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title author date output header-includes mainfont fontsize always_allow_html editor_options
Supplemental Methods - Epigenenomics of Sample_Group in Heart Failure
Mark E. Pepin
31 December, 2021
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\usepackage{booktabs}
\usepackage{longtable}
\usepackage{array}
\usepackage{multirow}
\usepackage[table]{xcolor}
\usepackage{wrapfig}
\usepackage{float}
\usepackage{colortbl}
\usepackage{pdflscape}
\usepackage{tabu}
\usepackage{threeparttable}
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Code Author: Mark E. Pepin, MD, PhD, MS Contact: [email protected]{.email} Institution: Heidelberg University Hospital Location: 669 Neuenheimer Feld, Institute for Experimental Cardiology, 69120 Heidelberg, Germany

Preliminary Setup

Parameters

Define the parameters used, along with the conditions required for the current analysis. This segment must be modified for each analysis performed.

##Set the experimental conditions [MUST DO THIS MANUALLY]
Run_tS<-Sys.time()
TREATMENT=c("CON", "DCM")
CELL=c("iPSC")
STATISTIC = 0.05 #P statistic threshold used in this combination.
VARIABLE = TREATMENT
COMPARISON= paste0(CELL[1], "_",VARIABLE[2], "_vs_", VARIABLE[1])

# Candidate Gene Selection (RNA-sequencing) EDIT THIS LIST BASED ON INTERESTS.
GENES=c("LEP", "ADIPOQ", "PLIN1")
VAR1="Treatment"
VAR2="Diabetes"
# Single Bar Graph
library(dplyr)
my_comparisons <- list( c("CON", "DCM")) # Comparisons to make
## Create color based on Genotype
ann_colors = list(Treatment = c(DCM="darkgray", CON = "darkcyan"))
TreatmentColors<-ann_colors$Treatment
ann_colorTable<-as.data.frame(ann_colors)

ann_colGroup<-subset(ann_colorTable, rownames(ann_colorTable) %in% TREATMENT)
ann_colListGroup<-list(ann_colors$Treatment)
ann_colGroupVec<-ann_colGroup$Treatment
names(ann_colGroupVec)<-as.factor(rownames(ann_colGroup))
GROUP_colors<-list(ann_colGroupVec=ann_colGroupVec)

# Create Output Folder Structure
ifelse(!dir.exists(file.path(paste0("../2_Output/"))), dir.create(file.path(paste0("../2_Output/"))), FALSE)
## [1] FALSE
ifelse(!dir.exists(file.path(paste0("../2_Output/", COMPARISON))), dir.create(file.path(paste0("../2_Output/", COMPARISON))), FALSE)
## [1] FALSE

Packages

if (!require("pacman")) install.packages("pacman")
pacman::p_load(dplyr, Hmisc, openxlsx, corrplot, RColorBrewer, kableExtra, ggplot2, gridExtra, ggpubr, ggsignif, DESeq2, data.table, GenomicFeatures, biomaRt, Haplin, pheatmap, calibrate, ggrepel, tidyr, gtools)

Genome-wide DNA methylation - Illumina(R) HumanMethylation 450k Methylation

library(minfi)
library(limma)
library(shinyMethyl)
library(dplyr)
library(IlluminaHumanMethylation450kmanifest)
library(IlluminaHumanMethylation450kanno.ilmn12.hg19)
library(RColorBrewer)
#########Part 1: Importing the Data
#Parameters
##Get the array annotation
annoM450k<-getAnnotation(IlluminaHumanMethylation450kanno.ilmn12.hg19)
annoM450k<-dplyr::select(as.data.frame(annoM450k), Name, chr, pos, Relation_to_Island, UCSC_RefGene_Name, UCSC_RefGene_Accession, UCSC_RefGene_Group, Regulatory_Feature_Group)
#Import the sample sheet
targets<-read.metharray.sheet(base="../1_Input/IDAT", pattern=".csv")
## [1] "../1_Input/IDAT/SampleSheet.csv"
 #Import the annotation file
AnnoTargets<-targets
targets$ID <- paste(targets$Sample_Group,targets$Sample_Name,sep=".")
targets$Sample_Well<-AnnoTargets$Sample_Well
targets$Sample_Plate<-AnnoTargets$Sample_Plate
targets$Sample_Group<-AnnoTargets$Sample_Group
targets$Sample_Group<-factor(targets$Sample_Group, levels = c("CON", "DCM"))
targets$Sex<-AnnoTargets$Sex
targets$Outcome<-AnnoTargets$Outcome
targets$Conc<-AnnoTargets$Conc
targets$Col_ID<-AnnoTargets$Col_ID
targets$Tissue<-AnnoTargets$Tissue
targets<-targets %>% filter(Tissue %in% CELL)

#Import the array data from input directory (red and green .idat files)
RGSet<-read.metharray.exp(base = "../1_Input/IDAT", targets = targets, verbose = TRUE)
sampleNames(RGSet)<-targets$Sample_Name
##Quality Control
#First step is to identify CpGs that failed to identify methylated positions (defined by expression intensity that reflects background levels)
detP<-detectionP(RGSet)
PLOT.COL <- brewer.pal(8,"Dark2")
##Detection P-value Plot
pdf(file=paste0("../2_Output/", COMPARISON, "/", COMPARISON, "_detectionP.pdf"))
par(mfrow=c(1,1))
barplot(colMeans(detP), col=PLOT.COL[factor(targets$Sample_Group)], 
        cex.names=0.8, ylim=c(0,0.005), ylab="Mean detection p-values")
abline(h=0.05,col="red")
legend("topleft", legend=levels(factor(targets$Sample_Group)), fill=PLOT.COL, 
       bg="white")
dev.off()
## quartz_off_screen 
##                 2
## Filter RGSet by detection P-value
keep <- colMeans(detP) < 0.05
RGSet <- RGSet[,keep]
RGSet
## class: RGChannelSet 
## dim: 622399 24 
## metadata(0):
## assays(2): Green Red
## rownames(622399): 10600313 10600322 ... 74810490 74810492
## rowData names(0):
## colnames(24): A1_cells_bo A2_cells_bo ... C9_cells_co C10_cells_co
## colData names(17): Sample_Name Sample_Well ... ID filenames
## Annotation
##   array: IlluminaHumanMethylation450k
##   annotation: ilmn12.hg19
##QC Report
qcReport(RGSet, sampNames=targets$ID, sampGroups=targets$Sample_Group, pdf="qcReport.pdf")
## quartz_off_screen 
##                 2
#
par(mfrow=c(1,1))
barplot(colMeans(detP), col=PLOT.COL[factor(targets$Sample_Group)], las=2, 
        cex.names=0.8, ylim=c(0,0.005), ylab="Mean detection p-values")
abline(h=0.05,col="red")
legend("topleft", legend=levels(factor(targets$Sample_Group)), fill=PLOT.COL, 
       bg="white")

