Introduction

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.

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 @ref(fig:implementation)): (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).

Implementation of each outlier detection method

Setup

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")

Loading libraries

library(epimutacions)

Quick start

The workflow in figure @ref(fig:workflow) explains the main analysis in the epimutacions package.

The package allows two different types of inputs:

1. Case samples IDAT files (raw microarray intensities) together with RGChannelSet object 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 @ref(datasets)).
1. GenomicRatioSet object containing case and control samples.

The normalization (epi_preprocess()) converts the raw microarray intensities into usable methylation measurement (β 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.

Allowed data formats, normalization and input types

Datasets

The package contains 3 example datasets adapted from Gene Expression Omnibus (GEO):

1. 4 case samples IDAT files (GEO: GSE131350)
1. reference_panel: a RGChannelSet class object containing 22 healthy individuals (GEO: GSE127824)
1. methy: a GenomicRatioSet object 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 @ref(candreg)).

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(): 2024-10-24
# $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

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"]]

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"]]

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] FALSE

However, 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] FALSE

We are going to preprocess the IDAT files and reference panel (@ref(datasets)). 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")

Epimutations

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 @ref(datasets)). 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)

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] 0.1

The 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] 0.01

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 DMR \| For 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.

If no outliers are detected in the region, valures for sz, cpg_n, cpg_ids, outlier_score, outlier_direction, pvalue, adj_pvalue and epi_region_id are set to NA

As an example we are going to visualize the obtained results with MANOVA method (epi_mvo):

dim(epi_mvo)

[1] 51 16

class(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
1 302.558383959517/0.981008923520816  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-bound

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, omim = TRUE)

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_ACC` `OMIM_DESC`	OMIM accession and description number
`ensembl_reg_id` `ensembl_reg_coordinates` `ensembl_reg_type` `ensembl_reg_tissues`	The Ensembl region id, coordinates, type and tissue

As an example we are going to visualize different annotations

kableExtra::kable(
    rst_mvo[ c(27,32) ,c("epi_id", "cpg_ids", "Relation_to_Island")],
    row.names = FALSE) %>% 
  column_spec(1:3, width = "4cm")

epi_id	cpg_ids	Relation_to_Island
epi_manova_44	cg19560927,cg02876326,cg16167052	N_Shore/N_Shore/Island
epi_manova_51	cg01396855,cg08684893,cg02386644	N_Shore/N_Shore/N_Shore

kableExtra::kable(
    rst_mvo[ c(4,8,22) , c("epi_id", "OMIM_ACC", "OMIM_DESC")],
    row.names = FALSE ) %>% 
  column_spec(1:3, width = "4cm")

epi_id	OMIM_ACC	OMIM_DESC
epi_manova_4	248500	MANNOSID….
epi_manova_12	616689/6….	DEHYDRAT….
epi_manova_33	620332/6….	OOCYTE/Z….

kableExtra::kable(
    rst_mvo[ c(1:5), c("epi_id", "ensembl_reg_id", "ensembl_reg_type")],
    row.names = FALSE ) %>% 
  column_spec(1:3, width = "4cm")

epi_id	ensembl_reg_id	ensembl_reg_type
epi_manova_1	ENSR00000287480	Promoter
epi_manova_2	ENSR00000215007	Promoter
epi_manova_3	ENSR00000325316	Promoter
epi_manova_4	ENSR00001156259///ENSR00001156260	Promoter///CTCF Binding Site
epi_manova_5	ENSR00000324412	CTCF Binding Site

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:

p <- plot_epimutations(as.data.frame(epi_mvo[1,]), 
                       methy = methy, 
                       genes_annot = TRUE)

plot(p)

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

p <- plot_epimutations(as.data.frame(epi_mvo[1,]), 
                       methy =  methy, 
                       regulation = TRUE)

plot(p)

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

Session Info

sessionInfo()

