Supplemental Methods - Epigenenomics of Race in Heart Failure

Code Authors: Mark E. Pepin Contact: pepinme@gmail.com 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]
DIABETES=c("ND", "T2D") #Should define the reference group first
ISCHEMIA=c("NICM", "ICM")
RACE=c("Caucasian_American", "African_American") #, "Asian_American", "Eastern_African"
ISO=c("NONE", "DOB")
STATISTIC = 0.05 #P statistic threshold used in this combination.

VARIABLE = RACE

COMPARISON= paste0(VARIABLE[2], ".vs.", VARIABLE[1])

# Candidate Gene Selection (RNA-sequencing) EDIT THIS LIST BASED ON INTERESTS.
GENES=c("LEP", "ADIPOQ", "PLIN1")
VAR1="Race"
VAR2="Diabetes"
# Single Bar Graph
library(dplyr)
my_comparisons <- list( c("Caucasian_American", "African_American")) # Comparisons to make
# my_comparisons <- list( c("WT", "NR4A1.cKO"), c("ABHD5.cKO","cDKO"), c("WT", "ABHD5.cKO")) # Comparisons to make
## Create color based on Genotype
ann_colors = list(Race = c(Caucasian_American="grey", African_American = "darkcyan", Asian_American="black", Eastern_African = "black"))
RaceColors<-ann_colors$Race
ann_colorTable<-as.data.frame(ann_colors)

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

# Pathway Gene Screening
GENE.Pathway<-read.csv("../2_Input/Gene.Sets/Oxphos.KEGG.csv", header = F) #Can alter the gene pathway (just change path)
colnames(GENE.Pathway)<-"external_gene_name"

# Create Output Folder Structure
ifelse(!dir.exists(file.path(paste0("../3_Output/"))), dir.create(file.path(paste0("../3_Output/"))), FALSE)

## [1] FALSE

ifelse(!dir.exists(file.path(paste0("../3_Output/1_RNA/"))), dir.create(file.path(paste0("../3_Output/1_RNA/"))), FALSE)

## [1] FALSE

ifelse(!dir.exists(file.path(paste0("../3_Output/2_Methyl/"))), dir.create(file.path(paste0("../3_Output/2_Methyl/"))), FALSE)

## [1] FALSE

ifelse(!dir.exists(file.path(paste0("../3_Output/3_Combined/"))), dir.create(file.path(paste0("../3_Output/3_Combined/"))), 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)

Figure 1: Pilot Analysis - Illumina(R) HumanMethylation 450k Array GSE109097

The current study originated from the preliminary analysis of a cohort (n = 11) of patients with ischemic and non-ischemic end-stage HF (Figure 1A). Although we have previously reported that a patient’s history of ischemic heart diseases is most responsible for global differences in cardiac DNA methylation within this cohort (Pepin et al. 2018), it was noted here that, although the gobal signature encoded ischemic status, the most variable CpG probes on the array exhibited a divergent bimodal distribution. Dataset is available at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE109097

#   Differential expression analysis with limma
library(openxlsx)
library(magrittr)
library(dplyr)
library(ggpubr)
library(matrixStats)
library("ggrepel")
library(ggplot2)
library(cowplot) 
options(ggrepel.max.overlaps = Inf)
m450k<-read.xlsx("../2_Input/_Pilot/Pepin.Wende_2018.xlsx", sheet = "betaData", rowNames = T)
colData_m450k<-read.xlsx("../2_Input/_Pilot/Pepin.Wende_2018.xlsx", sheet = "colData")
# MDS in ggplot2
Ntop = 1000
RowVar<-rowVars(data.matrix(m450k)) #calculate variances for each row (vector)
MDS.set<-as.data.frame(cbind(m450k, RowVar))
MDS_matrix<-MDS.set %>% arrange(desc(RowVar)) %>% top_n(Ntop,RowVar) #Select top N rows by variance
# Compute MDS
mds <- MDS_matrix %>% dplyr::select(-RowVar) %>% t(.) %>%
  dist() %>%          
  cmdscale() %>%
  as_tibble()
colnames(mds) <- c("Dim.1", "Dim.2")
rownames(mds)<-colnames(m450k)
mds$Sample_Name<-rownames(mds)
mds<-dplyr::inner_join(mds, colData_m450k)

#K-means clustering
clust <- kmeans(mds[,1:2], 2)$cluster %>%
  as.factor()
mds <- mds %>%
  mutate(kmeans.2 = clust)
# Main plot
COLOR=c("#1b9e77", "#7570b3")
pmain <- ggplot(mds, aes(x = Dim.1, y = Dim.2, color = Race))+
  scale_color_manual(values = COLOR) +
  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),
      legend.position="bottom",
      legend.key=element_blank(),
      axis.text = element_text(size = 12),
      axis.title = element_text(size = 14, face="bold")) +
  geom_hline(yintercept = 0, size = 1) + 
  geom_vline(xintercept=0, size=1) +
  geom_point()+
  stat_ellipse()+
  geom_text_repel(data=mds, aes(label=Sample_Name), show_guide  = F) +
  labs(x="Principal Component 1", 
       y="Principal Component 2")
# Marginal densities along x axis
xdens <- axis_canvas(pmain, axis = "x")+
  geom_density(data = mds, aes(x = Dim.1, fill = Race),
              alpha = 0.7, size = 0.2)+
  scale_fill_manual(values = COLOR)
# Marginal densities along y axis
ydens <- axis_canvas(pmain, axis = "y", coord_flip = TRUE)+ #must set coord_flip = true if using coord_flip() below
  geom_density(data = mds, aes(x = Dim.2, fill = Race),
                alpha = 0.7, size = 0.2)+
  coord_flip()+
  scale_fill_manual(values = COLOR)
p1 <- insert_xaxis_grob(pmain, xdens, grid::unit(.2, "null"), position = "top")
p2<- insert_yaxis_grob(p1, ydens, grid::unit(.2, "null"), position = "right")
pdf(file="../3_Output/2_Methyl/Pilot.Scatterhist.pdf", height = 7, width = 7, onefile = F)
ggdraw(p2)
dev.off()

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ggdraw(p2)

Figure 2: Patient Information

Figure 2A: Correlation and Covariate Network Analysis

The first task in this project was to understand the covariates that accompany racial differences in the current cohort. Although both small sample size and limited medical documentation preclude generalizing conclusions, our initial observations. In accordance with the IRB exemption, we obtained health history and available demographics from the electronic medical record. Correlation-based analysis was then performed using these metrics, visualized as a heatmap (Figure 2A).

Run_tS<-Sys.time()
library(openxlsx)
library(dplyr)
library(Hmisc)
library(corrplot)
library(RColorBrewer)
options(kableExtra.latex.load_packages = FALSE)
library(kableExtra)
Patient_Data <- openxlsx::read.xlsx("../2_Input/1_Patient/colData_CHAAMPS.FINAL.xlsx", sheet = "Matrix", rowNames = T)
Patient_Data<-as.data.frame(Patient_Data)
# Patient_Data<-dplyr::select(Patient_Data, -Syst.PA, -PCWP,-RA)
Patient_Data<-Patient_Data[complete.cases(Patient_Data),]

# # Print the table for use
# Patient_Data %>% kable(#format="latex", 
#                        align="c", 
#                        booktabs=T, 
#                        caption="Patient Characteristics") %>% 
#   kable_styling(latex_options=c("striped", 
#                                 "condensed", 
#                                 "repeat_header"))

# Format for Correlation
Patient_corr<-data.matrix(Patient_Data)
cor.r<-cor(Patient_corr)
paletteLength <- 100
myColor <- colorRampPalette(c("dodgerblue4", "white", "brown4"))(paletteLength)
p.mat<-cor.mtest(cor.r)$p
rownames(p.mat)<-rownames(cor.r)
colnames(p.mat)<-colnames(cor.r)
corrplot(cor.r, 
         order="original",
         type="full",
         method = "square",
         outline=FALSE,
         col = myColor,
         tl.cex=0.7,
         tl.col="black",
         p.mat=p.mat,
         sig.level = 0.05,
         insig="blank",
         addgrid.col = NA)

Correlation Matrix of Patient Characteristics. All data were obtained from electronic medical records and de-identified in accordance with the UAB Institutional Review Board requirements.

pdf(file=paste0("../3_Output/_Patient/Patient.Correlation.pdf"))
corrplot(cor.r, 
         order="original",
         type="full",
         method = "square",
         outline=FALSE,
         col = myColor,
         tl.cex=0.7,
         tl.col="black",
         p.mat=p.mat,
         sig.level = 0.05,
         insig="blank",
         addgrid.col = NA)
dev.off()

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Figure 2B-H: Patient Variables - Bar Graphs

To examine the changes of individual variables, we used bar graphs according to patients’ self-identified race, as previously reported (n.d.). A pairwise comparison of numeric factors was generated to identify any significant differences by categorical patient race (Figure 2B-H). Although a few characteristics appeared to correlate with race, no significant differences were present on pairwise analysis following adjustment for multiple comparisons.

#####################################################
# Bar Graphs of the PHI
######################################################
#Location where graphs will be saved
library(dplyr)
library(ggplot2)
library(ggpubr)
library(gridExtra)
VAR2="Race"
PARAMS=c("Age", "Creatinine", "Blood.Glucose", "LV.EF")

ifelse(!dir.exists(file.path(paste0("../3_Output/_Patient/Candidates/"))), dir.create(file.path(paste0("../3_Output/_Patient/Candidates/"))), FALSE)

## [1] FALSE

# Patient_Data<-openxlsx::read.xlsx("../2_Input/_Patient/Patients_Health.Info.xlsx", sheet = "Matrix_Pre.v.CON", rowNames = T)
# Patient_Data$Sample_ID<-rownames(Patient_Data)
# Patient_Data<-dplyr::select(Patient_Data, -Sex)
# Patient_ALL<-reshape(Patient_Data, varying = 14:29, sep = "_", direction = "long")
# Patient_ALL<-dplyr::rename(Patient_ALL, Timing=time)

colData_all<-openxlsx::read.xlsx("../2_Input/1_Patient/colData_CHAAMPS.FINAL.xlsx", sheet = "Summary", rowNames = T, startRow = 2)
graph_info<-subset(colData_all, Race %in% RACE)

## create a function that converts factors to numeric
asNumeric=function(x){as.numeric(as.character(x))}
factorsNumeric=function(d){modifyList(d, lapply(d[, sapply(d, is.factor)], asNumeric))}

##
graph_info<-factorsNumeric(graph_info)
# graph_info$Response<-factor(graph_info$Response, levels = c("CON", "NR", "R"))
graph_info$Ischemia<-factor(graph_info$Ischemia, levels = c("NICM", "ICM"))
graph_info$Diabetes<-factor(graph_info$Diabetes, levels = c("ND", "T2D"))
graph_info$HTN<-factor(graph_info$HTN, levels = c("N", "Y"))

groupsize<-graph_info %>% group_by(Race) %>% tally()

#Define function for mean and standard deviation for each group
data_summary <- function(data, varname, groupnames){
  require(plyr)
  summary_func <- function(x, col){
    c(mean = mean(x[[col]], na.rm=TRUE),
      sd = sd(x[[col]], na.rm=TRUE))
  }
  data_sum<-ddply(data, groupnames, .fun=summary_func,
                  varname)
  data_sum <- rename(data_sum, c("mean" = varname))
 return(data_sum)
}
########################################################
plotlist = list()
for (i in seq_along(PARAMS)){
PARAM<-as.name(PARAMS[i]) #define a "name" for the variable
ds <- data_summary(graph_info, varname=PARAM, groupnames=c(VAR2))
ds<-dplyr::left_join(ds, groupsize)
ds<-dplyr::mutate(ds, upper=ds[,2]+(sd/sqrt(n-1)))
patient<-ggplot(ds, aes_string(x=VAR2, y=PARAMS[i], fill=VAR2)) + 
  geom_bar(stat="identity", color="black", 
           position=position_dodge()) +
  geom_errorbar(aes_string(ymin=PARAMS[i], ymax="upper"), width=.2,
                 position=position_dodge(.9))
patient_plot<-patient+labs(title=paste0("Patient Data - ", PARAMS[i]), x=VAR2, fill=VAR2)+
    theme_classic2(base_size = 10) +
    scale_fill_manual(values=c('black', "white", "#999999")) + 
    stat_compare_means(mapping = aes_string(x=VAR2, y=PARAMS[i]), data = graph_info,label = "p.signif")
pdf(file=paste0("../3_Output/_Patient/Candidates/", PARAMS[i], "_", VAR2, "_barplot.pdf"), width = 3.5, height = 2.5)
print(patient_plot)
dev.off()
plotlist[[i]] = patient_plot
}
t<-marrangeGrob(grobs = plotlist, legend, nrow=3, ncol=2)
ggsave(paste0("../3_Output/_Patient/Candidates/_barplots_", VAR2, ".pdf"), t, width = 6, height = 7)

##########################################################
#Boxplot
##########################################################

plotlist = list()
for (i in seq_along(PARAMS)){
PARAM<-as.name(PARAMS[i]) #define a "name" for the variable
ds <- data_summary(graph_info, varname=PARAM, groupnames=c(VAR2))
ds<-dplyr::left_join(ds, groupsize)
ds<-dplyr::mutate(ds, upper=ds[,2]+(sd/sqrt(n-1)))
g<-ggplot(graph_info, aes_string(x=VAR2, y=PARAMS[i], fill=VAR2)) + 
  geom_boxplot() + stat_compare_means(label = "p.signif")
g_plot<-g+labs(title=paste0("Patient Data - ", PARAMS[i]), x=VAR2)+
   theme_classic2(base_size = 10) +
   scale_fill_manual(values=c('black', "white", "#999999")) + ylim(NA, max(ds$upper+.25*(ds$upper)))
pdf(file=paste0("../3_Output/_Patient/Candidates/", PARAMS[i], "_", VAR2, "_boxplot.pdf"), width = 3.5, height = 2.5)
print(g_plot)
dev.off()
plotlist[[i]] = g_plot
}
t<-marrangeGrob(grobs = plotlist, legend, nrow=3, ncol=2)
ggsave(paste0("../3_Output/_Patient/Candidates/_boxplots_", VAR2, ".pdf"), t, width = 6, height = 7)