# #Determine the fraction of "failed" CpG probes (those which failed to identify a methylated CpG)
# colMeans(failed)
#Convert R/G to Methylated/Unmethylated in an object of class MethylSet
MSet<-preprocessRaw(RGSet)
#QC data
qc<-getQC(MSet)
plotQC(qc)

pdf(file=paste0("../2_Output/", COMPARISON, "/", COMPARISON, "_QC.Methyl.pdf"))
plotQC(qc)
dev.off()
## quartz_off_screen 
##                 2
##Density plot
# densityPlot(RGSet, sampGroups = targets$Sample_Group, main= "Beta", xlab = "Beta")
pdf(file=paste0("../2_Output/", COMPARISON, "/", COMPARISON, "_densityPlot.pdf"))
densityPlot(RGSet, sampGroups = targets$Sample_Group, main= "Beta", xlab = "Beta")
dev.off()
## quartz_off_screen 
##                 2
densityBeanPlot(RGSet, sampGroups = targets$Sample_Group)

pdf(file=paste0("../2_Output/", COMPARISON, "/", COMPARISON, "_BeanPlot.pdf"))
densityBeanPlot(RGSet, sampGroups = targets$Sample_Group)
dev.off()
## quartz_off_screen 
##                 2
qcReport(RGSet, pdf= paste0("../2_Output/", COMPARISON, "/", COMPARISON, "_qcReport.pdf"))
## quartz_off_screen 
##                 2
# #Convert to a shinyMethyl dataset (nice summary of the quality control)
# summarized.data <- shinySummarize(RGSet)
# runShinyMethyl(summarized.data)

gRatioSet.quantile <- preprocessQuantile(RGSet, fixOutliers = TRUE, removeBadSamples = TRUE, badSampleCutoff = 10.5, quantileNormalize = TRUE, stratified = TRUE, mergeManifest = FALSE)
beta.all<-getBeta(RGSet)
write.csv(beta.all, "../1_Input/EPIC.betaValues.csv")

Supplemental Figure SXX: Outlier Analysis

par(mfrow=c(1,1))
barplot(colMeans(detP), col=PLOT.COL[factor(targets$Sample_Group)], las=2, 
        cex.names=0.8, ylim=c(0,0.005), ylab="Mean detection p-values")
abline(h=0.05,col="red")
legend("topleft", legend=levels(factor(targets$Sample_Group)), fill=PLOT.COL, 
       bg="white")

Supplemental Figure

Figure 2A: MDS Plot - Unsupervised Clustering by "Sample_Group"

Because such a robust racial signature in cardiac DNA methylation was seen in the pilot analysis, we reproduced the unsupervised method in the larger cohort. This time, we continue to see a distinct racial difference. Furthermore, we found that this racially-determined clustering persisted to among the 500,000 most-variable CpG probes in the EPIC array.

PCA

#Plot Features of the PCA
library(dplyr)
library(plotly)
##Import the data to be used for PCA
Index_PCA<-targets
rownames(Index_PCA)<-Index_PCA$Sample_Name
PCA_data<-as.data.frame(getM(gRatioSet.quantile))
#transpose the dataset (required for PCA)
data.pca<-t(PCA_data)
data.pca<-as.data.frame(data.pca)
##merge the file
data.pca_Final<-merge(Index_PCA, data.pca, by=0)
rownames(data.pca_Final)<-data.pca_Final$Row.names
pca.comp<-prcomp(data.pca_Final[,(ncol(Index_PCA)+2):ncol(data.pca_Final)])

pcaCharts=function(x) {
    x.var <- x$sdev ^ 2
    x.pvar <- x.var/sum(x.var)
    par(mfrow=c(2,2))
    plot(x.pvar,xlab="Principal component",
         ylab="Proportion of variance", ylim=c(0,1), type='b')
    plot(cumsum(x.pvar),xlab="Principal component",
         ylab="Cumulative Proportion of variance",
         ylim=c(0,1),
         type='b')
    screeplot(x)
    screeplot(x,type="l")
    par(mfrow=c(1,1))
}
pcaCharts(pca.comp)

png(file=paste0("../2_Output/", COMPARISON,  "/", COMPARISON, "_PCA.Charts.png"))
pcaCharts(pca.comp)
dev.off()
## quartz_off_screen 
##                 2

3-Dimensional PCA

From the previous calculations, it is seens that only 2 principal components are necessary (accounting for >80% cumulative variance). Nonetheless, below is a 3-D PCA to ensure that all groups are characterize to higher-degree of stringency. Nevertheless, a racial difference could not be appreciated.

##Create a 3D-PCA for Inspection
library(plotly)
##Index
Index_PCA<-targets
rownames(Index_PCA)<-Index_PCA$Sample_Name

PCs<-merge(pca.comp$x, Index_PCA, by=0)
rownames(PCs)<-PCs$Row.names
PCs$Group <- as.factor(PCs$Sample_Group)
fig <- plot_ly(PCs, x = ~PC1, y = ~PC2, z = ~PC3, color = ~Sample_Group, text = ~paste('Sample_Name:', Sample_Name, '<br>Tissue:', Tissue, '<br>Outcome:', Outcome, '<br>Sex:', Sex))
fig <- fig %>% add_markers()
fig <- fig %>% layout(scene = list(xaxis = list(title = 'PC1'),
                     yaxis = list(title = 'PC2'),
                     zaxis = list(title = 'PC3')))
fig
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Differential Methylation of iPSCs (DCM vs. CON)

Using Minfi to compute differential expression

Owing to the apparent separation by patient Sample_Group, we chose to identify the CpG sites responsible for a Sample_Group-based epigenomic difference.

#Quantification and Differential Expression Analysis
RGSet<-read.metharray.exp(base = "../1_Input/IDAT", targets = targets, verbose = TRUE)
# sampleNames(RGSet)<-targets$Sample_Name
GRset.funnorm <- preprocessFunnorm(RGSet)
beta <- getBeta(GRset.funnorm)
M<-getM(GRset.funnorm)
Condition <- pData(GRset.funnorm)$Sample_Group
dmp <- dmpFinder(beta, pheno = Condition  , type = "categorical")
DMPs<-merge(dmp, annoM450k, by= 0)
DMPs<-as.data.frame(DMPs)
rownames(DMPs)<-DMPs$Row.names
DMPs<-DMPs %>% dplyr::select(-Row.names)
#create an beta table
beta.table<-as.data.frame(t(beta))
beta.table$Col_ID<-rownames(beta.table)
beta_named<-merge(beta.table, targets, by = "Col_ID")
rownames(beta_named)<-beta_named$Sample_Name
beta_named<-beta_named %>% dplyr::select(-names(targets)) %>% t()
write.csv(beta_named, paste0("../2_Output/", COMPARISON, "/", COMPARISON, "_beta.table.csv"))
#Add beta values to statistics
Results_dmp<-merge(DMPs, beta_named, by = 0)
#Calculate Average CpG Methylation by Sample_Group
library(dplyr)
library(matrixStats)
Results<-Results_dmp %>% replace(is.na(.), 0) %>% dplyr::mutate(
  DCM_SD = rowSds(as.matrix(Results_dmp[,targets$Sample_Name[targets$Sample_Group=="DCM"]])),
  DCM_Mean = rowMeans(as.matrix(Results_dmp[,targets$Sample_Name[targets$Sample_Group=="DCM"]])),
  CON_SD = rowSds(as.matrix(Results_dmp[,targets$Sample_Name[targets$Sample_Group=="CON"]])),
  CON_Mean = rowMeans(as.matrix(Results_dmp[,targets$Sample_Name[targets$Sample_Group=="CON"]])),
  Methylation.Diff=(DCM_Mean-CON_Mean)*100)
rownames(Results)<-Results$Row.names
Results_dmp_p05<-filter(Results, pval<0.05)
Results_dmp_q05<-filter(Results, qval<0.05)
#########################################
#Identify Promoter-associated CpG Islands
library(tidyr)
PromCGI<-dplyr::filter(Results_dmp_p05, grepl("Island", Relation_to_Island), grepl("TSS", UCSC_RefGene_Group))
#Separate Gene Names into unique rows
PromCGI_sep<-PromCGI %>% mutate(UCSC_RefGene_Name = strsplit(as.character(UCSC_RefGene_Name), ";")) %>% unnest(UCSC_RefGene_Name) %>% distinct()
#Save a copy of the countData
library(openxlsx)
wb_countData<-createWorkbook()
addWorksheet(wb_countData, "Unfiltered")
  writeData(wb_countData, "Unfiltered", Results, startCol = 1)
addWorksheet(wb_countData, "P_0.05")
  writeData(wb_countData, "P_0.05", Results_dmp_p05, startCol = 1)
addWorksheet(wb_countData, "Q_0.05")
  writeData(wb_countData, "Q_0.05", Results_dmp_q05, startCol = 1)
addWorksheet(wb_countData, "Promoter.CGI")
  writeData(wb_countData, "Promoter.CGI", PromCGI_sep, startCol = 1)
saveWorkbook(wb_countData, file = paste0("../2_Output/", COMPARISON, "/", COMPARISON, "_DMPs.xlsx"), overwrite = TRUE)