R version 4.4.2 (2024-10-31)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 24.04.1 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        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: Etc/UTC
tzcode source: system (glibc)

attached base packages:
[1] parallel  stats4    stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] IlluminaHumanMethylationEPICanno.ilm10b2.hg19_0.6.0
 [2] kableExtra_1.4.0                                   
 [3] minfi_1.53.0                                       
 [4] bumphunter_1.49.0                                  
 [5] locfit_1.5-9.10                                    
 [6] iterators_1.0.14                                   
 [7] foreach_1.5.2                                      
 [8] Biostrings_2.75.1                                  
 [9] XVector_0.47.0                                     
[10] SummarizedExperiment_1.37.0                        
[11] Biobase_2.67.0                                     
[12] MatrixGenerics_1.19.0                              
[13] matrixStats_1.4.1                                  
[14] GenomicRanges_1.59.1                               
[15] GenomeInfoDb_1.43.1                                
[16] IRanges_2.41.1                                     
[17] S4Vectors_0.45.2                                   
[18] ExperimentHub_2.15.0                               
[19] AnnotationHub_3.15.0                               
[20] BiocFileCache_2.15.0                               
[21] dbplyr_2.5.0                                       
[22] BiocGenerics_0.53.3                                
[23] generics_0.1.3                                     
[24] epimutacions_1.11.0                                
[25] epimutacionsData_1.10.0                            
[26] BiocStyle_2.35.0                                   