Figure 3: Survival Analysis

To determine whether our cohort reflected the known racial differences in outcomes following LVAD-induced cardiac unloading, we were approved an IRB amendment to obtain all-cause mortality in the 2 years following LVAD implantation. Kaplan-Meier survival curve was generated with a Log-Rank (Mantel-Cox) statistic comparing 2-year survival of AA and CA patients. Subsequent Cox proportional hazards analysis was performed to identify possible covariates that could also account for differences in health outcomes. From this, it was noted that obesity was negatively associated with mortality risk, consistent with the reported “paradox” of heart failure among obese subjects.

library(survminer)
library(survminer)
library(RTCGA.clinical)
Outcomes<-read.xlsx("../2_Input/1_Patient/Outcomes.xlsx")
fit<-survfit(Surv(Censor_2yr, Outcome_2yr) ~ Race, data = Outcomes)
# ggsurvplot(fit, data = Outcomes, risk.table = TRUE)

###################################################
ggsurvplot(
   fit,                     # survfit object with calculated statistics.
   data = Outcomes,  # data used to fit survival curves. 
   risk.table = F,       # show risk table.
   pval = TRUE,             # show p-value of log-rank test.
   conf.int = TRUE,         # show confidence intervals for 
                            # point estimaes of survival curves.
   xlim = c(0,730),        # present narrower X axis, but not affect
                            # survival estimates.
   break.time.by = 100,     # break X axis in time intervals by 500.
   ggtheme = theme_minimal(), # customize plot and risk table with a theme.
 risk.table.y.text.col = T, # colour risk table text annotations.
  risk.table.y.text = FALSE # show bars instead of names in text annotations
                            # in legend of risk table
)

# Fit a Cox proportional hazards model
surv_object<-Surv(time = Outcomes$Censor_2yr, event = Outcomes$Outcome_2yr)
fit.coxph <- coxph(surv_object ~ Diabetes + Ischemia + Obese + HTN + B.Blocker + ACE + Family.Hx, 
                   data = Outcomes)
ggforest(fit.coxph, data = Outcomes)

pdf(file="../3_Output/_Patient/Cox.Survival.pdf", height = 5, width = 5, onefile = F)
ggforest(fit.coxph, data = Outcomes)
dev.off()

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Genome-wide DNA methylation - Illumina(R) HumanMethylation EPIC Array (GSE164197)

To replicate our pilot analysis of DNA methylation in the testing cohort, cardiac DNA methylation analysis was again performed using these 31 cardiac ventricular biopsies (GSE164197). This platform permits the analysis of many additional CpG sites, as follows:

Supplemental Figure S1 - Illumina HumanMethylation EPIC Array CpG Distribution.

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## [1] "../2_Input/3_Methyl/Wende_CHAAMP_MethylationEPIC_Sample_Sheet.csv"

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## class: RGChannelSet 
## dim: 1051815 32 
## metadata(0):
## assays(2): Green Red
## rownames(1051815): 1600101 1600111 ... 99810990 99810992
## rowData names(0):
## colnames(32): LVAD001 LVAD084 ... LVAD083 LVAD092
## colData names(17): Sample_Name Sample_Well ... Basename filenames
## Annotation
##   array: IlluminaHumanMethylationEPIC
##   annotation: ilm10b5.hg38

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Supplemental Figure S2: Outlier (LVAD054)

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

Supplemental Figure S2: LVAD054 Outlier Identification

Supplemental Figure S4, Figure 4B: SNPs in Methylation Data

SNPs have been shown to confound the interpretation of methylation arrays when mutations exist within the CpG sites. To address this concern, a package called “MethylToSNP” exists to identify novel SNPs based on methylation array data, which has been proposed to reduce the number of CpGs that are filtered. Using this approach, 1,294 CpGs were identified as putative SNPs which are likely influenced by minor allele fractions (MAFs); among these, 1,076 (83%) have been idenbtified as SNPs via published genomic sequencing analyses.

To determine whether the SNP frequency in our dataset contributes to racial differences, the beta values within these SNPs were clustered via hierarchical clustering, resulting in a complete separation according to patient race (see figure).

#Remove SNPs
##Option 1: Built-in function that removes all known SNPs
gRatioSet_noSNPs<-dropLociWithSnps(gRatioSet.quantile, snps = c("SBE", "CpG"), maf = 0) #Doing this removes ~40% of all SNPs in the EPIC array
##Option 2: Identify putative SNPs using methylation barcoding (i.e. "gap hunting")
library("MethylToSNP")
library("RColorBrewer")
library("pheatmap")
Mvalues<-as.data.frame(getM(MSet))
x <- MethylToSNP(MSet, SNP=SNPs.147CommonSingle, verbose=TRUE)
x$CpG_ID<-rownames(x)
###
pdf(file="../3_Output/2_Methyl/Putative.SNPs.pdf", width = 10, height = 5) #Print Putative SNP Methylation
plotPotentialSNPs(x, MSet)
dev.off()

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plotPotentialSNPs(x, MSet)

Supplemental Figure S4: Putative SNP Identification from CpG Probe Intensity

SNPs<-merge(x, as.data.frame(getM(MSet)), by = "row.names")
write.csv(SNPs, "SNPs.csv")
SNPs_matrix<-SNPs %>% set_rownames(.$Row.names) %>% dplyr::select(contains("LVAD")) %>% data.matrix()
SNPs_matrix<-SNPs_matrix[,colnames(SNPs_matrix)!="LVAD054"]
SNPs_matrix<-SNPs_matrix[!is.infinite(rowSums(SNPs_matrix)),] #One infinite value exists!! (took ~2-3 days to troubleshoot)

# Index
Index<-openxlsx::read.xlsx("../2_Input/1_Patient/colData_CHAAMPS.FINAL.xlsx", sheet = "Summary", startRow = 2, rowNames = TRUE)
Index_SNPs<-Index[colnames(SNPs_matrix),] %>% dplyr::select(Race)
ann_colors = list(Race = c(African_American="#1b9e77", Asian_American = "#d95f02", Caucasian_American="#7570b3", Eastern_African = "#e7298a"))
paletteLength <- 100
myColor <- colorRampPalette(c("dodgerblue4", "white", "gold2"))(paletteLength)
ann_colors = list(Race = c(African_American="#1b9e77", 
                           Asian_American = "#d95f02", 
                           Caucasian_American="#7570b3", 
                           Eastern_African = "#e7298a"))
heatmap_SNP<-pheatmap(SNPs_matrix, scale="row", #Heatmap of SNPs
                      cluster_cols = TRUE, 
                      cluster_rows = TRUE,
                     cutree_cols = 3,
                     cutree_rows = 4,
                     angle_col = 45,
                     fontsize_col = 8,
                     color = myColor, 
                     show_rownames = FALSE, 
                     border_color = NA, 
                     annotation_colors = ann_colors,
                     annotation_col = Index_SNPs,
                    filename = paste0("../3_Output/2_Methyl/SNPS.heatmap.pdf"))
pheatmap(SNPs_matrix, scale="row", #Heatmap of SNPs
                      cluster_cols = TRUE, 
                      cluster_rows = TRUE,
                     cutree_cols = 3,
                     cutree_rows = 4,
                     angle_col = 45,
                     fontsize_col = 8,
                     color = myColor, 
                     show_rownames = FALSE, 
                     border_color = NA, 
                     annotation_colors = ann_colors,
                     annotation_col = Index_SNPs)
## Cluster Analysis
hc <-heatmap_SNP$tree_row
lbl <- cutree(hc, 4) # split gene dendrogram in 5 groups
cluster1<-which(lbl==1)
cluster2<-which(lbl==2)
cluster3<-which(lbl==3)
cluster4<-which(lbl==4) 
#
Cluster1_data<-SNPs_matrix[cluster1,]
Cluster2_data<-SNPs_matrix[cluster2,]
Cluster3_data<-SNPs_matrix[cluster3,]
Cluster4_data<-SNPs_matrix[cluster4,]
#
heatmap_c1<-pheatmap(Cluster1_data, scale="row", 
                      cluster_cols = TRUE, 
                      cluster_rows = TRUE,
                      #breaks = myBreaks,
                      cutree_cols = 2,
                      angle_col = 45,
                      fontsize_col = 8,
                      color = myColor, 
                      show_rownames = FALSE, 
                      border_color = NA, 
                      annotation_colors = ann_colors,
                     annotation_col = Index_SNPs,
                      filename = paste0("../3_Output/2_Methyl/SNPS_Cluster1.heatmap.pdf"))
#
heatmap_c2<-pheatmap(Cluster2_data, scale="row", 
                      cluster_cols = TRUE, 
                      cluster_rows = TRUE,
                      #breaks = myBreaks,
                      cutree_cols = 2,
                      angle_col = 45,
                      fontsize_col = 8,
                      color = myColor, 
                      show_rownames = FALSE, 
                      border_color = NA, 
                     annotation_colors = ann_colors,
                     annotation_col = Index_SNPs,
                      filename = paste0("../3_Output/2_Methyl/SNPS_Cluster2.heatmap.pdf"))
#
heatmap_c3<-pheatmap(Cluster3_data, scale="row", 
                      cluster_cols = TRUE, 
                      cluster_rows = TRUE,
                      #breaks = myBreaks,
                      cutree_cols = 2,
                      angle_col = 45,
                      fontsize_col = 8,
                      color = myColor, 
                      show_rownames = FALSE, 
                      border_color = NA, 
                     annotation_colors = ann_colors,
                     annotation_col = Index_SNPs,
                      filename = paste0("../3_Output/2_Methyl/SNPS_Cluster3.heatmap.pdf"))
#
heatmap_c4<-pheatmap(Cluster4_data, scale="row", 
                      cluster_cols = TRUE, 
                      cluster_rows = TRUE,
                      #breaks = myBreaks,
                      cutree_cols = 2,
                      angle_col = 45,
                      fontsize_col = 8,
                      color = myColor, 
                      show_rownames = FALSE, 
                      border_color = NA, 
                     annotation_colors = ann_colors,
                     annotation_col = Index_SNPs,
                      filename = paste0("../3_Output/2_Methyl/SNPS_Cluster4.heatmap.pdf"))

Figure 4A: MDS Plot - Unsupervised Clustering by Race

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.

Supplemental Figure S3: MDS Plot

Supplementary Figure S3 - MDS Plots

Differential Methylation of African American and Caucasian_American

Using Minfi to compute differential expression

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

library(minfi)
library(limma)
library(shinyMethyl)
library(dplyr)
library(IlluminaHumanMethylationEPICanno.ilm10b5.hg38)
library(IlluminaHumanMethylationEPICmanifest)
library(RColorBrewer)
#########Part 1: Importing the Data
#Parameters
##Set the experimental conditions
DIABETES=c("ND", "T2D") #Should define the reference group first
ISCHEMIA=c("NICM", "ICM")
RACE=c("Caucasian_American", "African_American")
ISO=c("NONE", "DOB")
STATISTIC = 0.05 #P statistic threshold used in this combination.
VARIABLE = RACE
COMPARISON= paste0(VARIABLE[2], ".vs.", VARIABLE[1])
ifelse(!dir.exists(file.path("../3_Output/2_Methyl/", COMPARISON)), dir.create(file.path("../3_Output/2_Methyl/", COMPARISON)), FALSE)

## [1] FALSE

##Get the array annotation
annoEPIC<-getAnnotation(IlluminaHumanMethylationEPICanno.ilm10b5.hg38)
annoEPIC<-dplyr::select(as.data.frame(annoEPIC), 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="../2_Input/3_Methyl", pattern="csv$")

## [1] "../2_Input/3_Methyl/Wende_CHAAMP_MethylationEPIC_Sample_Sheet.csv"

targets<-dplyr::filter(targets, Sample_Name!="LVAD054") #OUTLIER
# Filter targets
targets_filtered<-subset(targets, Race %in% RACE & Diabetes %in% DIABETES & Ischemia %in% ISCHEMIA)
#Import the array data from input directory (red and green .idat files)
RGSet<-read.metharray.exp(base = "../2_Input/3_Methyl/", targets = targets_filtered, verbose = TRUE)
sampleNames(RGSet)<-targets_filtered$Sample_Name
#Check the phenotype metadata to verify correct parsing
phenoData<-pData(RGSet)
#Get the manifest annotation (EPIC, methyl450k, etc...)
manifest<-getManifest(IlluminaHumanMethylationEPICmanifest)
typeIProbes <- getProbeInfo(manifest, type = "I")$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)
failed<-detP>0.01
#Determine the fraction of "failed" CpG probes (those which failed to identify a methylated CpG)
colMeans(failed)

##      LVAD001      LVAD084      LVAD058      LVAD014      LVAD122      LVAD006 
## 0.0002044213 0.0001894073 0.0001154922 0.0002633223 0.0001928720 0.0003129840 
##      LVAD013      LVAD133      LVAD111      LVAD075      LVAD066      LVAD067 
## 0.0001686187 0.0001743933 0.0003083643 0.0003441669 0.0003014348 0.0001709285 
##      LVAD125      LVAD142      LVAD060      LVAD127      LVAD079      LVAD087 
## 0.0002263648 0.0002621674 0.0003383923 0.0003961384 0.0003638006 0.0002702519 
##      LVAD141      LVAD132      LVAD121      LVAD032      LVAD130      LVAD106 
## 0.0001813228 0.0002956601 0.0002298296 0.0002067311 0.0002055762 0.0001143373 
##      LVAD157      LVAD153      LVAD140      LVAD083      LVAD092 
## 0.0002148156 0.0001928720 0.0002240550 0.0002078860 0.0001593793