Distribution of Methylation by Genomic and CpG Annotation

The following figure illustrates the enrichment of differential methylation with respect to CpG-based and genomic annotations.

library(plotly)
library(dplyr)
library(tidyr)
library(stringr)
library(reshape2)
library(readxl)
library(kableExtra)
library(ComplexHeatmap)
library(openxlsx)

##Create a regional annotation matrix of the m450k array
Region_epic<-Results_dmp %>% 
  dplyr::select(Row.names, UCSC_RefGene_Group, Relation_to_Island)
Stage1<-Region_epic %>% 
  mutate(UCSC_RefGene_Group = strsplit(as.character(UCSC_RefGene_Group), ";")) %>% 
  unnest(UCSC_RefGene_Group) 
Stage2<-Stage1 %>% 
  mutate(Relation_to_Island = strsplit(as.character(Relation_to_Island), ";")) %>%  
  unnest(Relation_to_Island)
Stage3<-distinct(Stage2)
Regional.Groups<-dplyr::group_by_(Stage3, "UCSC_RefGene_Group", "Relation_to_Island") %>% 
  tally()
Region.matrix<-Regional.Groups %>% 
  spread(Relation_to_Island, n)
Region.matrix<-Region.matrix %>% 
  dplyr::select(UCSC_RefGene_Group, OpenSea, N_Shelf, N_Shore, Island, S_Shore, S_Shelf)
rownames(Region.matrix)<-Region.matrix$UCSC_RefGene_Group
Region.matrix<-Region.matrix[c("TSS1500", "TSS200", "5'UTR", "1stExon", "Body", "3'UTR"),]
rownames(Region.matrix)<-Region.matrix$UCSC_RefGene_Group
Contour_3D_epic<-Region.matrix[,-1]
rownames(Contour_3D_epic)<-Region.matrix$UCSC_RefGene_Group
write.xlsx(Contour_3D_epic, "../2_Output/Contour.3D_EPIC.xlsx", overwrite = TRUE)

##Create a regional annotation matrix of differentially-methylated positions
Region_p05<-Results_dmp_p05 %>% dplyr::select(Row.names, UCSC_RefGene_Group, Relation_to_Island)
Stage1<-Region_p05 %>% mutate(UCSC_RefGene_Group = strsplit(as.character(UCSC_RefGene_Group), ";")) %>% unnest(UCSC_RefGene_Group) 
Stage2<-Stage1 %>% mutate(Relation_to_Island = strsplit(as.character(Relation_to_Island), ";")) %>%  unnest(Relation_to_Island)
Stage3<-distinct(Stage2)
Regional.Groups<-dplyr::group_by_(Stage3, "UCSC_RefGene_Group", "Relation_to_Island") %>% tally()
Region.matrix<-Regional.Groups %>% spread(Relation_to_Island, n)
Region.matrix<-Region.matrix %>% dplyr::select(UCSC_RefGene_Group, OpenSea, N_Shelf, N_Shore, Island, S_Shore, S_Shelf)
rownames(Region.matrix)<-Region.matrix$UCSC_RefGene_Group
Region.matrix<-Region.matrix[c("TSS1500", "TSS200", "5'UTR", "1stExon", "Body", "3'UTR"),]
rownames(Region.matrix)<-Region.matrix$UCSC_RefGene_Group
Contour_3D<-Region.matrix[,-1]
rownames(Contour_3D)<-Region.matrix$UCSC_RefGene_Group
write.csv(Contour_3D, "../2_Output/Contour.3D_DMPs.p05.csv")
### Identify DMP Enrichment (IMPORTANT)
Enrichment_Region<-Contour_3D/Contour_3D_epic
Promoter_enr<-Enrichment_Region[rownames(Enrichment_Region)=="TSS200",] + Enrichment_Region[rownames(Enrichment_Region)=="TSS1500",]
rownames(Promoter_enr)<-"Promoter"
Enrichment_Region<-Enrichment_Region %>% 
  filter(!grepl("TSS", rownames(Enrichment_Region))) %>%
  rbind(Promoter_enr, .) %>%
  data.matrix()
paletteLength<-100
myColor <- colorRampPalette(c("dodgerblue4", "white", "brown4"))(paletteLength)
pheatmap(Enrichment_Region, color = myColor, cluster_rows = FALSE, cluster_cols = FALSE, filename = paste0("../2_Output/", COMPARISON, "/", COMPARISON, "_DMP.Distribution.pdf"))

![](README_files/figure-html/Methylation Distribution-1.png)