loaded via a namespace (and not attached):
  [1] ProtGenerics_1.39.0                               
  [2] bitops_1.0-9                                      
  [3] isotree_0.6.1-1                                   
  [4] httr_1.4.7                                        
  [5] RColorBrewer_1.1-3                                
  [6] tools_4.4.2                                       
  [7] doRNG_1.8.6                                       
  [8] backports_1.5.0                                   
  [9] utf8_1.2.4                                        
 [10] R6_2.5.1                                          
 [11] HDF5Array_1.35.1                                  
 [12] lazyeval_0.2.2                                    
 [13] Gviz_1.51.0                                       
 [14] rhdf5filters_1.19.0                               
 [15] withr_3.0.2                                       
 [16] prettyunits_1.2.0                                 
 [17] gridExtra_2.3                                     
 [18] base64_2.0.2                                      
 [19] preprocessCore_1.69.0                             
 [20] cli_3.6.3                                         
 [21] labeling_0.4.3                                    
 [22] sass_0.4.9                                        
 [23] robustbase_0.99-4-1                               
 [24] readr_2.1.5                                       
 [25] genefilter_1.89.0                                 
 [26] askpass_1.2.1                                     
 [27] Rsamtools_2.23.0                                  
 [28] systemfonts_1.1.0                                 
 [29] foreign_0.8-87                                    
 [30] siggenes_1.81.0                                   
 [31] illuminaio_0.49.0                                 
 [32] svglite_2.1.3                                     
 [33] rentrez_1.2.3                                     
 [34] dichromat_2.0-0.1                                 
 [35] scrime_1.3.5                                      
 [36] BSgenome_1.75.0                                   
 [37] limma_3.63.2                                      
 [38] rstudioapi_0.17.1                                 
 [39] RSQLite_2.3.8                                     
 [40] BiocIO_1.17.0                                     
 [41] dplyr_1.1.4                                       
 [42] Matrix_1.7-1                                      
 [43] interp_1.1-6                                      
 [44] fansi_1.0.6                                       
 [45] abind_1.4-8                                       
 [46] lifecycle_1.0.4                                   
 [47] yaml_2.3.10                                       
 [48] rhdf5_2.51.0                                      
 [49] SparseArray_1.7.2                                 
 [50] grid_4.4.2                                        
 [51] blob_1.2.4                                        
 [52] crayon_1.5.3                                      
 [53] lattice_0.22-6                                    
 [54] GenomicFeatures_1.59.1                            
 [55] annotate_1.85.0                                   
 [56] KEGGREST_1.47.0                                   
 [57] sys_3.4.3                                         
 [58] maketools_1.3.1                                   
 [59] pillar_1.9.0                                      
 [60] knitr_1.49                                        
 [61] beanplot_1.3.1                                    
 [62] rjson_0.2.23                                      
 [63] codetools_0.2-20                                  
 [64] glue_1.8.0                                        
 [65] data.table_1.16.2                                 
 [66] vctrs_0.6.5                                       
 [67] png_0.1-8                                         
 [68] IlluminaHumanMethylationEPICmanifest_0.3.0        
 [69] gtable_0.3.6                                      
 [70] IlluminaHumanMethylation450kanno.ilmn12.hg19_0.6.1
 [71] cachem_1.1.0                                      
 [72] xfun_0.49                                         
 [73] S4Arrays_1.7.1                                    
 [74] mime_0.12                                         
 [75] survival_3.7-0                                    
 [76] statmod_1.5.0                                     
 [77] nlme_3.1-166                                      
 [78] bit64_4.5.2                                       
 [79] progress_1.2.3                                    
 [80] filelock_1.0.3                                    
 [81] bslib_0.8.0                                       
 [82] nor1mix_1.3-3                                     
 [83] IlluminaHumanMethylation450kmanifest_0.4.0        
 [84] rpart_4.1.23                                      
 [85] colorspace_2.1-1                                  
 [86] DBI_1.2.3                                         
 [87] Hmisc_5.2-0                                       
 [88] nnet_7.3-19                                       
 [89] tidyselect_1.2.1                                  
 [90] bit_4.5.0                                         
 [91] compiler_4.4.2                                    
 [92] curl_6.0.1                                        
 [93] httr2_1.0.6                                       
 [94] htmlTable_2.4.3                                   
 [95] xml2_1.3.6                                        
 [96] DelayedArray_0.33.2                               
 [97] rtracklayer_1.67.0                                
 [98] checkmate_2.3.2                                   
 [99] scales_1.3.0                                      
[100] DEoptimR_1.1-3                                    
[101] quadprog_1.5-8                                    
[102] rappdirs_0.3.3                                    
[103] stringr_1.5.1                                     
[104] digest_0.6.37                                     
[105] rmarkdown_2.29                                    
[106] GEOquery_2.75.0                                   
[107] htmltools_0.5.8.1                                 
[108] pkgconfig_2.0.3                                   
[109] jpeg_0.1-10                                       
[110] base64enc_0.1-3                                   
[111] sparseMatrixStats_1.19.0                          
[112] fastmap_1.2.0                                     
[113] ensembldb_2.31.0                                  
[114] rlang_1.1.4                                       
[115] htmlwidgets_1.6.4                                 
[116] UCSC.utils_1.3.0                                  
[117] DelayedMatrixStats_1.29.0                         
[118] farver_2.1.2                                      
[119] jquerylib_0.1.4                                   
[120] jsonlite_1.8.9                                    
[121] BiocParallel_1.41.0                               
[122] mclust_6.1.1                                      
[123] VariantAnnotation_1.53.0                          
[124] RCurl_1.98-1.16                                   
[125] magrittr_2.0.3                                    
[126] Formula_1.2-5                                     
[127] GenomeInfoDbData_1.2.13                           
[128] Rhdf5lib_1.29.0                                   
[129] munsell_0.5.1                                     
[130] Rcpp_1.0.13-1                                     
[131] stringi_1.8.4                                     
[132] zlibbioc_1.52.0                                   
[133] MASS_7.3-61                                       
[134] plyr_1.8.9                                        
[135] ggrepel_0.9.6                                     
[136] deldir_2.0-4                                      
[137] splines_4.4.2                                     
[138] multtest_2.63.0                                   
[139] hms_1.1.3                                         
[140] rngtools_1.5.2                                    
[141] buildtools_1.0.0                                  
[142] reshape2_1.4.4                                    
[143] biomaRt_2.63.0                                    
[144] BiocVersion_3.21.1                                
[145] XML_3.99-0.17                                     
[146] evaluate_1.0.1                                    
[147] latticeExtra_0.6-30                               
[148] biovizBase_1.55.0                                 
[149] BiocManager_1.30.25                               
[150] tzdb_0.4.0                                        
[151] tidyr_1.3.1                                       
[152] openssl_2.2.2                                     
[153] purrr_1.0.2                                       
[154] reshape_0.8.9                                     
[155] ggplot2_3.5.1                                     
[156] xtable_1.8-4                                      
[157] restfulr_0.0.15                                   
[158] AnnotationFilter_1.31.0                           
[159] viridisLite_0.4.2                                 
[160] tibble_3.2.1                                      
[161] memoise_2.0.1                                     
[162] AnnotationDbi_1.69.0                              
[163] GenomicAlignments_1.43.0                          
[164] cluster_2.1.6

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.

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