#Convert R/G to Methylated/Unmethylated in an object of class MethylSet
MSet<-preprocessRaw(RGSet)
#QC data
qc<-getQC(MSet)
plotQC(qc)

##Density plot
gRatioSet.quantile <- preprocessQuantile(RGSet, fixOutliers = TRUE, removeBadSamples = TRUE, badSampleCutoff = 10.5, quantileNormalize = TRUE, stratified = TRUE, mergeManifest = TRUE, sex = NULL)
#Quantification and Differential Expression Analysis
mVals<-getM(gRatioSet.quantile)
mVals<-mVals[,targets_filtered$Sample_Name]
phenoData<-phenoData[targets_filtered$Sample_Name,] #ensure that mVals has the same data as phenoData, otherwise dmpFinder won't work.
#Use Limma to perform differential methylation analysis using M-values (logit-transformed methylation data)
targets_filtered$Race<-factor(targets_filtered$Race)
targets_filtered$Race<-relevel(targets_filtered$Race, ref = "Caucasian_American")
targets_filtered$Diabetes<-factor(targets_filtered$Diabetes)
targets_filtered$ICM<-factor(targets_filtered$Ischemia)
design<-model.matrix(~0+Race, data = targets_filtered)
fit <- lmFit(mVals, design)
contMatrix <- makeContrasts(Race=RaceAfrican_American-RaceCaucasian_American,
                           levels=design)
contMatrix

##                         Contrasts
## Levels                   Race
##   RaceCaucasian_American   -1
##   RaceAfrican_American      1

fit2 <- contrasts.fit(fit, contMatrix)
fit2 <- eBayes(fit2)
summary(decideTests(fit2))

##          Race
## Down      550
## NotSig 864898
## Up        411

DMPs <- topTable(fit2, number = "all", adjust.method = "BH")
DMPs<-merge(DMPs, annoEPIC, by= 0)
rownames(DMPs)<-DMPs$Row.names
DMPs<-DMPs %>% dplyr::select(-Row.names)
beta <- getBeta(gRatioSet.quantile)
#create an annotation table
beta.table<-beta
colnames(beta.table)<-phenoData$Sample_Name
write.csv(beta.table, paste0("../3_Output/2_Methyl/", COMPARISON, "/", COMPARISON, "_beta.table.csv"))
# annotated<-merge(beta.table, as.data.frame(Islands.UCSC), by = 0)
# annotated<-merge(annotated, as.data.frame(Locations), by.x = "Row.names", by.y = 0)
# annotated<-merge(annotated, as.data.frame(Other), by.x = "Row.names", by.y = 0)
# rownames(annotated)<-annotated$Row.names

#Merge annotation with differential methylation table
# Results_dmp<-merge(DMPs, annotated, by = 0) #
Results_dmp<-merge(DMPs, beta.table, by = 0)
#Calculate Average CpG Methylation by race - African_American
library(dplyr)
library(matrixStats)
Results<-Results_dmp %>% replace(is.na(.), 0) %>% mutate(
  African_American_SD = rowSds(as.matrix(Results_dmp[,targets_filtered$Sample_Name[targets_filtered$Race=="African_American"]])),
  African_American_Mean = rowMeans(as.matrix(Results_dmp[,targets_filtered$Sample_Name[targets_filtered$Race=="African_American"]])),
  Caucasian_American_SD = rowSds(as.matrix(Results_dmp[,targets_filtered$Sample_Name[targets_filtered$Race=="Caucasian_American"]])),
  Caucasian_American_Mean = rowMeans(as.matrix(Results_dmp[,targets_filtered$Sample_Name[targets_filtered$Race=="Caucasian_American"]])),
  Methylation.Diff=(African_American_Mean-Caucasian_American_Mean)*100
)
rownames(Results)<-Results$Row.names
Results_dmp_p05<-filter(Results, P.Value<0.05)
Results_dmp_q05<-filter(Results, adj.P.Val<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("../3_Output/2_Methyl/", COMPARISON, "/", COMPARISON, "_DMPs.xlsx"), overwrite = TRUE)

Regional Methylation

library(DMRcate)
library(missMethyl)
library(biomaRt)
myAnnotation<-cpg.annotate(object = mVals, datatype = "array", what = "M", 
                           analysis.type = "differential", design = design, 
                           contrasts = TRUE, cont.matrix = contMatrix, 
                           coef = "Race", arraytype = "EPIC")
design_test<-model.matrix(~targets_filtered$Race)
DMRs <- dmrcate(myAnnotation, lambda=1000, C=2) #calculates DMRs
results.ranges <- extractRanges(DMRs)
beta<-beta[,targets_filtered$Sample_Name] #ensure that columns are ordered exactly the same as the targets_filtered table.
groups <- c(African_American="#1b9e77", Caucasian_American="#7570b3")
type<-factor(targets_filtered$Race)
cols <- groups[as.character(type)] #creates a string of colors for the DMR.plot function
pdf(file = "../3_Output/2_Methyl/DMR.top.pdf")
DMR.plot(ranges = results.ranges, dmr = 3, CpGs = beta, what = "Beta", arraytype = "EPIC", phen.col = cols, genome = "hg38")
dev.off()

## quartz_off_screen 
##                 2

results.ranges.sig<-as.data.frame(results.ranges) %>% filter(Fisher < 0.05)
enrichment_GO <- goregion(results.ranges[(elementMetadata(results.ranges)[, "overlapping.genes"] %in% results.ranges.sig$overlapping.genes)], all.cpg = rownames(rownames(beta)), collection = "GO", array.type = "EPIC")

ensembl = useMart("ensembl",dataset="hsapiens_gene_ensembl") #uses human ensembl annotations
#gets gene symbol, transcript_id and go_id for all genes annotated with GO:0007507
gene.data <- getBM(attributes=c('external_gene_name', 'ensembl_transcript_id', 'go_id'), filters = 'go', values = 'GO:0006103', mart = ensembl)

Figure 5B: Distribution of Methylation by Genomic and CpG Annotation

The following figure illustrates the enrichment of differential methylation within CpG “Open Seas” (non-Island associatd CpG sites) found within the associated gene body.

change the viewing angle of 3D-Histogram by dragging mouse across it.

library(plotly)
library(dplyr)
library(stringr)
library(reshape2)
library(readxl)
library(kableExtra)

##Create a regional annotation matrix of the EPIC 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$UCSC_RefGene_Group
Region.matrix<-Region.matrix[c("TSS1500", "TSS200", "5'UTR", "1stExon", "Body", "ExonBnd", "3'UTR"),]
rownames(Region.matrix)<-Region.matrix$UCSC_RefGene_Group
Contour_3D_epic<-Region.matrix[,-1]
rownames(Contour_3D_epic)<-Region.matrix$UCSC_RefGene_Group
write.xlsx(Contour_3D_epic, "../3_Output/2_Methyl/Contour.3D_EPIC.xlsx")

##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$UCSC_RefGene_Group
Region.matrix<-Region.matrix[c("TSS1500", "TSS200", "5'UTR", "1stExon", "Body", "ExonBnd", "3'UTR"),]
rownames(Region.matrix)<-Region.matrix$UCSC_RefGene_Group
Contour_3D<-Region.matrix[,-1]
rownames(Contour_3D)<-Region.matrix$UCSC_RefGene_Group
write.xlsx(Contour_3D, "../3_Output/2_Methyl/Contour.3D_DMPs.p05.xlsx")
### 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)

##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	0.1285693	0.1358225	0.1551988	0.1823682	0.1568289	0.1320130
5’UTR	0.0574760	0.0620397	0.0677872	0.0905560	0.0667659	0.0582758
1stExon	0.0564209	0.0434783	0.0705167	0.0847120	0.0715037	0.0453401
Body	0.0600375	0.0646733	0.0697822	0.0974912	0.0749590	0.0661616
ExonBnd	0.0463516	0.0348432	0.0659091	0.0752212	0.0555556	0.0375940
3’UTR	0.0630122	0.0531746	0.0837796	0.1099838	0.0596878	0.0493359

write.xlsx(Enrichment_Region, paste0("../3_Output/2_Methyl/", COMPARISON, "/", COMPARISON, "_DMP.Enrichment_3D.xlsx"), rowNames = TRUE)
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).

Figure 5C: Heatmap and Hierarchical Clustering of Differential Methylation (P<0.05)

Due to the prominent signature of CpG Island-associated promoter methylation, we examined all DMPs present within this region via heatmap.

library(pheatmap)
library(dplyr)
##Import Data Matrix
# betaHM<-read.csv("../2_Input/EPIC.betaValues.csv", row.names = 1)
## Filters to Apply to DMR
pvalue_threshold=0.05
DMP_location="Island"
Gene_region="TSS"

##Filter Differential Methylation Data
DMR.p05<-Results %>% filter(P.Value<pvalue_threshold)
DMR.p05<-DMR.p05 %>% dplyr::select(Row.names, 
                            Methylation.Diff, 
                            P.Value, 
                            adj.P.Val, 
                            Relation_to_Island, 
                            UCSC_RefGene_Group, 
                            chr, 
                            pos, 
                            contains("LVAD"))
DMR.p05.Region<-DMR.p05 %>% 
  # filter(grepl(DMP_location, Relation_to_Island)) %>%
  filter(grepl(Gene_region, UCSC_RefGene_Group)) %>% 
  dplyr::select(contains("LVAD")) %>%
  data.matrix()
#Import the Index File
LVAD_Counts_Data <- targets
rownames(LVAD_Counts_Data)<-targets$Sample_Name
Index<-LVAD_Counts_Data %>% dplyr::select(Race, Age, BMI)
Index<-as.data.frame(Index)
paletteLength <- 100
ann_colors = list(Race = c(African_American="#1b9e77", Asian_American = "#d95f02", Caucasian_American="#7570b3", Eastern_African = "#e7298a"))
heatmap_DMC<-pheatmap(DMR.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("../3_Output/2_Methyl/", COMPARISON, "/", COMPARISON, "_", DMP_location, ".", Gene_region,"_", "ALL_SAMPLES.heatmap.pdf"))

heatmap_DMC<-pheatmap(DMR.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 8: GEO Coding

library(dplyr)
library("ggplot2")
theme_set(theme_bw())
library("sf")
library("rnaturalearth")
library("rnaturalearthdata")
library("tools")
library("maps")
library(openxlsx)
options(ggrepel.max.overlaps = Inf)
Patient_coord<-read.xlsx("../2_Input/1_Patient/colData_CHAAMPS.FINAL.xlsx", startRow = 2)
rownames(Patient_coord)<-Patient_coord$LVAD_ID
states <- st_as_sf(map("state", plot = FALSE, fill = TRUE))
states <- cbind(states, st_coordinates(st_centroid(states)))
states$ID <- toTitleCase(states$ID)
Patient_coord <- st_as_sf(Patient_coord, coords = c("Longitude", "Lattitude"), remove = FALSE,
    crs = 4326, agr = "constant")
pdf(file = "../3_Output/2_Methyl/GEO_Code.pdf", width = 10, height = 10)
ggplot(data = states) +
    geom_sf() +
    geom_sf(data = Patient_coord) +
    geom_text_repel(data = Patient_coord, aes(x = Longitude, y = Lattitude, label = LVAD_ID), size = 3) +
      geom_point(data = Patient_coord, aes(x = Longitude, y = Lattitude, colour=Race, label = LVAD_ID)) +
    coord_sf(xlim = c(-90, -82), ylim = c(30, 36), expand = FALSE)
dev.off()

## quartz_off_screen 
##                 2

ggplot(data = states) +
    geom_sf() +
    geom_sf(data = Patient_coord) +
    geom_text_repel(data = Patient_coord, aes(x = Longitude, y = Lattitude, label = LVAD_ID), size = 3) +
      geom_point(data = Patient_coord, aes(x = Longitude, y = Lattitude, colour=Race, label = LVAD_ID)) +
    coord_sf(xlim = c(-90, -82), ylim = c(30, 36), expand = FALSE)

Figure 5A: Volcano Plot - DMPs

# Load packages
library(dplyr)
library(ggplot2)
library(ggrepel)
library(openxlsx)
#
Results<-read.xlsx(paste0("../3_Output/2_Methyl/", COMPARISON, "/", COMPARISON, "_DMPs.xlsx"), sheet = "P_0.05")
#Read data from the web
Volcano_data = mutate(Results, sig=ifelse(Results$adj.P.Val<0.05 & abs(Results$Methylation.Diff)>10, "Q<0.05 and |Methylation| > 10%", "Not Sig"), minuslogpvalue = -log(P.Value), Methylation=Methylation.Diff)
max(Volcano_data$minuslogpvalue, na.rm = TRUE)

## [1] 36.50796

# Results<-Results %>% filter(grepl("TSS", UCSC_RefGene_Group))
#plot the ggplot
p = ggplot(Volcano_data, aes(Methylation, minuslogpvalue, color = sig)) + 
  scale_color_manual(values=c('grey','#E69F00'))+
  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),
      legend.position="bottom",
      legend.key=element_blank(),
      axis.text = element_text(size = 12),
      axis.title = element_text(size = 14, face="bold")) +
  geom_point(size = 0.08) + 
  labs(x="Percent Methylation", 
       y=expression(-Log[10](P-value))
       ) + xlim(-75,75)+ 
  ylim(0, 28) + 
  geom_hline(yintercept = 0, size = 1) + 
  geom_vline(xintercept=0, size=1) +
  geom_text_repel(data=filter(Volcano_data, minuslogpvalue>15, abs(Methylation) > 25), aes(label=Name), show_guide = F)
#add a repelling effect to the text labels.
p

pdf(file = paste0("../3_Output/2_Methyl/", COMPARISON, "/", COMPARISON, "Volcano.Plot.pdf"), height = 6, width = 5)
p
dev.off()