write.csv(Enrichment_Region, "DCM_vs_CON.csv")
##Make a Table of the CpG Methylation Distribution
Enrichment_Region %>% kable( align="c", booktabs=T, 
                     caption="Methylation Distribution") %>% 
  kable_styling(latex_options=c("striped", "condensed", "repeat_header"))
Methylation Distribution
OpenSea N_Shelf N_Shore Island S_Shore S_Shelf
Promoter 0.1164621 0.1029990 0.1242210 0.1460420 0.1156378 0.1037933
5'UTR 0.0530359 0.0622389 0.0569429 0.0741839 0.0573959 0.0576832
1stExon 0.0588488 0.0489130 0.0623833 0.0746424 0.0625000 0.0370370
Body 0.0523911 0.0493794 0.0510093 0.0633285 0.0507863 0.0473963
3'UTR 0.0485610 0.0415879 0.0554833 0.0638298 0.0563931 0.0479303
write.xlsx(Enrichment_Region, paste0("../2_Output/", COMPARISON, "/", COMPARISON, "_DMP.Enrichment_3D.xlsx"), overwrite = T)
color <- colorRampPalette(c("grey", "orange", "red"))
t <- list(
  family = "times",
  size = 16,
  color = "black")
x_axis<-list(title = 'CpG Region', 
                     type="category", 
                     zeroline=TRUE, 
                     showline=TRUE, 
                     zerolinewidth = 4, 
            zerolinecolor="darkgrey", 
            linecolor="darkgrey", 
            linewidth=4, 
            titlefont=t, 
            tickfont=t)
y_axis<-list(title = 'Gene Region', 
                     type="category", 
                     zeroline=TRUE, 
                     showline=TRUE, 
                     zerolinewidth = 4, 
            zerolinecolor="darkgrey", 
            linecolor="darkgrey", 
            linewidth=4, 
            titlefont=t, 
            tickfont=t)
z_axis<-list(title = 'Number of DMPs', 
                     zerolinewidth = 4, 
                    zerolinecolor="darkgrey", 
                    linecolor="darkgrey", 
                    linewidth=4, 
                    titlefont=t, 
                    tickfont=t)
q<-plot_ly(z=~Enrichment_Region, colors=color(10), 
    text=as.character(rownames(Enrichment_Region))) %>% add_surface() %>% 
    layout(scene = list(xaxis = x_axis, yaxis = y_axis, zaxis = z_axis))
q #must comment out for PDF generation via knitr (Pandoc).
<div id="htmlwidget-abe0d5b12822ca5cb440" style="width:672px;height:480px;" class="plotly html-widget"></div>
<script type="application/json" data-for="htmlwidget-abe0d5b12822ca5cb440">{"x":{"visdat":{"2ee73db9e5dd":["function () ","plotlyVisDat"]},"cur_data":"2ee73db9e5dd","attrs":{"2ee73db9e5dd":{"z":{},"text":["Promoter","5'UTR","1stExon","Body","3'UTR"],"colors":["#BEBEBE","#CCB893","#DAB269","#E9AD3F","#F7A715","#FF9200","#FF6E00","#FF4900","#FF2400","#FF0000"],"alpha_stroke":1,"sizes":[10,100],"spans":[1,20],"type":"surface","inherit":true}},"layout":{"margin":{"b":40,"l":60,"t":25,"r":10},"scene":{"xaxis":{"title":"CpG Region","type":"category","zeroline":true,"showline":true,"zerolinewidth":4,"zerolinecolor":"darkgrey","linecolor":"darkgrey","linewidth":4,"titlefont":{"family":"times","size":16,"color":"black"},"tickfont":{"family":"times","size":16,"color":"black"}},"yaxis":{"title":"Gene Region","type":"category","zeroline":true,"showline":true,"zerolinewidth":4,"zerolinecolor":"darkgrey","linecolor":"darkgrey","linewidth":4,"titlefont":{"family":"times","size":16,"color":"black"},"tickfont":{"family":"times","size":16,"color":"black"}},"zaxis":{"title":"Number of DMPs","zerolinewidth":4,"zerolinecolor":"darkgrey","linecolor":"darkgrey","linewidth":4,"titlefont":{"family":"times","size":16,"color":"black"},"tickfont":{"family":"times","size":16,"color":"black"}}},"hovermode":"closest","showlegend":false,"legend":{"yanchor":"top","y":0.5}},"source":"A","config":{"modeBarButtonsToAdd":["hoverclosest","hovercompare"],"showSendToCloud":false},"data":[{"colorbar":{"title":"Enrichment_Region","ticklen":2,"len":0.5,"lenmode":"fraction","y":1,"yanchor":"top"},"colorscale":[["0","rgba(190,190,190,1)"],["0.0416666666666666","rgba(196,188,174,1)"],["0.0833333333333333","rgba(201,185,158,1)"],["0.125","rgba(206,183,142,1)"],["0.166666666666667","rgba(212,181,126,1)"],["0.208333333333333","rgba(217,179,110,1)"],["0.25","rgba(222,177,95,1)"],["0.291666666666667","rgba(228,175,80,1)"],["0.333333333333333","rgba(233,173,63,1)"],["0.375","rgba(238,171,50,1)"],["0.416666666666667","rgba(244,169,35,1)"],["0.458333333333333","rgba(248,164,19,1)"],["0.5","rgba(251,157,11,1)"],["0.541666666666667","rgba(254,149,3,1)"],["0.583333333333333","rgba(255,137,0,1)"],["0.625","rgba(255,124,0,1)"],["0.666666666666667","rgba(255,110,0,1)"],["0.708333333333333","rgba(255,97,0,1)"],["0.75","rgba(255,83,0,1)"],["0.791666666666667","rgba(255,69,0,1)"],["0.833333333333333","rgba(255,57,0,1)"],["0.875","rgba(255,42,0,1)"],["0.916666666666667","rgba(255,30,0,1)"],["0.958333333333333","rgba(255,19,0,1)"],["1","rgba(255,0,0,1)"]],"showscale":true,"z":[[0.1164621465399,0.102998995174501,0.124221049754897,0.146041950369168,0.115637795393847,0.103793295570619],[0.0530358898721731,0.0622389306599833,0.0569429198682766,0.0741838816628411,0.0573958674975402,0.0576832151300236],[0.0588487972508591,0.0489130434782609,0.0623832648487112,0.0746423541620339,0.0625,0.037037037037037],[0.0523911393278587,0.0493794088757925,0.0510093237983163,0.0633284583420387,0.0507863308501833,0.0473962571196094],[0.0485609908224182,0.0415879017013233,0.0554833116601647,0.0638297872340425,0.056393076493579,0.0479302832244009]],"text":["Promoter","5'UTR","1stExon","Body","3'UTR"],"type":"surface","frame":null}],"highlight":{"on":"plotly_click","persistent":false,"dynamic":false,"selectize":false,"opacityDim":0.2,"selected":{"opacity":1},"debounce":0},"shinyEvents":["plotly_hover","plotly_click","plotly_selected","plotly_relayout","plotly_brushed","plotly_brushing","plotly_clickannotation","plotly_doubleclick","plotly_deselect","plotly_afterplot","plotly_sunburstclick"],"base_url":"https://plot.ly"},"evals":[],"jsHooks":[]}</script>

Figure XX: Heatmap and Hierarchical Clustering of Differential Methylation (P < 0.05)

Due to the prominent signature of CpG Island-associated promoter methylation in iPSCs exposed to serum for failing and non-failing patients, we examined all DMPs present within this region via heatmap.

library(ComplexHeatmap)
library(dplyr)
##Import Data Matrix
# betaHM<-read.csv("../1_Input/EPIC.betaValues.csv", row.names = 1)
## Filters to Apply to DMP
pvalue_threshold=0.001
METHYLATION=0
DMP_location="Island"
Gene_region="TSS"
Samples<-AnnoTargets$Sample_Name
##Filter Differential Methylation Data
DMP.p05<-Results %>% filter(pval<pvalue_threshold)
DMP.p05<-DMP.p05 %>% dplyr::select(Row.names, 
                            Methylation.Diff, 
                            pval, 
                            qval, 
                            Relation_to_Island, 
                            UCSC_RefGene_Group, 
                            chr, 
                            pos, 
                            matches(Samples)) %>%
                      filter(abs(Methylation.Diff)>METHYLATION)
DMP.p05.Region<-DMP.p05 %>% 
  # filter(grepl(DMP_location, Relation_to_Island)) %>%
  filter(grepl(Gene_region, UCSC_RefGene_Group)) %>% 
  dplyr::select(matches(AnnoTargets$Sample_Name)) %>%
  data.matrix()
#Import the Index File
LVAD_Counts_Data <- targets
rownames(LVAD_Counts_Data)<-targets$Sample_Name
Index<-LVAD_Counts_Data %>% dplyr::select(Sample_Group)
Index<-as.data.frame(Index)
paletteLength <- 100
ann_colors = list(Sample_Group = c(DCM="darkcyan", CON="darkgray"), Outcome = c(Bad = "firebrick2", Good = "darkgoldenrod2", CON = "black"))
myColor <- colorRampPalette(c("dodgerblue4", "white", "goldenrod2"))(paletteLength)
pheatmap::pheatmap(DMP.p05.Region, scale="row", 
                      cluster_cols = TRUE, 
                      cluster_rows = TRUE,
                      #breaks = myBreaks,
                      cutree_cols = 2,
                      cutree_rows = 2,
                      angle_col = 45,
                      fontsize_col = 8,
                      color = myColor, 
                      show_rownames = FALSE, 
                      border_color = NA, 
                      annotation_col = Index,
                      annotation_colors=ann_colors,
                      filename = paste0("../2_Output/", COMPARISON, "/", COMPARISON, "_", DMP_location, ".", Gene_region,".heatmap.pdf"))

pheatmap::pheatmap(DMP.p05.Region, scale="row", 
                      cluster_cols = TRUE, 
                      cluster_rows = TRUE,
                      #breaks = myBreaks,
                      cutree_cols = 2,
                      cutree_rows = 2,
                      angle_col = 45,
                      fontsize_col = 8,
                      color = myColor, 
                      show_rownames = FALSE, 
                      border_color = NA, 
                      annotation_col = Index,
                      annotation_colors=ann_colors)

