixStats_1.18.0
[132] restfulr_0.0.15
[133] curl_4.3.2
[134] shiny_1.7.1
[135] Rsamtools_2.12.0
[136] rjson_0.2.21
[137] lifecycle_1.0.1
[138] nlme_3.1-158
[139] jsonlite_1.8.0
[140] Rhdf5lib_1.18.2
[141] viridisLite_0.4.0
[142] askpass_1.1
[143] limma_3.52.2
[144] fansi_1.0.3
[145] pillar_1.7.0
[146] lattice_0.20-45
[147] GO.db_3.15.0
[148] KEGGREST_1.36.2
[149] fastmap_1.1.0
[150] httr_1.4.3
[151] DEoptimR_1.0-11
[152] survival_3.3-1
[153] interactiveDisplayBase_1.34.0
[154] glue_1.6.2
[155] png_0.1-7
[156] isotree_0.5.15
[157] BiocVersion_3.15.2
[158] bit_4.0.4
[159] stringi_1.7.6
[160] sass_0.4.1
[161] HDF5Array_1.24.1
[162] blob_1.2.3
[163] org.Hs.eg.db_3.15.0
[164] latticeExtra_0.6-29
[165] memoise_2.0.1
[166] dplyr_1.0.9 References
Aref-Eshghi, Erfan, Eric G. Bend, Samantha Colaiacovo, Michelle Caudle, Rana Chakrabarti, Melanie Napier, Lauren Brick, et al. 2019. “Diagnostic Utility of Genome-Wide Dna Methylation Testing in Genetically Unsolved Individuals with Suspected Hereditary Conditions.” The American Journal of Human Genetics. https://doi.org/https://doi.org/10.1016/j.ajhg.2019.03.008.
Barbosa, Mafalda, Ricky S Joshi, Paras Garg, Alejandro Martin-Trujillo, Nihir Patel, Bharati Jadhav, Corey T Watson, et al. 2018. “Identification of Rare de Novo Epigenetic Variations in Congenital Disorders.” Nature Communications 9 (1): 1–11.
Jaffe, Andrew E, Peter Murakami, Hwajin Lee, Jeffrey T Leek, M Daniele Fallin, Andrew P Feinberg, and Rafael A Irizarry. 2012. “Bump Hunting to Identify Differentially Methylated Regions in Epigenetic Epidemiology Studies.” International Journal of Epidemiology 41 (1): 200–209.