## quartz_off_screen 
##                 2

## Interactive Volcano Plot
Filtered_methyl<-filter(Volcano_data, P.Value<0.05, abs(Methylation.Diff)>5)

# library(plotly)
# fig <- plot_ly(
#   Filtered_methyl, x = ~Methylation, y = ~minuslogpvalue,
#   # Hover text:
#   text = ~paste("CpG: ", Methylation, '<br>P-value:', minuslogpvalue, '<br>CpG-ID:', Row.names, '<br>CpG-Region:', Relation_to_Island, '<br>Gene-Symbol:', UCSC_RefGene_Name),
#   color = ~sig, size = ~minuslogpvalue
# )
# fig

RNA-Sequencing Analysis (GSE164197)

RNA-Sequencing Alignment using STAR

Once the proper patient samples were selected for the analysis, RNA was isolated from the left ventricle endocardial tissue using the RNeasy Lipid Mini-Kit according to the manufacturer’s instructions (Qiagen, Valencia, CA). High-throughput RNA sequencing was performed at the Heflin Genomics Core at the University of Alabama at Birmingham. Once sample read quality was checked (multiQC analysis), the paired-end fastq files were then aligned to the reference genome, which was created using Gencode human sequence (GRCh38.p10.genome.fa) and annotation (gencode.v27.chr_patch_hapl_scaff.annotation.gtf). STAR aligner is the current gold-standard for this, which we used for the current analysis. Before aligning each fastq file to the genome, an annotated reference genome must first be assembled. This was performed as follows (this was performed in Cheaha as `bash GenomeReference.sh’:

STAR=../../Tools/STAR-2.5.3a/bin/Linux_x86_64/STAR

$STAR \

--runThreadN 12 \

--runMode genomeGenerate \

--genomeDir ./ \

--genomeFastaFiles /data/scratch/pepinme/huHrt/Input/Genome/GRCh38.p10.genome.fa \

Alignment of short reads to this annotated genome could then proceed, using the following SLURM batch script which was submitted to the UAB Cheaha compute cluster (See Attached Shell script). This shell script contains the following STAR alignment run settings:

$STAR_RUN \

--genomeDir $GENOME_DIR \

--readFilesCommand zcat \

--readFilesIn $INPUT_DIR/fastq/${VAR}_R1_001.fastq.gz \

$INPUT_DIR/fastq/${VAR}_R2_001.fastq.gz \

--sjdbGTFfile $GENOME_DIR/gencode.v27.chr_patch_hapl_scaff.annotation.gtf \

--sjdbOverhang 99 \

--quantMode GeneCounts \

--runThreadN 12 \

--outSAMtype BAM SortedByCoordinate \

--outFileNamePrefix ${RESULTS_DIR}/Alignment/${VAR}_

Read Count Compiling

Before the DESeq2-based differential expression can be computed, the counts generated by STAR need to be compiled, since the .tab file contains count information based on forward, reverse, and combined reads. Therefore, we will take the fourth column in each table and merge them.

Count.files <- list.files(path = "../1_Cheaha/counts/", pattern = "*_ReadsPerGene.out.tab", full.names = TRUE, all.files = TRUE)
Counts <- lapply(Count.files, read.table, skip = 4) #skip the first 4 rows, since these are summary data.
#Create a data.frame containing the raw counts
countData.raw <- as.data.frame(sapply(Counts, function(x) x[,4])) #selects only the 4th column as the raw counts.
#Generate Column names and Row names for the counts (remove the extra nonsense from the path names)
colnames <- gsub( "_ReadsPerGene[.]out[.]tab", "", Count.files)
colnames <- gsub( "[.][.]/1_Cheaha/counts//", "", colnames)
colnames(countData.raw) <- colnames
row.names(countData.raw) <- Counts[[1]][,1]

Data Pre-Processing

After alignment of the fastq files to the annotated genome assembly (hg38), the first step in the analysis is to consolidate the raw data from the provided files into data matrix that can be used to generate a normalized count matrix and differential expression dataset.

## [1] FALSE

Count Normalization

DESeq2 (version 1.18.1) was used to perform the raw count normalization within R (version 3.4.2).

######### RUN DESeq2
dds<-DESeqDataSetFromMatrix(countData=countData, colData = colData, design= ~Race)
dds

## class: DESeqDataSet 
## dim: 58381 29 
## metadata(1): version
## assays(1): counts
## rownames(58381): ENSG00000223972.5 ENSG00000227232.5 ...
##   ENSG00000210195.2 ENSG00000210196.2
## rowData names(0):
## colnames(29): LVAD001 LVAD006 ... LVAD153 LVAD157
## colData names(35): LVAD_ID Zip9 ... RA Color

# dds$ICM<-relevel(dds$ICM, ref = "NICM") # setting the reference to wild-type genotype. only relevant for factors.
#Determine the Dispersion Relationship (determines which distribution to use for the differential analysis) - should take about 2 minutes
dds <- estimateSizeFactors(dds)
dds <- estimateDispersions(dds)
plotDispEsts(dds)

png(file=paste0("../3_Output/1_RNA/", COMPARISON, "/", COMPARISON, "_Dispersion.png"))
plotDispEsts(dds)
dev.off()

## quartz_off_screen 
##                 2

There appears to be a linear negative correlation between the mean and dispersion estimates, so the parametric “Wald” model should be an appropriate fit for differential expression analysis. Furthermore, we could get away with the parametric fit-type, but the run-time is not significantly impaired, allowing us to use the ‘local’ fit-type. NOTE: If it were nonlinear throughout, we would require a ‘local’ nonparametric fit-type.

Differential Expression Analysis

##Pre-Filter to reduce the size of this dataset (according to the DESeq2 document reccomendations)
dds <- dds[ rowSums(counts(dds)) > 1, ]
dds

## class: DESeqDataSet 
## dim: 32903 29 
## metadata(1): version
## assays(2): counts mu
## rownames(32903): ENSG00000223972.5 ENSG00000227232.5 ...
##   ENSG00000210195.2 ENSG00000210196.2
## rowData names(10): baseMean baseVar ... dispOutlier dispMAP
## colnames(29): LVAD001 LVAD006 ... LVAD153 LVAD157
## colData names(36): LVAD_ID Zip9 ... Color sizeFactor

################Run DESeq2 differential quantification (Likelihood ratio test (LRT) or Wald-test)
dds<-DESeq(dds, test="Wald", fitType="parametric")
#compile the results tables
resultsNames(dds)

## [1] "Intercept"                                  
## [2] "Race_African_American_vs_Caucasian_American"

resdf<-as.data.frame(results(dds, format = "DataFrame"))
resdf$ensembl_gene_id<-as.character(row.names(resdf))

Once the differential Expression analysis was performed, the following were compiled into a results data matrix: Log2FoldChange, P-value, Bonferroni-Adjusted P-Value (Q-value), and normalized counts for each sample.

####Add Annotation to the results file (this will take some time, about 5 minutes...)
##Add Gene Information
library(biomaRt)
hsapiens <- biomaRt::useEnsembl(biomart = "ENSEMBL_MART_ENSEMBL",
                                     host = "www.ensembl.org",
                                     dataset = "hsapiens_gene_ensembl",
                                     version = 94)
bm <- getBM(attributes=c("ensembl_gene_id_version", "external_gene_name", "chromosome_name", "start_position", "end_position"),  mart=hsapiens)
write.csv(bm, "../2_Input/BiomaRt_Annotation.csv")
bm<-read.csv("../2_Input/BiomaRt_Annotation.csv", row.names = 1)
results<-merge(resdf, bm, by.x="ensembl_gene_id", by.y="ensembl_gene_id_version")

####Add normalized count data (for heatmap and sota)
normcount<-as.data.frame(counts(dds, normalized=TRUE))
normcount$ensembl_gene_id<-rownames(normcount)
results<-dplyr::left_join(results, normcount, by="ensembl_gene_id")
results<-results[order(results$pvalue),] # order table by pvalue
##Create a Counts table with annotated Gene name
# Counts_table<-dplyr::select(results, external_gene_name, contains("LVAD"))
# write.xlsx(Counts_table, "../2_Input/CountData.xlsx", row.names = FALSE)
#Create filters as tabs
results_p05<-dplyr::filter(results, pvalue<0.05) 
results_q05<-dplyr::filter(results, padj<0.05)
library(openxlsx)
wb_DESeq<-createWorkbook()
#Unfiltered
  addWorksheet(wb_DESeq, "Unfiltered")
  writeData(wb_DESeq, "Unfiltered", results, startCol = 1)
#P-value Significant (0.05)
  addWorksheet(wb_DESeq, "P_0.05")
  writeData(wb_DESeq, "P_0.05", results_p05, startCol = 1)
#Q-value Significant (0.05)
  addWorksheet(wb_DESeq, "Q_0.05")
  writeData(wb_DESeq, "Q_0.05", results_q05, startCol = 1)
saveWorkbook(wb_DESeq, file = paste0("../3_Output/1_RNA/", COMPARISON, "/", COMPARISON,"_DESeq2.xlsx"), overwrite = TRUE)

Supplemental Figure S5: MDS Plot Transcriptomics

In contrast to our epigenomic analysis, unsupervised MDS failed to denote any separation by race (Supplemental Figure S5).

library(limma)
#Rewrite the plotMDS function to output gene index.
plotMDS.default<-
function (x, top = 500, labels = colnames(x), col = NULL, cex = 1, 
    dim.plot = c(1, 2), ndim = max(dim.plot), gene.selection = "pairwise", 
    xlab = paste("Dimension", dim.plot[1]), ylab = paste("Dimension", 
        dim.plot[2]), ...) 
{
    x <- as.matrix(x)
    ok <- is.finite(x)
    if (!all(ok)) 
        x <- x[apply(ok, 1, all), ]
    if (is.null(labels)) 
        labels <- 1:dim(x)[2]
    nprobes <- nrow(x)
    nsamples <- ncol(x)
    if (ndim < 2) 
        stop("Need at least two dim.plot")
    if (nsamples < ndim) 
        stop("Two few samples")
    gene.selection <- match.arg(gene.selection, c("pairwise", 
        "common"))
    cn <- colnames(x)
    dd <- matrix(0, nrow = nsamples, ncol = nsamples, dimnames = list(cn, 
        cn))
    topindex <- nprobes - top + 1
    if (gene.selection == "pairwise") {
        for (i in 2:(nsamples)) for (j in 1:(i - 1)) dd[i, j] = sqrt(mean(sort.int((x[, 
            i] - x[, j])^2, partial = topindex)[topindex:nprobes]))
    }
    else {
    
    #       Same genes used for all comparisons ,"common"
   
        s <- rowMeans((x - rowMeans(x))^2)
        q <- quantile(s, p = (topindex - 1.5)/(nprobes - 1))

        x <- x[s >= q, ]
    
    #    an extra line
        ind.top.genes<-which(s >= q)
        
        for (i in 2:(nsamples)) dd[i, 1:(i - 1)] = sqrt(colMeans((x[, 
            i] - x[, 1:(i - 1), drop = FALSE])^2))
    }
    a1 <- cmdscale(as.dist(dd), k = ndim)
    mds <- new("MDS", list(dim.plot = dim.plot, distance.matrix = dd, 
        cmdscale.out = a1, top = top, gene.selection = gene.selection))
    mds$x <- a1[, dim.plot[1]]
    mds$y <- a1[, dim.plot[2]]
     mdsPlot<-plotMDS(mds, labels = labels, col = col, cex = cex, xlab = xlab, 
        ylab = ylab, ...)
list       (mds=mds,  ind.top.genes=ind.top.genes)  
}

# Perform MDS on RNA-sequencing data
MDS_data<-read.xlsx("../2_Input/CountData.xlsx")
rownames(MDS_data)<-make.unique(MDS_data$external_gene_name, sep = ".")
MDS_data<-dplyr::select(MDS_data, -external_gene_name)
## Create color based on race
ann_colors = list(Race = c(African_American="#1b9e77", Asian_American = "#d95f02", Caucasian_American="#7570b3", Eastern_African = "#e7298a"))
## MDS PLot
NGENES = 10000
mds<-plotMDS.default(MDS_data, gene.selection = "common", top = NGENES, col = colData_all$Color)

## Extract genes from this
MDS_GeneList<-rownames(as.data.frame(mds$ind.top.genes))
TopGenes_MDS<-subset(results, ensembl_gene_id %in% MDS_GeneList)

Supplemental Figure S6: In Silico Cell Sorting via Cibersort

Due to the documented importance immune cell composition on cardiac phenotype (18, 56), in-silico method of cellular deconvolution was performed using CIBERSORT (https://cibersort.stanford.edu) to infer relative enrichment of immunologic cell-types in our cardiac samples. No racial differences were noted from this approach.

Supplemental Figure S6

QQ Plot

Before we examined the gene networks and pathways differentially regulated by NRF2 knockout, the first task was to determine whether transgene induction resulted in global changes. An effective way of determining this is the QQ plot, which compares the P-value distribution produced by the pairwise comparison to that of a random normal distribution. Below, it is evident that differential expression according to patient race yields a robustly divergent expression pattern that likely reflects population differences in gene expression.

#Create Q-Q plot
test<-results
test<-test[complete.cases(test),]
pQQ(test$pvalue, lim=c(0,10))

png(file=paste0("../3_Output/1_RNA/", COMPARISON,  "/", COMPARISON,"_QQ.Plot.png"))
pQQ(test$pvalue, lim=c(0,10))
dev.off()

## quartz_off_screen 
##                 2

Principal Components Analysis

Once we established that the populations under consideration truly display divergene expression patterns, we sought to determine whether unbiased global gene expression patterns recapitulate the described phenotypes within each heart failure group. To accomplish this, an unsupervised Principal Components Analysis (PCA) was initially used with normalized counts.