Figure XX: Volcano Plot - DMPs

# Load packages
library(dplyr)
library(ggplot2)
library(ggrepel)
library(openxlsx)
Results<-read.xlsx(paste0("../2_Output/", COMPARISON, "/", COMPARISON, "_DMPs.xlsx"), sheet = "Unfiltered")
#Read data from the web
Volcano_data = mutate(Results, sig=ifelse(Results$pval<0.05 & abs(Results$Methylation.Diff)>5, "P<0.05 and |Methylation| > 5%", "Not Sig"), minuslogpvalue = -log(pval), Methylation=Methylation.Diff)

#Split gene names for labelling
Volcano_data_split<-Volcano_data %>% mutate(UCSC_RefGene_Name = strsplit(as.character(UCSC_RefGene_Name), ";")) %>% unnest(UCSC_RefGene_Name) %>% distinct()

max(Volcano_data$minuslogpvalue, na.rm = TRUE)
## [1] 15.86107
# Results<-Results %>% filter(grepl("TSS", UCSC_RefGene_Group))
#plot the ggplot
p = ggplot(Volcano_data_split, aes(Methylation, minuslogpvalue)) + theme(panel.background = element_rect("white", colour = "black", size=2), panel.grid.major = element_line(colour = "gray50", size=.75), panel.grid.minor = element_line(colour = "gray50", size=0.4)) + 
geom_point(aes(fill=sig, size = minuslogpvalue), colour="grey", shape=21, stroke = 0, alpha = 8/10) + labs(x=expression(Methylation_Change), y=expression(-Log[10](P-value))) + xlim(min(Volcano_data$Methylation, na.rm = TRUE),max(Volcano_data$Methylation, na.rm = TRUE))+ ylim(-0, max(Volcano_data$minuslogpvalue, na.rm = TRUE)) +   geom_hline(yintercept = 0, size = 1) + geom_vline(xintercept=0, size=1)+ 
  scale_fill_manual(values=c("grey", "darkgoldenrod2")) +
  scale_size_continuous(range = c(.25, 4))
pdf(file = paste0("../2_Output/", COMPARISON, "/", COMPARISON, "Volcano.Plot.pdf"), height = 6, width = 10)
p+
  geom_text_repel(data=top_n(filter(Volcano_data_split, pval<0.05, Methylation < 0, sig!="Not Sig"), 10, -Methylation), aes(label=UCSC_RefGene_Name)) +
  geom_text_repel(data=top_n(filter(Volcano_data_split, pval<0.05, Methylation > 0, sig!="Not Sig"), 10, Methylation), aes(label=UCSC_RefGene_Name))
dev.off()
## quartz_off_screen 
##                 2

Combined Analysis - Biopsy and iPSC

library(openxlsx)
library(dplyr)
library(tidyr)
library(ComplexHeatmap)
library(minfi)
NUMBER = 20
STAT = 0.001
#Index
AnnoTargets<-read.metharray.sheet(base="../1_Input/IDAT", pattern=".csv")
## [1] "../1_Input/IDAT/SampleSheet.csv"
rownames(AnnoTargets)<-AnnoTargets$Sample_Name
Index_combined<-AnnoTargets %>% dplyr::select(Sample_Group, Tissue, Sample_ID)
#iPSC Data
DMPs_iPSC<-read.xlsx("../2_Output/iPSC_DCM_vs_CON/iPSC_DCM_vs_CON_DMPs.xlsx", sheet = "P_0.05", rowNames = T)
iPSC_stats<-DMPs_iPSC %>% dplyr::select(Methylation_iPSC = Methylation.Diff, pval_iPSC = pval, adj.P.Val_iPSC = qval, Name:Regulatory_Feature_Group)


#biopsies
DMPs_heart<-read.xlsx("../2_Output/heart_DCM_vs_CON/heart_DCM_vs_CON_DMPs.xlsx", sheet = "P_0.05", rowNames = T)
heart_stats<-DMPs_heart %>% dplyr::select(Methylation_heart = Methylation.Diff, pval_heart = pval, adj.P.Val_heart = qval, Name:Regulatory_Feature_Group)
heart_index<-AnnoTargets %>% dplyr::filter(Tissue=="heart") %>% dplyr::select(Tissue, Sample_Group, Sample_ID)

#Beta
beta_all<-read.csv("../1_Input/beta_all.csv", row.names = 1)

#combine data
Combined_DMPs<-inner_join(iPSC_stats, heart_stats)
Combined_DMPs_Separated<-Combined_DMPs %>% mutate(UCSC_RefGene_Name = strsplit(as.character(UCSC_RefGene_Name), ";")) %>% unnest(UCSC_RefGene_Name) %>% distinct() %>% merge(., beta_all, by.x = "Name", by.y = 0)

# Select only co-methylated positions (iPSC and heart)
Combined_together<-Combined_DMPs_Separated %>% filter(Methylation_iPSC>0 & Methylation_heart>0 | Methylation_iPSC<0 & Methylation_heart<0) %>% arrange(desc(Methylation_iPSC)) %>% filter(pval_iPSC < STAT) %>% arrange(pval_iPSC)
#Find row number for top candidates
Candidates<-which(Combined_together$UCSC_RefGene_Name == "ATG7")
# Combined_together<-merge(Combined_together, beta_all, by.x = "Name", by.y = 0)
rownames(Combined_together)<-make.unique(Combined_together$UCSC_RefGene_Name, sep = ".")
#Create heatmap matrices and annotation
heart_hm<-Combined_together %>% dplyr::select(contains("NF") | contains("biopsy")) %>% data.matrix()
heart_index<-AnnoTargets %>% dplyr::filter(Tissue=="heart") %>% dplyr::select(Tissue, Sample_Group, Sample_ID)
iPSC_hm<-Combined_together %>% dplyr::select(contains("cells") & contains("co"),contains("cells") & contains("go"), contains("cells") & contains("bo")) %>% data.matrix()
iPSC_index<-AnnoTargets %>% dplyr::filter(Tissue=="iPSC") %>% dplyr::select(Tissue, Sample_Group, Sample_ID)
write.xlsx(Combined_together, "../2_Output/Overlapping.DMPs_iPSC.Biopsy.xlsx", overwrite = T)

# Venn Diagram
library(VennDiagram)
futile.logger::flog.threshold(futile.logger::ERROR, name = "VennDiagramLogger")
## NULL
x<-list(iPSC = DMPs_iPSC$Name, heart = DMPs_heart$Name)
venn.diagram(x,fill = c("darkgray", "firebrick2"), alpha = c(0.75, 0.75), lty = 'blank', filename = "../2_Output/Overlapping_DMPs_iPSC.Biopsy.png", na = "remove")
## [1] 1
# Heatmap

ann_colors = list(Sample_Group = c(DCM="#1b9e77", CON = "goldenrod2"), Tissue = c(heart = "coral2", iPSC = "darkgray"), Sample_ID = c(A = "white", B = "orange", C = "black", F = "red", G = "green", T = "blue", V = "purple"))
paletteLength <- 100
myColor <- colorRampPalette(c("dodgerblue4", "white", "gold2"))(paletteLength)

#iPSC
heatmap_iPSC<-ComplexHeatmap::pheatmap(iPSC_hm, scale="row",
                    cluster_cols = F,
                    cluster_rows = T,
                     # angle_col = 45,
                     fontsize_col = 8,
                     color = myColor,
                    annotation_names_col = FALSE,
                     show_rownames = F,
                     border_color = NA,
                    annotation_colors = ann_colors,
                    annotation_col = iPSC_index)
heatmap_iPSC


ha = rowAnnotation(foo = anno_mark(at = c(1:10), labels = Combined_together$UCSC_RefGene_Name[1:10]))

#heart
heatmap_heart<-ComplexHeatmap::pheatmap(heart_hm, scale = "row",
                    cluster_cols = F,
                    cluster_rows = T,
                     # angle_col = 45,
                     fontsize_col = 8,
                     color = myColor,
                     show_rownames = F,
                     border_color = NA,
                    annotation_names_col = FALSE,
                    right_annotation = ha,
                    annotation_colors = ann_colors,
                    annotation_col = heart_index)

heatmap_heart
heatmap_iPSC + heatmap_heart

pdf(file="../2_Output/complexheatmap_combined.pdf", height = 5, width = 7, onefile = F)
heatmap_iPSC + heatmap_heart
dev.off()
## quartz_off_screen 
##                 2

Validation

#Import Pepin et al. 2019 Combined methylation-RNA-sequencing dataset
Pepin_DMPs<-read.xlsx("../1_Input/Validation/HF.Effect_NR_Pre.v.CON_Annotated_DiffMeth.xlsx")

Motif Enrichment

###### Working example (from BED file)
library(monaLisa)
library(GenomicRanges)
library(SummarizedExperiment)
library(openxlsx)
library(dplyr)
library(TFBSTools)
library(JASPAR2020)
library(BSgenome.Hsapiens.UCSC.hg19)
mcparams <- BiocParallel::MulticoreParam(10L) #parallelization (10-core)
DMPs_iPSC<-read.xlsx("../3_Results/Overlapping.DMPs_iPSC.Biopsy.xlsx", startRow = 2)
bed<-DMPs_iPSC %>% dplyr::select(chr, pos, Methylation_iPSC) %>% transmute(seqnames=chr, start=pos-20, end=pos+20, width = 41, strand = "*", deltaMeth=Methylation_iPSC)
bed_mr<-as(bed, "GRanges")
# define bins by differential methylation
bins <- bin(x = bed_mr$deltaMeth, binmode = "equalN", nElement = 100, minAbsX = 1)
table(bins)
## bins
## [-6.06,-1.5]  (-1.5,1.02]  (1.02,7.75] 
##          100          226          100
pdf(paste0("../2_Output/_bins.pdf"), width = 7.5, height = 5)
plotBinDensity(bed_mr$deltaMeth, bins, legend = "topright")
dev.off()
## quartz_off_screen 
##                 2
# get PWMs from JASPAR
pwms <- getMatrixSet(JASPAR2020,
                     opts = list(matrixtype = "PWM",
                                 tax_group = "vertebrates"))
# trim bed file for sequenes that are consistent
lmrsel <- trim(resize(bed_mr, width = median(width(bed_mr)), fix = "center"))
# get sequences from mouse genome
lmrseqs <- getSeq(BSgenome.Hsapiens.UCSC.hg19, bed_mr)
# GC proportion (bias)
plotBinDiagnostics(seqs = lmrseqs, bins = bins, aspect = "GCfrac")

plotBinDiagnostics(seqs = lmrseqs, bins = bins, aspect = "dinucfreq")
# run motif enrichment
se <- calcBinnedMotifEnrR(seqs = lmrseqs, bins = bins, pwmL = pwms, BPPARAM = BiocParallel::MulticoreParam(8))
# Filter results
Test<-as.data.frame(assays(se))
sel <- apply(assay(se, "negLog10P"), 1, 
             function(x) max(abs(x), 0, na.rm = TRUE)) > 1.0
sum(sel)
## [1] 65
#> [1] 59
seSel <- se[sel, ]

# plot
pdf("../2_Output/Motifs.pdf", width = 11, height = 10)
plotMotifHeatmaps(x = seSel, which.plots = c("log2enr", "negLog10P"), 
                  width = 2.0, cluster = TRUE, maxEnr = 2, maxSig = 10, 
                  show_motif_GC = TRUE, show_dendrogram = T,show_seqlogo = TRUE)
dev.off()
## quartz_off_screen 
##                 2
plotMotifHeatmaps(x = seSel, which.plots = c("log2enr", "negLog10P"), 
                  width = 2.0, cluster = TRUE, maxEnr = 2, maxSig = 10, 
                  show_motif_GC = TRUE, show_dendrogram = T,show_seqlogo = TRUE)

Supplemental Table: R Session Information

All packages and setting are acquired using the following command:

options(kableExtra.latex.load_packages = FALSE)
Run_tE<-Sys.time()
Run_time<-Run_tE - Run_tS
Run_time
## Time difference of 14.97141 mins
library(kableExtra)
sinfo<-devtools::session_info()
sinfo$platform
##  setting  value
##  version  R version 4.1.2 (2021-11-01)
##  os       macOS Big Sur 10.16
##  system   x86_64, darwin17.0
##  ui       X11
##  language (EN)
##  collate  en_US.UTF-8
##  ctype    en_US.UTF-8
##  tz       Europe/Berlin
##  date     2021-12-31
##  pandoc   2.14.0.3 @ /Applications/RStudio.app/Contents/MacOS/pandoc/ (via rmarkdown)
sinfo$packages %>% kable( 
                         align="c", 
                         caption="Packages and Required Dependencies") %>% 
    kable_styling(latex_options=c("repeat_header", "condensed"))
Packages and Required Dependencies
package ondiskversion loadedversion path loadedpath attached is_base date source md5ok library
abind abind 1.4.5 1.4-5 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/abind /Library/Frameworks/R.framework/Versions/4.1/Resources/library/abind FALSE FALSE 2016-07-21 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
annotate annotate 1.72.0 1.72.0 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/annotate /Library/Frameworks/R.framework/Versions/4.1/Resources/library/annotate FALSE FALSE 2021-10-26 Bioconductor /Library/Frameworks/R.framework/Versions/4.1/Resources/library
AnnotationDbi AnnotationDbi 1.56.2 1.56.2 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/AnnotationDbi /Library/Frameworks/R.framework/Versions/4.1/Resources/library/AnnotationDbi TRUE FALSE 2021-11-09 Bioconductor /Library/Frameworks/R.framework/Versions/4.1/Resources/library
askpass askpass 1.1 1.1 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/askpass /Library/Frameworks/R.framework/Versions/4.1/Resources/library/askpass FALSE FALSE 2019-01-13 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
assertthat assertthat 0.2.1 0.2.1 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/assertthat /Library/Frameworks/R.framework/Versions/4.1/Resources/library/assertthat FALSE FALSE 2019-03-21 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
backports backports 1.4.0 1.4.0 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/backports /Library/Frameworks/R.framework/Versions/4.1/Resources/library/backports FALSE FALSE 2021-11-23 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
base64 base64 2.0 2.0 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/base64 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/base64 FALSE FALSE 2016-05-10 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
base64enc base64enc 0.1.3 0.1-3 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/base64enc /Library/Frameworks/R.framework/Versions/4.1/Resources/library/base64enc FALSE FALSE 2015-07-28 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
beanplot beanplot 1.2 1.2 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/beanplot /Library/Frameworks/R.framework/Versions/4.1/Resources/library/beanplot FALSE FALSE 2014-09-19 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
Biobase Biobase 2.54.0 2.54.0 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/Biobase /Library/Frameworks/R.framework/Versions/4.1/Resources/library/Biobase TRUE FALSE 2021-10-26 Bioconductor /Library/Frameworks/R.framework/Versions/4.1/Resources/library
BiocFileCache BiocFileCache 2.2.0 2.2.0 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/BiocFileCache /Library/Frameworks/R.framework/Versions/4.1/Resources/library/BiocFileCache FALSE FALSE 2021-10-26 Bioconductor /Library/Frameworks/R.framework/Versions/4.1/Resources/library
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Rcpp Rcpp 1.0.7 1.0.7 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/Rcpp /Library/Frameworks/R.framework/Versions/4.1/Resources/library/Rcpp FALSE FALSE 2021-07-07 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
RCurl RCurl 1.98.1.5 1.98-1.5 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/RCurl /Library/Frameworks/R.framework/Versions/4.1/Resources/library/RCurl FALSE FALSE 2021-09-17 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
readr readr 2.1.1 2.1.1 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/readr /Library/Frameworks/R.framework/Versions/4.1/Resources/library/readr FALSE FALSE 2021-11-30 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
readxl readxl 1.3.1 1.3.1 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/readxl /Library/Frameworks/R.framework/Versions/4.1/Resources/library/readxl TRUE FALSE 2019-03-13 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
remotes remotes 2.4.2 2.4.2 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/remotes /Library/Frameworks/R.framework/Versions/4.1/Resources/library/remotes FALSE FALSE 2021-11-30 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
reshape reshape 0.8.8 0.8.8 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/reshape /Library/Frameworks/R.framework/Versions/4.1/Resources/library/reshape FALSE FALSE 2018-10-23 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
reshape2 reshape2 1.4.4 1.4.4 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/reshape2 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/reshape2 TRUE FALSE 2020-04-09 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
restfulr restfulr 0.0.13 0.0.13 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/restfulr /Library/Frameworks/R.framework/Versions/4.1/Resources/library/restfulr FALSE FALSE 2017-08-06 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
rhdf5 rhdf5 2.38.0 2.38.0 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/rhdf5 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/rhdf5 FALSE FALSE 2021-10-26 Bioconductor /Library/Frameworks/R.framework/Versions/4.1/Resources/library
rhdf5filters rhdf5filters 1.6.0 1.6.0 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/rhdf5filters /Library/Frameworks/R.framework/Versions/4.1/Resources/library/rhdf5filters FALSE FALSE 2021-10-26 Bioconductor /Library/Frameworks/R.framework/Versions/4.1/Resources/library
Rhdf5lib Rhdf5lib 1.16.0 1.16.0 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/Rhdf5lib /Library/Frameworks/R.framework/Versions/4.1/Resources/library/Rhdf5lib FALSE FALSE 2021-10-26 Bioconductor /Library/Frameworks/R.framework/Versions/4.1/Resources/library
rjson rjson 0.2.20 0.2.20 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/rjson /Library/Frameworks/R.framework/Versions/4.1/Resources/library/rjson FALSE FALSE 2018-06-08 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
rlang rlang 0.4.12 0.4.12 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/rlang /Library/Frameworks/R.framework/Versions/4.1/Resources/library/rlang FALSE FALSE 2021-10-18 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
rmarkdown rmarkdown 2.11 2.11 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/rmarkdown /Library/Frameworks/R.framework/Versions/4.1/Resources/library/rmarkdown FALSE FALSE 2021-09-14 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
rngtools rngtools 1.5.2 1.5.2 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/rngtools /Library/Frameworks/R.framework/Versions/4.1/Resources/library/rngtools FALSE FALSE 2021-09-20 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
rpart rpart 4.1.15 4.1-15 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/rpart /Library/Frameworks/R.framework/Versions/4.1/Resources/library/rpart FALSE FALSE 2019-04-12 CRAN (R 4.1.2) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
rprojroot rprojroot 2.0.2 2.0.2 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/rprojroot /Library/Frameworks/R.framework/Versions/4.1/Resources/library/rprojroot FALSE FALSE 2020-11-15 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
Rsamtools Rsamtools 2.10.0 2.10.0 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/Rsamtools /Library/Frameworks/R.framework/Versions/4.1/Resources/library/Rsamtools FALSE FALSE 2021-10-26 Bioconductor /Library/Frameworks/R.framework/Versions/4.1/Resources/library
RSQLite RSQLite 2.2.8 2.2.8 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/RSQLite /Library/Frameworks/R.framework/Versions/4.1/Resources/library/RSQLite FALSE FALSE 2021-08-21 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
rstatix rstatix 0.7.0 0.7.0 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/rstatix /Library/Frameworks/R.framework/Versions/4.1/Resources/library/rstatix FALSE FALSE 2021-02-13 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
rstudioapi rstudioapi 0.13 0.13 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/rstudioapi /Library/Frameworks/R.framework/Versions/4.1/Resources/library/rstudioapi FALSE FALSE 2020-11-12 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
rtracklayer rtracklayer 1.54.0 1.54.0 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/rtracklayer /Library/Frameworks/R.framework/Versions/4.1/Resources/library/rtracklayer TRUE FALSE 2021-10-26 Bioconductor /Library/Frameworks/R.framework/Versions/4.1/Resources/library
rvest rvest 1.0.2 1.0.2 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/rvest /Library/Frameworks/R.framework/Versions/4.1/Resources/library/rvest FALSE FALSE 2021-10-16 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
S4Vectors S4Vectors 0.32.3 0.32.3 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/S4Vectors /Library/Frameworks/R.framework/Versions/4.1/Resources/library/S4Vectors TRUE FALSE 2021-11-21 Bioconductor /Library/Frameworks/R.framework/Versions/4.1/Resources/library
sass sass 0.4.0 0.4.0 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/sass /Library/Frameworks/R.framework/Versions/4.1/Resources/library/sass FALSE FALSE 2021-05-12 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
scales scales 1.1.1 1.1.1 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/scales /Library/Frameworks/R.framework/Versions/4.1/Resources/library/scales FALSE FALSE 2020-05-11 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