PCA Features

Before running the principal components analysis, it was necessary to first determine the number of PC’s required to account for 80% of the variance, a machine-learning algorithmm benchmark that provides sufficient confidence in the analysis.

#Plot Features of the PCA
library(dplyr)
library(plotly)
##Import the data to be used for PCA
normCount_all<-data.matrix(read.xlsx("../2_Input/RNA_input.xlsx", sheet = "CountData", rowNames = T))
#transpose the dataset (required for PCA)
data.pca<-t(normCount_all)
data.pca<-as.data.frame(data.pca)
##Import the data to be used for annotation
rownames(colData_all)<-colData_all$LVAD_ID
Index<-colData_all
Index<-as.data.frame(Index)
##merge the file
data.pca_Final<-merge(Index, data.pca, by=0)
rownames(data.pca_Final)<-data.pca_Final$Row.names
pca.comp<-prcomp(data.pca_Final[,(ncol(Index)+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("../3_Output/1_RNA/", 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<-openxlsx::read.xlsx("../2_Input/1_Patient/colData_CHAAMPS.FINAL.xlsx", sheet="Summary", startRow=2)
# Index_PCA<-dplyr::filter(Index_PCA, Race %in% c("Caucasian_American", "African_American"))
Index_PCA$Race<-factor(Index_PCA$Race, levels = c("Caucasian_American","African_American", "Asian_American", "Eastern_African"))
rownames(Index_PCA)<-Index_PCA$LVAD_ID
PCs<-merge(pca.comp$x, Index_PCA, by=0)
rownames(PCs)<-PCs$Row.names
# PCs$Race[which(PCs$Race == 1)] <- 'Caucasian American'
# PCs$Race[which(PCs$Race == 2)] <- 'African American'
# PCs$Race[which(PCs$Race == 3)] <- 'Asian American'
# PCs$Race[which(PCs$Race == 4)] <- 'Eastern African'
PCs$Race <- as.factor(PCs$Race)
fig <- plot_ly(PCs, x = ~PC1, y = ~PC2, z = ~PC3, color = ~Race, colors = c("#7570b3", "#1b9e77", "#d95f02", "#e7298a"))
fig <- fig %>% add_markers()
fig <- fig %>% layout(scene = list(xaxis = list(title = 'PC1'),
                     yaxis = list(title = 'PC2'),
                     zaxis = list(title = 'PC3')))
fig

Heatmap and Clustering of DEGs (P < 0.01)

In order to visualize the distribution of differentially expressed genes, as well as determine the effect various heart failure etiologies on transcription, hierarchical clustering and heatmap visualization were performed at the Q < 0.05 statistical level. This analysis reveals that P < 0.05 is sufficient to delineate hierarchical clustering according to patient race.

library(pheatmap)
results_p05<-filter(results, pvalue<0.01)
normCount_all<-read.xlsx("../2_Input/CountData.xlsx", rowNames = F)
rownames(normCount_all)<-make.unique(normCount_all$external_gene_name, sep = ".")
normCount_all<-data.matrix(normCount_all[,2:ncol(normCount_all)])
hm_data<-subset(normCount_all, rownames(normCount_all) %in% results_p05$external_gene_name)
hm_data<-data.matrix(hm_data)
##
##Index file for annotating samples
rownames(colData_all)<-colData_all$LVAD_ID
Index<-dplyr::select(colData_all, Race, Age, Cardiac.Index, BMI)
Index<-as.data.frame(Index)
ann_colors = list(Race = c(African_American="#1b9e77", Asian_American = "#d95f02", Caucasian_American="#7570b3", Eastern_African = "#e7298a"))
paletteLength <- 100
myColor <- colorRampPalette(c("dodgerblue4", "white", "gold2"))(paletteLength)
pheatmap(hm_data, scale="row", 
         cluster_cols = TRUE, 
         cluster_rows = TRUE,
         #breaks = myBreaks,
         cutree_cols = 2,
         cutree_rows = 2,
         angle_col = 45,
         fontsize_col = 8,
         color = myColor, 
         show_rownames = FALSE, 
         border_color = NA, 
         annotation_colors = ann_colors,
         annotation_col = Index,
         filename=paste0("../3_Output/1_RNA/", COMPARISON,  "/", COMPARISON,"_Heatmap_Normcount.P01.pdf"))
vst<-varianceStabilizingTransformation(data.matrix(read.xlsx("../2_Input/RNA_input.xlsx", sheet = "CountData", rowNames = T)))
normhm<-vst[row.names(resdf[which(resdf$pvalue<0.05),]),]
normhm<-scale(t(normhm))
normhm<-t(normhm)
pheatmap(normhm, #Variance stabilized transformation
         cluster_cols=T, 
         clustering_method = "ward.D2",
         border_color=NA, 
         cluster_rows=T, 
         scale = 'row', 
         show_colnames = T, 
         show_rownames = F, 
         annotation_colors = ann_colors,
         color = myColor,
         annotation_col = Index,
         filename=paste0("../3_Output/1_RNA/", COMPARISON,  "/", COMPARISON,"_VST.Heatmap.P01.pdf"))

pheatmap(normhm, #Variance stabilized transformation
         cluster_cols=T, 
         clustering_method = "ward.D2",
         border_color=NA, 
         cluster_rows=T, 
         scale = 'row', 
         show_colnames = T, 
         show_rownames = F, 
         annotation_colors = ann_colors,
         color = myColor,
         annotation_col = Index)

DEGs involved in Epigenetic Regulation

We used the following script to identify all epigenetic regulators that appear differentially-expressed in cardiac samples of African Americans relative to their age-adjusted Caucasian American counterparts. This provides a list of candidate mediators of methylation differences.

##########Find all Epigeneric Regulators 
EPIs<-openxlsx::read.xlsx("../2_Input/Miscelleneous/EpiGenes_main.xlsx")
EPIs_Genes<-EPIs$HGNC_symbol
DEG_List<-read.xlsx(paste0("../3_Output/1_RNA/", COMPARISON, "/", COMPARISON, "_DESeq2.xlsx"), sheet = "Unfiltered")
Epi_DEGs<-merge(DEG_List, EPIs, by.x="external_gene_name", by.y="HGNC_symbol")
openxlsx:::write.xlsx(Epi_DEGs,paste0("../3_Output/1_RNA/", COMPARISON,"/",COMPARISON, "_Epigenetic.DEGs.xlsx"))
#############################

Differentially-expressed Genes in heart failure.

Figure 6A-B: Gene Expression Bar Graphs

It was desired to inspect individual genes’ expression according to patient race. To accomplish this, the following script enables parsing of normalized counts to generate a quick comparison of gene expression.

###########################################################################################################
library(ggplot2)
library(gridExtra)
library(ggpubr)
library(dplyr)
library(gtools)
library(openxlsx)
ifelse(!dir.exists(file.path(paste0("../3_Output/1_RNA/Candidates"))), dir.create(file.path(paste0("../3_Output/1_RNA/Candidates"))), FALSE)

## [1] FALSE

#Import Index file
colData<-openxlsx::read.xlsx("../2_Input/1_Patient/colData_CHAAMPS.FINAL.xlsx", startRow = 2)
colData$Sample_ID<-as.character(colData$LVAD_ID)
##Import normalized counts
Counts<-read.xlsx("../2_Input/CountData.xlsx")
#Remove outliers
vector<-colData$LVAD_ID
Counts<-dplyr::select(Counts, external_gene_name, all_of(vector))
#Filter results by the gene vector
DEGs<-subset(Counts, external_gene_name %in% GENES)
rownames(DEGs)<-make.unique(as.character(DEGs$external_gene_name, sep = "."))
tDEGs<-as.data.frame(t(DEGs[,2:ncol(DEGs)]))
## convert all genes to numeric (from factors)
asNumeric=function(x){as.numeric(as.character(x))}
factorsNumeric=function(d){modifyList(d, lapply(d[, sapply(d, is.character)], asNumeric))}
##
tDEGs<-factorsNumeric(tDEGs)
tDEGs$LVAD_ID<-rownames(tDEGs)
colData.ex<-dplyr::inner_join(tDEGs, colData)
colData.ex$Race<-factor(colData.ex$Race, levels = c("African_American", "Caucasian_American", "Asian_American", "Eastern_African"))
colData.ex<-dplyr::group_by_(colData.ex, "Race") #define groups for statistics
write.xlsx(colData.ex, "../3_Output/1_RNA/Candidates/Candidate_genes.xlsx")
## For loop creating a graph for each gene
plotlist = list()
p<-1
for (i in GENES){
g_plot<-ggboxplot(colData.ex, x = "Race", 
          y = i, 
          fill = "Race",
          add = "jitter"
          ) + 
  scale_fill_manual(values = RaceColors) +
  stat_compare_means(aes(group = Race),
                    comparisons = my_comparisons, 
                    label = "p.signif"
                    ) +
  theme(axis.text.x=element_text(size=rel(0.75)), axis.text.y=element_text(size=rel(0.75)), legend.position="bottom") + # resize labels, remove legend
  scale_y_continuous(limits = c(0, max(colData.ex[,i])*1.1)) # + # expand = expansion(mult = c(0, 0.1)) ### Y scale (to see the statistics)
  # geom_text_repel(aes(label=Sample_ID), , color = "grey", size = 2) # Add Sample_ID
pdf(file=paste0("../3_Output/1_RNA/Candidates/", i, "_Expression.pdf"), width = 4, height = 3)
print(g_plot)
dev.off()
plotlist[[i]] = g_plot
}
t<-marrangeGrob(grobs = plotlist, legend, nrow=3, ncol=2)
ggsave("../3_Output/1_RNA/Candidates/_DEGs.pdf", t, width = 6, height = 7)
t

########################################################

Pathway-based Gene Expression

Once it was noted that differential expression disproportionately affected metabolic pathways, a script was created to examine all genes encoding intermediates of said metabolic pathways, as follows:

#GENE SET PLOTTING
####This code is useful in determining DEGs that associate with a given pathway
########################################################
library(ggplot2)
library(gridExtra)
library(ggpubr)
library(dplyr)
library(gtools)
######################################################
#Import dataset
Counts<-read.xlsx("../2_Input/CountData.xlsx")
#Import the Pathway Information
GENE.Pathway<-read.csv("../2_Input/Gene.Sets/Oxphos.KEGG.csv", header = F)
colnames(GENE.Pathway)<-"external_gene_name"
#Filter results by the gene vector
DEGSET<-Counts %>% inner_join(., GENE.Pathway)
rownames(DEGSET)<-DEGSET$external_gene_name
GENESET_FIL=as.character(DEGSET$external_gene_name)
colData<-openxlsx::read.xlsx("../2_Input/1_Patient/colData_CHAAMPS.FINAL.xlsx", sheet = "Summary", startRow = 2)
colData<-dplyr::filter(colData, Race %in% RACE, Ischemia %in% ISCHEMIA, Diabetes %in% DIABETES)
#Filter results by the gene vector
tDEGs<-as.data.frame(t(DEGSET))
tDEGs = tDEGs[-1, ]
## convert all genes to numeric (from factors)
asNumeric=function(x){as.numeric(as.character(x))}
factorsNumeric=function(d){modifyList(d, lapply(d[, sapply(d, is.character)], asNumeric))}
##
tDEGs<-factorsNumeric(tDEGs)
tDEGs$LVAD_ID<-rownames(tDEGs)
colData.ex<-dplyr::inner_join(tDEGs, colData)
colData.ex$Race<-factor(colData.ex$Race, levels = c("Caucasian_American", "African_American"))
colData.ex$Ischemia<-factor(colData.ex$Ischemia, levels = c("NICM", "ICM"))
colData.ex<-dplyr::group_by_(colData.ex, VAR1) #define groups for statistics
groupsize<-dplyr::tally(colData.ex) #define sample size (for SEM)
#Define function for mean and standard deviation for each group
data_summary <- function(data, varname, groupnames){
  require(plyr)
  summary_func <- function(x, col){
    c(mean = mean(x[[col]], na.rm=TRUE),
      sd = sd(x[[col]], na.rm=TRUE))
  }
  data_sum<-ddply(data, groupnames, .fun=summary_func,
                  varname)
  data_sum <- rename(data_sum, c("mean" = varname))
 return(data_sum)
}
#Create a p-value for every summarized data (relative to CON)
t.test2 <- function(m1,m2,s1,s2,n1,n2,m0=0,equal.variance=FALSE)
{
    if( equal.variance==FALSE ) 
    {
        se <- sqrt( (s1^2/n1) + (s2^2/n2) )
        # welch-satterthwaite df
        df <- ( (s1^2/n1 + s2^2/n2)^2 )/( (s1^2/n1)^2/(n1-1) + (s2^2/n2)^2/(n2-1) )
    } else
    {
        # pooled standard deviation, scaled by the sample sizes
        se <- sqrt( (1/n1 + 1/n2) * ((n1-1)*s1^2 + (n2-1)*s2^2)/(n1+n2-2) ) 
        df <- n1+n2-2
    }      
    t <- (m1-m2-m0)/se 
    dat <- c(m1-m2, se, t, 2*pt(-abs(t),df))    
    names(dat) <- c("Difference of means", "Std Error", "t", "p-value")
    return(dat) 
}
########################################################
plotlist = list()
p<-1
if(any(grepl("package:plyr", search()))) detach("package:plyr") else message("plyr not loaded")
for (i in GENESET_FIL){
  GENE<-as.name(i) #define a "name" for the variable
  ds <- data_summary(colData.ex, varname=i, groupnames=c(VAR1))
  ds<-dplyr::left_join(ds, groupsize)
  ds<-dplyr::mutate(ds, upper=ds[,2]+(sd))
  for(j in seq_along(ds[,4])){
    p[j]=round(t.test2(m1=ds[j,2], m2=ds[1,i], s1=ds[j,"sd"], s2=ds[1,"sd"], n1=ds[j,"n"], n2=ds[1,"n"], equal.variance = FALSE)[4], 2)
  } ### THIS MUST BE CORRECTED BASED ON THE ANALYSIS (change [,THIS] as needed to create: m = mean, s = standard deviation, n = sample size.
  ds$p_con<-stars.pval(p)
g<-ggplot(ds, aes_string(x=VAR1, y=i, fill = VAR1)) +
    geom_bar(stat="identity", color="black",
           position=position_dodge()) +
    # geom_point(shape="O",stat="identity", mapping = aes_string(x=VAR2, y=i),data = colData.ex, position=position_dodge(0.9), stroke = 2) +
    geom_errorbar(aes_string(ymin=i, ymax="upper"), width=.2,
                 position=position_dodge(.9)) +
    geom_text(aes(label=p_con), position=position_dodge(width=0.9), vjust=-1, size=5) +
    scale_fill_manual(values=c("white", "black", "darkcyan")) +
    ylim(NA, 1.1*max(ds$upper))
g_plot<-g+labs(title=paste0("Expression - ", i), x=VAR1, y = "Normalized Counts")+
   theme_classic2(base_size = 10)
pdf(file=paste0("../3_Output/1_RNA/Candidates/", i, "_", VAR1, ".and.", VAR1, "_Expression.pdf"), width = 3, height = 1.5)
print(g_plot)
dev.off()
plotlist[[i]] = g_plot
}
t<-marrangeGrob(grobs = plotlist, legend, nrow=3, ncol=2)
ggsave(paste0("../3_Output/1_RNA/Candidates/_DEGs_Pathway", VAR1, ".v.", VAR2, ".pdf"), t, width = 6, height = 7)
########################################################