scrime scrime 1.3.5 1.3.5 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/scrime /Library/Frameworks/R.framework/Versions/4.1/Resources/library/scrime FALSE FALSE 2018-12-01 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
seqLogo seqLogo 1.60.0 1.60.0 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/seqLogo /Library/Frameworks/R.framework/Versions/4.1/Resources/library/seqLogo FALSE FALSE 2021-10-26 Bioconductor /Library/Frameworks/R.framework/Versions/4.1/Resources/library
sessioninfo sessioninfo 1.2.1 1.2.1 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/sessioninfo /Library/Frameworks/R.framework/Versions/4.1/Resources/library/sessioninfo FALSE FALSE 2021-11-02 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
shape shape 1.4.6 1.4.6 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/shape /Library/Frameworks/R.framework/Versions/4.1/Resources/library/shape FALSE FALSE 2021-05-19 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
shiny shiny 1.7.1 1.7.1 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/shiny /Library/Frameworks/R.framework/Versions/4.1/Resources/library/shiny TRUE FALSE 2021-10-02 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
shinyMethyl shinyMethyl 1.30.0 1.30.0 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/shinyMethyl /Library/Frameworks/R.framework/Versions/4.1/Resources/library/shinyMethyl TRUE FALSE 2021-10-26 Bioconductor /Library/Frameworks/R.framework/Versions/4.1/Resources/library
siggenes siggenes 1.68.0 1.68.0 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/siggenes /Library/Frameworks/R.framework/Versions/4.1/Resources/library/siggenes FALSE FALSE 2021-10-26 Bioconductor /Library/Frameworks/R.framework/Versions/4.1/Resources/library
sm sm 2.2.5.7 2.2-5.7 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/sm /Library/Frameworks/R.framework/Versions/4.1/Resources/library/sm FALSE FALSE 2021-09-13 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
sparseMatrixStats sparseMatrixStats 1.6.0 1.6.0 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/sparseMatrixStats /Library/Frameworks/R.framework/Versions/4.1/Resources/library/sparseMatrixStats FALSE FALSE 2021-10-26 Bioconductor /Library/Frameworks/R.framework/Versions/4.1/Resources/library
stabs stabs 0.6.4 0.6-4 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/stabs /Library/Frameworks/R.framework/Versions/4.1/Resources/library/stabs FALSE FALSE 2021-01-29 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
stringi stringi 1.7.6 1.7.6 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/stringi /Library/Frameworks/R.framework/Versions/4.1/Resources/library/stringi FALSE FALSE 2021-11-29 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
stringr stringr 1.4.0 1.4.0 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/stringr /Library/Frameworks/R.framework/Versions/4.1/Resources/library/stringr TRUE FALSE 2019-02-10 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
SummarizedExperiment SummarizedExperiment 1.24.0 1.24.0 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/SummarizedExperiment /Library/Frameworks/R.framework/Versions/4.1/Resources/library/SummarizedExperiment TRUE FALSE 2021-10-26 Bioconductor /Library/Frameworks/R.framework/Versions/4.1/Resources/library
survival survival 3.2.13 3.2-13 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/survival /Library/Frameworks/R.framework/Versions/4.1/Resources/library/survival TRUE FALSE 2021-08-24 CRAN (R 4.1.2) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
svglite svglite 2.0.0 2.0.0 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/svglite /Library/Frameworks/R.framework/Versions/4.1/Resources/library/svglite FALSE FALSE 2021-02-20 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
systemfonts systemfonts 1.0.3 1.0.3 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/systemfonts /Library/Frameworks/R.framework/Versions/4.1/Resources/library/systemfonts FALSE FALSE 2021-10-13 CRAN (R 4.1.2) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
testthat testthat 3.1.1 3.1.1 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/testthat /Library/Frameworks/R.framework/Versions/4.1/Resources/library/testthat FALSE FALSE 2021-12-03 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
TFBSTools TFBSTools 1.32.0 1.32.0 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/TFBSTools /Library/Frameworks/R.framework/Versions/4.1/Resources/library/TFBSTools TRUE FALSE 2021-10-26 Bioconductor /Library/Frameworks/R.framework/Versions/4.1/Resources/library
TFMPvalue TFMPvalue 0.0.8 0.0.8 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/TFMPvalue /Library/Frameworks/R.framework/Versions/4.1/Resources/library/TFMPvalue FALSE FALSE 2018-05-16 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
tibble tibble 3.1.6 3.1.6 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/tibble /Library/Frameworks/R.framework/Versions/4.1/Resources/library/tibble FALSE FALSE 2021-11-07 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
tidyr tidyr 1.1.4 1.1.4 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/tidyr /Library/Frameworks/R.framework/Versions/4.1/Resources/library/tidyr TRUE FALSE 2021-09-27 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
tidyselect tidyselect 1.1.1 1.1.1 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/tidyselect /Library/Frameworks/R.framework/Versions/4.1/Resources/library/tidyselect FALSE FALSE 2021-04-30 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
tzdb tzdb 0.2.0 0.2.0 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/tzdb /Library/Frameworks/R.framework/Versions/4.1/Resources/library/tzdb FALSE FALSE 2021-10-27 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
usethis usethis 2.1.3 2.1.3 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/usethis /Library/Frameworks/R.framework/Versions/4.1/Resources/library/usethis FALSE FALSE 2021-10-27 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
utf8 utf8 1.2.2 1.2.2 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/utf8 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/utf8 FALSE FALSE 2021-07-24 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
vctrs vctrs 0.3.8 0.3.8 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/vctrs /Library/Frameworks/R.framework/Versions/4.1/Resources/library/vctrs FALSE FALSE 2021-04-29 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
VennDiagram VennDiagram 1.7.1 1.7.1 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/VennDiagram /Library/Frameworks/R.framework/Versions/4.1/Resources/library/VennDiagram TRUE FALSE 2021-12-02 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
vioplot vioplot 0.3.7 0.3.7 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/vioplot /Library/Frameworks/R.framework/Versions/4.1/Resources/library/vioplot FALSE FALSE 2021-07-27 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
viridisLite viridisLite 0.4.0 0.4.0 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/viridisLite /Library/Frameworks/R.framework/Versions/4.1/Resources/library/viridisLite FALSE FALSE 2021-04-13 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
webshot webshot 0.5.2 0.5.2 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/webshot /Library/Frameworks/R.framework/Versions/4.1/Resources/library/webshot FALSE FALSE 2019-11-22 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
wesanderson wesanderson 0.3.6 0.3.6 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/wesanderson /Library/Frameworks/R.framework/Versions/4.1/Resources/library/wesanderson TRUE FALSE 2018-04-20 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
withr withr 2.4.3 2.4.3 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/withr /Library/Frameworks/R.framework/Versions/4.1/Resources/library/withr FALSE FALSE 2021-11-30 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
xfun xfun 0.28 0.28 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/xfun /Library/Frameworks/R.framework/Versions/4.1/Resources/library/xfun FALSE FALSE 2021-11-04 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
XML XML 3.99.0.8 3.99-0.8 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/XML /Library/Frameworks/R.framework/Versions/4.1/Resources/library/XML FALSE FALSE 2021-09-17 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
xml2 xml2 1.3.3 1.3.3 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/xml2 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/xml2 FALSE FALSE 2021-11-30 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
xtable xtable 1.8.4 1.8-4 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/xtable /Library/Frameworks/R.framework/Versions/4.1/Resources/library/xtable FALSE FALSE 2019-04-21 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
XVector XVector 0.34.0 0.34.0 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/XVector /Library/Frameworks/R.framework/Versions/4.1/Resources/library/XVector TRUE FALSE 2021-10-26 Bioconductor /Library/Frameworks/R.framework/Versions/4.1/Resources/library
yaml yaml 2.2.1 2.2.1 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/yaml /Library/Frameworks/R.framework/Versions/4.1/Resources/library/yaml FALSE FALSE 2020-02-01 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
zip zip 2.2.0 2.2.0 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/zip /Library/Frameworks/R.framework/Versions/4.1/Resources/library/zip FALSE FALSE 2021-05-31 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
zlibbioc zlibbioc 1.40.0 1.40.0 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/zlibbioc /Library/Frameworks/R.framework/Versions/4.1/Resources/library/zlibbioc FALSE FALSE 2021-10-26 Bioconductor /Library/Frameworks/R.framework/Versions/4.1/Resources/library
zoo zoo 1.8.9 1.8-9 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/zoo /Library/Frameworks/R.framework/Versions/4.1/Resources/library/zoo FALSE FALSE 2021-03-09 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library

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