Figure 7: Combining DNA methylation with RNA-sequencing Analysis

library(dplyr)
library(openxlsx)
library(tidyr)
STATISTIC = 0.05 #P statistic threshold used in this combination.
ifelse(!dir.exists(file.path(paste0("../3_Output/3_Combined/", COMPARISON))), dir.create(file.path(paste0("../3_Output/3_Combined/", COMPARISON))), FALSE)

## [1] FALSE

#Import the differential Methylation data
DEGs<-read.xlsx(paste0("../3_Output/1_RNA/", COMPARISON, "/", COMPARISON, "_DESeq2.xlsx"), sheet = "P_0.05")
colnames(DEGs)<-paste0("RNA_",colnames(DEGs))
DEGs$Gene.Symbol<-DEGs$RNA_external_gene_name
DMPs<-read.xlsx(paste0("../3_Output/2_Methyl/", COMPARISON, "/", COMPARISON, "_DMPs.xlsx"), sheet = "P_0.05")
colnames(DMPs)<-paste0("Methyl_", colnames(DMPs))
##
DMPs_separated<-DMPs %>% mutate(DMPs, chromStart=Methyl_pos, chromEnd=Methyl_pos+1, Gene.Symbol = strsplit(as.character(Methyl_UCSC_RefGene_Name), ";")) %>% unnest(Gene.Symbol) %>% distinct()
DMPs_separated$Gene.Symbol<-as.character(DMPs_separated$Gene.Symbol)
write.csv(DMPs_separated, "DMPs_separated.csv")
#Merge the datasets
DMPs_DEGs<-inner_join(DEGs, DMPs_separated, by="Gene.Symbol")
DMPs_promoter.DEGs<-dplyr::filter(DMPs_DEGs, grepl("TSS", Methyl_UCSC_RefGene_Group), RNA_pvalue<0.05) %>% filter((Methyl_Methylation.Diff>0 & RNA_log2FoldChange<0) | (Methyl_Methylation.Diff<0 & RNA_log2FoldChange>0))
Up.DEGs<-dplyr::filter(DMPs_promoter.DEGs, RNA_log2FoldChange<0)
Down.DEGs<-dplyr::filter(DMPs_promoter.DEGs,  RNA_log2FoldChange>0)

#Body Promoter DEGs
DMPs_body.DEGs<-DMPs_DEGs %>% filter(grepl("Body", Methyl_UCSC_RefGene_Group), RNA_pvalue<0.05) %>% filter((Methyl_Methylation.Diff>0 & RNA_log2FoldChange>0) | (Methyl_Methylation.Diff<0 & RNA_log2FoldChange<0))

library(openxlsx)
wb_combine<-createWorkbook()
#Unfiltered
  addWorksheet(wb_combine, "Inverse Promoter")
  writeData(wb_combine, "Inverse Promoter", DMPs_promoter.DEGs, startCol = 1)
  addWorksheet(wb_combine, "Direct Body")
  writeData(wb_combine, "Direct Body", DMPs_body.DEGs, startCol = 1)
saveWorkbook(wb_combine, file = paste0("../3_Output/3_Combined/", COMPARISON,"/",COMPARISON, "_Inverse_DMPs.Promoter.xlsx"), overwrite = TRUE)

Figure 7A: Combined Heatmap

library(ComplexHeatmap)
library(openxlsx)
library(dplyr)
# detach("package:pheatmap", unload=TRUE)
#Index
Index_hm<-read.xlsx("../2_Input/1_Patient/colData_CHAAMPS.FINAL.xlsx", rowNames = T, startRow = 2)
Index_hm<-Index_hm %>% 
  subset(Race %in% RACE) %>% 
  filter(rownames(.)!="LVAD054") %>%
  dplyr::select(Race, Age, BMI, Cardiac.Index)

#Gene Expression
DEG_hm.prom<-DMPs_promoter.DEGs %>% dplyr::select(contains("RNA_LVAD")) %>% data.matrix()
rownames(DEG_hm.prom)<-make.unique(DMPs_promoter.DEGs$RNA_external_gene_name, sep = ".")
colnames(DEG_hm.prom)<-gsub('RNA_','',colnames(DEG_hm.prom)) #Remove RNA_ prefix from column names
DEG_hm.prom<-t(scale(t(DEG_hm.prom)))

DMP_hm.prom<-DMPs_promoter.DEGs %>% dplyr::select(contains("Methyl_LVAD")) %>% data.matrix()
rownames(DMP_hm.prom)<-make.unique(DMPs_promoter.DEGs$RNA_external_gene_name, sep = ".")
colnames(DMP_hm.prom)<-gsub('Methyl_','',colnames(DMP_hm.prom)) #Remove Methyl_ prefix from column names
DMP_hm.prom<-t(scale(t(DMP_hm.prom)))

#Make heatmap for DMPs
paletteLength <- 100
myColor_DMC <- colorRampPalette(c("cyan4", "white", "firebrick2"))(paletteLength)
ann_colors = list(Race = c(African_American="#1b9e77", Asian_American = "#d95f02", Caucasian_American="#7570b3", Eastern_African = "#e7298a"),
                  BMI = brewer.pal(6, "Blues"),
                  Age = brewer.pal(6, "Purples"),
                  Cardiac.Index = brewer.pal(6, "Reds")
                    )
heatmap_DMC<-pheatmap(DMP_hm.prom, scale="none", 
                      cluster_cols = TRUE, 
                      cluster_rows = TRUE,
                      cutree_rows = 2,
                      fontsize_col =  8,
                      color = myColor_DMC, 
                      clustering_distance_cols = "correlation",
                      show_colnames = F,
                      show_rownames = FALSE, 
                      border_color = NA, 
                      annotation_col = Index_hm,
                      annotation_colors=ann_colors,
                      annotation_legend = F,
                      filename = paste0("../3_Output/3_Combined/", COMPARISON, "/", COMPARISON, "_", DMP_location, ".", Gene_region,"_", "DMP.promoters.heatmap.pdf"))

#DEG Heatmap
colnames_ordered<-colnames(DMP_hm.prom)[column_order(heatmap_DMC)] #Get column order from the DMC Heatmap
Testing<-DEG_hm.prom[,match(colnames_ordered, colnames(DEG_hm.prom))]
paletteLength <- 100
myColor <- colorRampPalette(c("dodgerblue4", "white", "gold2"))(paletteLength) #DEG Colors
heatmap_DEG<-pheatmap(Testing, scale="row", 
                      cluster_cols = T, 
                      cluster_rows = TRUE,
                      cutree_rows = 2,
                      fontsize_col = 8,
                      color = myColor, 
                      clustering_distance_cols = "correlation",
                      show_colnames = F,
                      show_rownames = FALSE, 
                      border_color = NA, 
                      annotation_col = Index_hm,
                      annotation_colors=ann_colors,
                      filename = paste0("../3_Output/3_Combined/", COMPARISON, "/", COMPARISON, "_", DMP_location, ".", Gene_region,"_", "DEG.promoters.heatmap.pdf"))

pdf(file = paste0("../3_Output/3_Combined/", 
                  COMPARISON, "/", 
                  COMPARISON, "_", 
                  DMP_location, ".", 
                  Gene_region,"_", 
                  "DMC.DEG_Overlapping.Heatmap.pdf"), 
    height = 8, 
    width = 10
    )
heatmap_DMC + heatmap_DEG
dev.off()

## quartz_off_screen 
##                 2

heatmap_DMC + heatmap_DEG

Figure 7B: Dendrogram Link

library(tidyverse)  # data manipulation
library(cluster)    # clustering algorithms
library(factoextra) # clustering visualization
library(dendextend) # for comparing two dendrograms

##Methylation
res.dist_DMP <- dist(t(DMP_hm.prom), method = "euclidean")
hc1 <- hclust(res.dist_DMP, method = "ward.D2")
dend1 <- as.dendrogram (hc1)
colors_to_use <- as.numeric(targets_filtered$Race)
colors_to_use <- colors_to_use[order.dendrogram(dend1)]
labels_colors(dend1)<-colors_to_use

##DEG
res.dist_DEG <- dist(t(DEG_hm.prom), method = "euclidean")
hc2 <- hclust(res.dist_DEG, method = "ward.D2")
dend2 <- as.dendrogram (hc2)
colors_to_use2 <- as.numeric(targets_filtered$Race)
colors_to_use2 <- colors_to_use2[order.dendrogram(dend2)]
labels_colors(dend2)<-colors_to_use2
#
dend_list <- dendlist(dend1, dend2)
#
dendTangle<-dend_list %>% untangle(method = "step1side")
##

Test2<-tanglegram(dendTangle, sort = FALSE, common_subtrees_color_lines = TRUE, highlight_distinct_edges  = TRUE, highlight_branches_lwd = TRUE, lwd = 2, edge.lwd = 2, lab.cex=.7)

pdf(file = "../3_Output/3_Combined/TANGLE.pdf", height = 8, width = 10)
tanglegram(dendTangle, sort = FALSE, common_subtrees_color_lines = TRUE, highlight_distinct_edges  = TRUE, highlight_branches_lwd = TRUE, lwd = 2, edge.lwd = 2, lab.cex=.7)
dev.off()

## quartz_off_screen 
##                 2

entanglement(Test2)

## [1] 0.1390582

cors <- cor.dendlist(Test2)

ha_index<-dplyr::select(targets_filtered, Race, HbA1C, BMI)
rownames(ha_index)<-targets_filtered$Sample_Name
ha = HeatmapAnnotation(df = ha_index)

# Print correlation matrix
library(ComplexHeatmap)
p1=ComplexHeatmap::pheatmap(DMP_hm.prom, scale = "row")
p2=Heatmap(scale(DEG_hm.prom), name = "RNA-Seq", km = 2, top_annotation = ha)

p1+p2

Figure 7C: Circos Plot

library(dplyr)
#create gene labels
Gene_labels_Body<-DMPs_body.DEGs%>% dplyr::filter(abs(RNA_log2FoldChange)>.585, grepl("Body", Methyl_UCSC_RefGene_Group)) %>% dplyr::filter(abs(Methyl_Methylation.Diff)>5) %>% dplyr::select(chrom=Methyl_chr, chromStart=RNA_start_position, chromEnd=RNA_end_position, GeneSymbol=RNA_external_gene_name, RNA_padj) %>% distinct() %>% dplyr::top_n(50, RNA_padj) %>% dplyr::select(-RNA_padj)
Gene_labels_Body<-arrange(Gene_labels_Body, chromStart)
Gene_labels_Body$chrom<-factor(Gene_labels_Body$chrom, levels=c("chr1", "chr2", "chr3", "chr4", 
                                                      "chr5", "chr6", "chr7", "chr8", 
                                                      "chr9", "chr10", "chr11", "chr12", 
                                                      "chr13", "chr14", "chr15", "chr16", 
                                                      "chr17", "chr18", "chr19", "chr20", 
                                                      "chr21", "chr22", "chr23", "chrX", 
                                                      "chrY"))
Gene_labels_Body<-Gene_labels_Body[order(Gene_labels_Body$chrom),]
Gene_labels_Body<-Gene_labels_Body[!duplicated(Gene_labels_Body[,4]),]
Gene_labels_Body$Color<-"gray80" #Color the text of the corresponding circos
# Gene Labels based on Genomic Region
Gene_labels_Promoter<-DMPs_promoter.DEGs %>% dplyr::filter(abs(RNA_log2FoldChange)>.585, grepl("TSS", Methyl_UCSC_RefGene_Group)) %>% dplyr::filter(abs(Methyl_Methylation.Diff)>5) %>% dplyr::select(chrom=Methyl_chr, chromStart=RNA_start_position, chromEnd=RNA_end_position, GeneSymbol=RNA_external_gene_name, RNA_padj) %>% distinct() %>% dplyr::top_n(50, RNA_padj) %>% dplyr::select(-RNA_padj)
Gene_labels_Promoter<-arrange(Gene_labels_Promoter, chromStart)
Gene_labels_Promoter$chrom<-factor(Gene_labels_Promoter$chrom, levels=c("chr1", "chr2", "chr3", "chr4", 
                                                      "chr5", "chr6", "chr7", "chr8", 
                                                      "chr9", "chr10", "chr11", "chr12", 
                                                      "chr13", "chr14", "chr15", "chr16", 
                                                      "chr17", "chr18", "chr19", "chr20", 
                                                      "chr21", "chr22", "chr23", "chrX", 
                                                      "chrY"))
Gene_labels_Promoter<-Gene_labels_Promoter[order(Gene_labels_Promoter$chrom),]
Gene_labels_Promoter<-Gene_labels_Promoter[!duplicated(Gene_labels_Promoter[,4]),]
Gene_labels_Promoter$Color<-"gray40" #Color the text of the corresponding circos
# Create a composite Labels frame with coloring based on CpG Region
Gene.labels<-rbind(Gene_labels_Body, Gene_labels_Promoter)
Gene.labels<-Gene.labels[order(Gene.labels$chrom),]


##Gene Expression
##Fold Change UP
Gene_FoldChange.UP<-dplyr::select(DMPs_DEGs, chrom=Methyl_chr, 
                                                      chromStart, chromEnd, FoldChange_DEG=RNA_log2FoldChange)
Gene_FoldChange.UP<-dplyr::filter(Gene_FoldChange.UP, FoldChange_DEG>0)
# Gene_FoldChange.UP<-dplyr::mutate(Gene_FoldChange.UP, chromEnd=chromStart+1)
Gene_FoldChange.UP<-dplyr::select(Gene_FoldChange.UP, chrom, chromStart, chromEnd, FoldChange_DEG)
Gene_FoldChange.UP<-arrange(Gene_FoldChange.UP, chromStart)
Gene_FoldChange.UP$chrom<-factor(Gene_FoldChange.UP$chrom, levels=c("chr1", "chr2", "chr3", "chr4", 
                                                      "chr5", "chr6", "chr7", "chr8", 
                                                      "chr9", "chr10", "chr11", "chr12", 
                                                      "chr13", "chr14", "chr15", "chr16", 
                                                      "chr17", "chr18", "chr19", "chr20", 
                                                      "chr21", "chr22", "chr23", "chrX", 
                                                      "chrY"))
Gene_FoldChange.UP<-Gene_FoldChange.UP[order(Gene_FoldChange.UP$chrom),]
##Fold Change DOWN
Gene_FoldChange.DOWN<-dplyr::select(DMPs_DEGs, chrom=Methyl_chr, 
                                                      chromStart, chromEnd, FoldChange_DEG=RNA_log2FoldChange)
Gene_FoldChange.DOWN<-dplyr::filter(Gene_FoldChange.DOWN, FoldChange_DEG<0)
# Gene_FoldChange.DOWN<-dplyr::mutate(Gene_FoldChange.DOWN, chromEnd=chromStart+1)
Gene_FoldChange.DOWN<-dplyr::select(Gene_FoldChange.DOWN, chrom, chromStart, chromEnd, FoldChange_DEG)
Gene_FoldChange.DOWN<-arrange(Gene_FoldChange.DOWN, chromStart)
Gene_FoldChange.DOWN$chrom<-factor(Gene_FoldChange.DOWN$chrom, levels=c("chr1", "chr2", "chr3", "chr4", 
                                                      "chr5", "chr6", "chr7", "chr8", 
                                                      "chr9", "chr10", "chr11", "chr12", 
                                                      "chr13", "chr14", "chr15", "chr16", 
                                                      "chr17", "chr18", "chr19", "chr20", 
                                                      "chr21", "chr22", "chr23", "chrX", 
                                                      "chrY"))
Gene_FoldChange.DOWN<-Gene_FoldChange.DOWN[order(Gene_FoldChange.DOWN$chrom),]
##Fold Change List
Gene_FoldChange_List<-list(Gene_FoldChange.UP, Gene_FoldChange.DOWN)

# Methylation Density - Islands
DMR_Islands<-DMPs %>% dplyr::filter(Methyl_Relation_to_Island=="Island") %>% dplyr::select(chrom=Methyl_chr, Methyl_pos, perc.change=Methyl_Methylation.Diff)
DMR_Islands<-dplyr::filter(DMR_Islands, chrom!="chrM")
DMR_Islands<-dplyr::mutate(DMR_Islands, chromEnd=Methyl_pos+1) %>% dplyr::select(chrom, chromStart=Methyl_pos, chromEnd, perc.change)
DMR_Islands$chrom<-factor(DMR_Islands$chrom, levels=c("chr1", "chr2", "chr3", "chr4", 
                                                      "chr5", "chr6", "chr7", "chr8", 
                                                      "chr9", "chr10", "chr11", "chr12", 
                                                      "chr13", "chr14", "chr15", "chr16", 
                                                      "chr17", "chr18", "chr19", "chr20", 
                                                      "chr21", "chr22", "chr23", "chrX", 
                                                      "chrY"))
DMR_Islands<-DMR_Islands[order(DMR_Islands$chrom),]
Methyl.UP.Island<-filter(DMR_Islands, perc.change>0)
Methyl.DOWN.Island<-filter(DMR_Islands, perc.change<0)
Methyl.List.island<-list(Methyl.DOWN.Island, Methyl.UP.Island)

# Methylation Density - Seas
DMR_Sea<-DMPs %>% dplyr::filter(Methyl_Relation_to_Island=="OpenSea") %>% dplyr::select(chrom=Methyl_chr, Methyl_pos, perc.change=Methyl_Methylation.Diff)
DMR_Sea<-dplyr::filter(DMR_Sea, chrom!="chrM")
DMR_Sea<-dplyr::mutate(DMR_Sea, chromEnd=Methyl_pos+1) %>% dplyr::select(chrom, chromStart=Methyl_pos, chromEnd, perc.change)
DMR_Sea$chrom<-factor(DMR_Sea$chrom, levels=c("chr1", "chr2", "chr3", "chr4", 
                                                      "chr5", "chr6", "chr7", "chr8", 
                                                      "chr9", "chr10", "chr11", "chr12", 
                                                      "chr13", "chr14", "chr15", "chr16", 
                                                      "chr17", "chr18", "chr19", "chr20", 
                                                      "chr21", "chr22", "chr23", "chrX", 
                                                      "chrY"))
DMR_Sea<-DMR_Sea[order(DMR_Sea$chrom),]
Methyl.UP.Sea<-filter(DMR_Sea, perc.change>0)
Methyl.DOWN.Sea<-filter(DMR_Sea, perc.change<0)
Methyl.List.Sea<-list(Methyl.DOWN.Sea, Methyl.UP.Sea)
# All DMPs
CpG_EPIC<-DMPs %>% dplyr::select(chrom=Methyl_chr, Methyl_pos, perc.change=Methyl_Methylation.Diff)
CpG_EPIC<-dplyr::filter(CpG_EPIC, chrom!="chrM")
CpG_EPIC<-dplyr::mutate(CpG_EPIC, chromEnd=Methyl_pos+1) %>% dplyr::select(chrom, chromStart=Methyl_pos, chromEnd, perc.change)
CpG_EPIC$chrom<-factor(CpG_EPIC$chrom, levels=c("chr1", "chr2", "chr3", "chr4", 
                                                      "chr5", "chr6", "chr7", "chr8", 
                                                      "chr9", "chr10", "chr11", "chr12", 
                                                      "chr13", "chr14", "chr15", "chr16", 
                                                      "chr17", "chr18", "chr19", "chr20", 
                                                      "chr21", "chr22", "chr23", "chrX", 
                                                      "chrY"))
CpG_EPIC<-CpG_EPIC[order(CpG_EPIC$chrom),]
Methyl.UP.EPIC<-filter(CpG_EPIC, perc.change>0)
Methyl.DOWN.EPIC<-filter(CpG_EPIC, perc.change<0)
Methyl.List.EPIC<-list(Methyl.DOWN.EPIC, Methyl.UP.EPIC)

Methyl.List<-list(DMR_Sea,DMR_Islands)
#Plot the Circos
library(circlize)
library(gtools)
library(dplyr)

circos.genomicDensity1 = function (data, ylim.force = FALSE, window.size = NULL, overlap = TRUE, col = ifelse(area, "grey", "black"), lwd = par("lwd"), lty = par("lty"), type = "l", area = TRUE, area.baseline = NULL, baseline = 0, border = NA, ...) { if (!is.null(area.baseline)) 
data = normalizeToDataFrame(data)
if (!is.dataFrameList(data)) {
data = list(data)
}
if (length(col) == 1) {
col = rep(col, length(data))
}
if (length(lwd) == 1) {
lwd = rep(lwd, length(data))
}
if (length(lty) == 1) {
lty = rep(lty, length(data))
}
if (length(type) == 1) {
type = rep(type, length(data))
}
if (length(area) == 1) {
area = rep(area, length(data))
}

if (length(baseline) == 1) {
    baseline = rep(baseline, length(data))
}
if (length(border) == 1) {
    border = rep(border, length(data))
}
s = sapply(get.all.sector.index(), function(si) get.cell.meta.data("xrange", 
    sector.index = si))

if (is.null(window.size)) {
    window.size = 10^nchar(sum(s))/1000
}
df = vector("list", length = length(data))
for (i in seq_along(data)) {
    all.chr = unique(data[[i]][[1]])
    for (chr in all.chr) {
        region = data[[i]][data[[i]][[1]] == chr, 2:3, drop = FALSE]
        dn = genomicDensity(region, window.size = window.size, 
            overlap = overlap)
        dn = cbind(rep(chr, nrow(dn)), dn)
        df[[i]] = rbind(df[[i]], dn)
    }
}
if (ylim.force) {
    ymax = 1
}
else {
    ymax = max(sapply(df, function(gr) max(gr[[4]])))
}

circos.genomicTrackPlotRegion(df, ylim = c(-ymax,0), panel.fun = function(region, 
    value, ...) {
    i = getI(...)

    circos.genomicLines(region, -value, col = col[i], lwd = lwd[i], 
        lty = lty[i], type = type[i], border = border[i], 
        area = area[i], baseline = baseline[i])
}, ...)
}

environment(circos.genomicDensity1) <- asNamespace('circlize')

#to get error line number:

f <- function (data, ylim.force = FALSE, window.size = NULL, overlap = TRUE,
col = ifelse(area, "grey", "black"), lwd = par("lwd"), lty = par("lty"),
type = "l", area = TRUE, area.baseline = NULL, baseline = 0,
border = NA, ...)
{
circos.genomicDensity1(data, ylim.force = FALSE, window.size = NULL, overlap = TRUE,
col = ifelse(area, "grey", "black"), lwd = par("lwd"), lty = par("lty"),
type = "l", area = TRUE, area.baseline = NULL, baseline = 0,
border = NA, ...)
}
###########################################################
# pdf(file="trial_2.pdf",width=10,height=10)
# par(mar = c(1, 1, 1, 1))
# circos.par(gap.degree=1, track.margin = c(0, 0))
# circos.initializeWithIdeogram(track.height = 0.05)
# circos.genomicDensity(Methyl.UP, col = c("goldenrod3"), track.height = 0.1, baseline="bottom", bg.border ="white", track.margin = c(0, 0.0))
# circos.genomicDensity1(Methyl.DOWN, col = c("dodgerblue3"), track.height = 0.1, baseline="top", bg.border ="white", track.margin = c(0, 0.0))
# circos.clear()
# dev.off()
######################################################

om = circos.par("track.margin")
oc = circos.par("cell.padding")
circos.par(track.margin = c(0, 0), cell.padding = c(0, 0, 0, 0))
circos.par(start.degree = -250)
pdf(file=paste0("../3_Output/3_Combined/", COMPARISON, "/", COMPARISON, "_Circos.pdf"))
circos.initializeWithIdeogram(track.height = 0.05)

circos.genomicDensity(Methyl.List, col=add_transparency(c("gray80", "gray40"), transparency = 0.2) ,
                      track.height=0.2, bg.border=NA)
# ### Labels for DMRs in Islands
# circos.genomicDensity(Methyl.UP.Island, col = c("coral2"), track.height = 0.1, baseline="bottom", bg.border ="white", track.margin = c(0, 0.0))
# circos.genomicDensity1(Methyl.DOWN.Island, col = c("darkcyan"), track.height = 0.1, baseline="top", bg.border ="white", track.margin = c(0, 0.0))
# ### Labels for DMRs in Body
# circos.genomicDensity(Methyl.UP.Body, col = c("coral2"), track.height = 0.1, baseline="bottom", bg.border ="white", track.margin = c(0, 0.0))
# circos.genomicDensity1(Methyl.DOWN.Body, col = c("darkcyan"), track.height = 0.1, baseline="top", bg.border ="white", track.margin = c(0, 0.0))
#DEG with inverse GPI Islands Promoters
circos.genomicTrackPlotRegion(Gene_FoldChange_List,
                              ylim = c(-6, 6), bg.border=NA,
                              panel.fun = function(region, value, ...) {
 col = ifelse(value[[1]] > 0, "darkgoldenrod1", "dodgerblue2")
 circos.genomicPoints(region, value, col = add_transparency(col, 0.2), cex = 0.3, pch = 16)
 cell.xlim = get.cell.meta.data("cell.xlim")
 for(h in c(-3, -1.5, 0, 1.5, 3)) {
   circos.lines(cell.xlim, c(h, h), col ="#00000040")
 }
}, track.height = 0.2)

circos.genomicLabels(Gene.labels, labels.column=4, side='inside', col = Gene.labels$Color ,cex=0.6)
# circos.par(track.margin=om, cell.padding=oc)
# ## Add link for all DEGs with DMRs in promoter CGIs
# Link_Anchor <- read.csv("../1_Input/4_Combined/Circos/Link_Anchor.csv")
# Link<-read.csv("../1_Input/4_Combined/Circos/Link_DEG.DMR_Promoter.CGI_P<0.05.csv")
# Link$chrom<-factor(Link$chrom, levels=c("chr1", "chr2", "chr3", "chr4", 
#                                                       "chr5", "chr6", "chr7", "chr8", 
#                                                       "chr9", "chr10", "chr11", "chr12", 
#                                                       "chr13", "chr14", "chr15", "chr16", 
#                                                       "chr17", "chr18", "chr19", "chr20", 
#                                                       "chr21", "chr22", "chr23", "chrX", 
#                                                       "chrY"))
# Link<-Link[order(Link$chrom),]
# Link_Anchor<-Link_Anchor[1:nrow(Link),]
# circos.genomicLink(Link, Link_Anchor, col="black", lwd=0.5)
circos.clear()
dev.off()

## quartz_off_screen 
##                 2

om = circos.par("track.margin")
oc = circos.par("cell.padding")
circos.par(track.margin = c(0, 0), cell.padding = c(0, 0, 0, 0))
circos.par(start.degree = -250)
circos.initializeWithIdeogram(track.height = 0.05)

circos.genomicDensity(Methyl.List, col=add_transparency(c("gray80", "gray40"), transparency = 0.2) ,
                      track.height=0.2, bg.border=NA)
# ### Labels for DMRs in Islands
# circos.genomicDensity(Methyl.UP.Island, col = c("coral2"), track.height = 0.1, baseline="bottom", bg.border ="white", track.margin = c(0, 0.0))
# circos.genomicDensity1(Methyl.DOWN.Island, col = c("darkcyan"), track.height = 0.1, baseline="top", bg.border ="white", track.margin = c(0, 0.0))
# ### Labels for DMRs in Body
# circos.genomicDensity(Methyl.UP.Body, col = c("coral2"), track.height = 0.1, baseline="bottom", bg.border ="white", track.margin = c(0, 0.0))
# circos.genomicDensity1(Methyl.DOWN.Body, col = c("darkcyan"), track.height = 0.1, baseline="top", bg.border ="white", track.margin = c(0, 0.0))
#DEG with inverse GPI Islands Promoters
circos.genomicTrackPlotRegion(Gene_FoldChange_List,
                              ylim = c(-6, 6), bg.border=NA,
                              panel.fun = function(region, value, ...) {
 col = ifelse(value[[1]] > 0, "darkgoldenrod1", "dodgerblue2")
 circos.genomicPoints(region, value, col = add_transparency(col, 0.2), cex = 0.3, pch = 16)
 cell.xlim = get.cell.meta.data("cell.xlim")
 for(h in c(-3, -1.5, 0, 1.5, 3)) {
   circos.lines(cell.xlim, c(h, h), col ="#00000040")
 }
}, track.height = 0.2)

# 
# circos.genomicLabels(Gene.labels, labels.column=4, side='inside', col = Gene.labels$Color ,cex=0.6)
circos.clear()

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 35.38961 mins

library(kableExtra)
sinfo<-devtools::session_info()
sinfo$platform

##  setting  value                       
##  version  R version 4.0.4 (2021-02-15)
##  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     2021-04-01

sinfo$packages %>% kable( 
                         align="c", 
                         longtable=T, 
                         booktabs=T,
                         caption="Packages and Required Dependencies") %>% 
    kable_styling(latex_options=c("striped", "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/4.0/Resources/library/abind	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/abind	FALSE	FALSE	2016-07-21	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
annotate	annotate	1.68.0	1.68.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/annotate	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/annotate	FALSE	FALSE	2020-10-27	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
AnnotationDbi	AnnotationDbi	1.52.0	1.52.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/AnnotationDbi	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/AnnotationDbi	TRUE	FALSE	2020-10-27	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
AnnotationFilter	AnnotationFilter	1.14.0	1.14.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/AnnotationFilter	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/AnnotationFilter	FALSE	FALSE	2020-10-27	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
AnnotationHub	AnnotationHub	2.22.0	2.22.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/AnnotationHub	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/AnnotationHub	TRUE	FALSE	2020-10-27	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
askpass	askpass	1.1	1.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/askpass	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/askpass	FALSE	FALSE	2019-01-13	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
assertthat	assertthat	0.2.1	0.2.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/assertthat	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/assertthat	FALSE	FALSE	2019-03-21	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
backports	backports	1.2.1	1.2.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/backports	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/backports	FALSE	FALSE	2020-12-09	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
base64	base64	2.0	2.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/base64	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/base64	FALSE	FALSE	2016-05-10	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
base64enc	base64enc	0.1.3	0.1-3	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/base64enc	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/base64enc	FALSE	FALSE	2015-07-28	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
beanplot	beanplot	1.2	1.2	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/beanplot	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/beanplot	FALSE	FALSE	2014-09-19	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
BiasedUrn	BiasedUrn	1.7	1.07	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/BiasedUrn	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/BiasedUrn	FALSE	FALSE	2015-12-28	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
Biobase	Biobase	2.50.0	2.50.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/Biobase	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/Biobase	TRUE	FALSE	2020-10-27	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
BiocFileCache	BiocFileCache	1.14.0	1.14.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/BiocFileCache	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/BiocFileCache	TRUE	FALSE	2020-10-27	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
BiocGenerics	BiocGenerics	0.36.0	0.36.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/BiocGenerics	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/BiocGenerics	TRUE	FALSE	2020-10-27	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
BiocManager	BiocManager	1.30.10	1.30.10	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/BiocManager	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/BiocManager	FALSE	FALSE	2019-11-16	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
BiocParallel	BiocParallel	1.24.1	1.24.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/BiocParallel	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/BiocParallel	FALSE	FALSE	2020-11-06	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
BiocVersion	BiocVersion	3.12.0	3.12.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/BiocVersion	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/BiocVersion	FALSE	FALSE	2020-05-14	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
biomaRt	biomaRt	2.46.3	2.46.3	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/biomaRt	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/biomaRt	TRUE	FALSE	2021-02-11	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
Biostrings	Biostrings	2.58.0	2.58.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/Biostrings	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/Biostrings	TRUE	FALSE	2020-10-27	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
biovizBase	biovizBase	1.38.0	1.38.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/biovizBase	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/biovizBase	FALSE	FALSE	2020-10-27	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
bit	bit	4.0.4	4.0.4	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/bit	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/bit	FALSE	FALSE	2020-08-04	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
bit64	bit64	4.0.5	4.0.5	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/bit64	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/bit64	FALSE	FALSE	2020-08-30	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
bitops	bitops	1.0.6	1.0-6	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/bitops	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/bitops	FALSE	FALSE	2013-08-17	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
blob	blob	1.2.1	1.2.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/blob	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/blob	FALSE	FALSE	2020-01-20	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
broom	broom	0.7.5	0.7.5	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/broom	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/broom	FALSE	FALSE	2021-02-19	CRAN (R 4.0.4)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
BSgenome	BSgenome	1.58.0	1.58.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/BSgenome	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/BSgenome	FALSE	FALSE	2020-10-27	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
bslib	bslib	0.2.4	0.2.4	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/bslib	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/bslib	FALSE	FALSE	2021-01-25	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
bsseq	bsseq	1.26.0	1.26.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/bsseq	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/bsseq	FALSE	FALSE	2020-10-27	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
bumphunter	bumphunter	1.32.0	1.32.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/bumphunter	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/bumphunter	TRUE	FALSE	2020-10-27	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
cachem	cachem	1.0.4	1.0.4	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/cachem	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/cachem	FALSE	FALSE	2021-02-13	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
Cairo	Cairo	1.5.12.2	1.5-12.2	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/Cairo	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/Cairo	FALSE	FALSE	2020-07-07	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
calibrate	calibrate	1.7.7	1.7.7	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/calibrate	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/calibrate	TRUE	FALSE	2020-06-19	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
callr	callr	3.5.1	3.5.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/callr	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/callr	FALSE	FALSE	2020-10-13	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
car	car	3.0.10	3.0-10	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/car	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/car	FALSE	FALSE	2020-09-29	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
carData	carData	3.0.4	3.0-4	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/carData	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/carData	FALSE	FALSE	2020-05-22	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
cellranger	cellranger	1.1.0	1.1.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/cellranger	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/cellranger	FALSE	FALSE	2016-07-27	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
checkmate	checkmate	2.0.0	2.0.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/checkmate	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/checkmate	FALSE	FALSE	2020-02-06	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
circlize	circlize	0.4.12	0.4.12	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/circlize	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/circlize	TRUE	FALSE	2021-01-08	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
Ckmeans.1d.dp	Ckmeans.1d.dp	4.3.3	4.3.3	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/Ckmeans.1d.dp	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/Ckmeans.1d.dp	TRUE	FALSE	2020-07-22	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
class	class	7.3.18	7.3-18	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/class	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/class	FALSE	FALSE	2021-01-24	CRAN (R 4.0.4)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
classInt	classInt	0.4.3	0.4-3	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/classInt	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/classInt	FALSE	FALSE	2020-04-07	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
cli	cli	2.3.1	2.3.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/cli	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/cli	FALSE	FALSE	2021-02-23	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
clue	clue	0.3.58	0.3-58	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/clue	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/clue	FALSE	FALSE	2020-12-03	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
cluster	cluster	2.1.1	2.1.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/cluster	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/cluster	TRUE	FALSE	2021-02-14	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
codetools	codetools	0.2.18	0.2-18	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/codetools	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/codetools	FALSE	FALSE	2020-11-04	CRAN (R 4.0.4)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
colorspace	colorspace	2.0.0	2.0-0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/colorspace	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/colorspace	FALSE	FALSE	2020-11-11	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
ComplexHeatmap	ComplexHeatmap	2.6.2	2.6.2	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/ComplexHeatmap	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/ComplexHeatmap	TRUE	FALSE	2020-11-12	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
corrplot	corrplot	0.84	0.84	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/corrplot	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/corrplot	TRUE	FALSE	2017-10-16	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
cowplot	cowplot	1.1.1	1.1.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/cowplot	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/cowplot	TRUE	FALSE	2020-12-30	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
crayon	crayon	1.4.1	1.4.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/crayon	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/crayon	FALSE	FALSE	2021-02-08	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
crosstalk	crosstalk	1.1.1	1.1.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/crosstalk	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/crosstalk	FALSE	FALSE	2021-01-12	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
curl	curl	4.3	4.3	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/curl	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/curl	FALSE	FALSE	2019-12-02	CRAN (R 4.0.1)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
data.table	data.table	1.14.0	1.14.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/data.table	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/data.table	TRUE	FALSE	2021-02-21	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
DBI	DBI	1.1.1	1.1.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/DBI	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/DBI	FALSE	FALSE	2021-01-15	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
dbplyr	dbplyr	2.1.0	2.1.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/dbplyr	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/dbplyr	TRUE	FALSE	2021-02-03	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
DelayedArray	DelayedArray	0.16.2	0.16.2	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/DelayedArray	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/DelayedArray	FALSE	FALSE	2021-02-26	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
DelayedMatrixStats	DelayedMatrixStats	1.12.3	1.12.3	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/DelayedMatrixStats	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/DelayedMatrixStats	FALSE	FALSE	2021-02-03	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
dendextend	dendextend	1.14.0	1.14.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/dendextend	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/dendextend	TRUE	FALSE	2020-08-26	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
desc	desc	1.3.0	1.3.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/desc	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/desc	FALSE	FALSE	2021-03-05	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
DESeq2	DESeq2	1.30.1	1.30.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/DESeq2	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/DESeq2	TRUE	FALSE	2021-02-19	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
devtools	devtools	2.3.2	2.3.2	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/devtools	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/devtools	FALSE	FALSE	2020-09-18	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
dichromat	dichromat	2.0.0	2.0-0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/dichromat	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/dichromat	FALSE	FALSE	2013-01-24	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
digest	digest	0.6.27	0.6.27	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/digest	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/digest	FALSE	FALSE	2020-10-24	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
DMRcate	DMRcate	2.4.1	2.4.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/DMRcate	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/DMRcate	TRUE	FALSE	2021-01-14	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
DMRcatedata	DMRcatedata	2.8.2	2.8.2	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/DMRcatedata	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/DMRcatedata	TRUE	FALSE	2021-02-11	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
doRNG	doRNG	1.8.2	1.8.2	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/doRNG	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/doRNG	FALSE	FALSE	2020-01-27	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
dplyr	dplyr	1.0.5	1.0.5	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/dplyr	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/dplyr	TRUE	FALSE	2021-03-05	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
DSS	DSS	2.38.0	2.38.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/DSS	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/DSS	FALSE	FALSE	2020-10-27	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
e1071	e1071	1.7.6	1.7-6	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/e1071	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/e1071	FALSE	FALSE	2021-03-18	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
edgeR	edgeR	3.32.1	3.32.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/edgeR	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/edgeR	FALSE	FALSE	2021-01-14	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
ellipsis	ellipsis	0.3.1	0.3.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/ellipsis	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/ellipsis	FALSE	FALSE	2020-05-15	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
ensembldb	ensembldb	2.14.0	2.14.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/ensembldb	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/ensembldb	FALSE	FALSE	2020-10-27	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
evaluate	evaluate	0.14	0.14	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/evaluate	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/evaluate	FALSE	FALSE	2019-05-28	CRAN (R 4.0.1)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
ExperimentHub	ExperimentHub	1.16.0	1.16.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/ExperimentHub	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/ExperimentHub	TRUE	FALSE	2020-10-27	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
factoextra	factoextra	1.0.7	1.0.7	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/factoextra	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/factoextra	TRUE	FALSE	2020-04-01	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
fansi	fansi	0.4.2	0.4.2	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/fansi	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/fansi	FALSE	FALSE	2021-01-15	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
farver	farver	2.1.0	2.1.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/farver	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/farver	FALSE	FALSE	2021-02-28	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
fastmap	fastmap	1.1.0	1.1.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/fastmap	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/fastmap	FALSE	FALSE	2021-01-25	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
ff	ff	4.0.4	4.0.4	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/ff	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/ff	FALSE	FALSE	2020-10-13	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
forcats	forcats	0.5.1	0.5.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/forcats	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/forcats	TRUE	FALSE	2021-01-27	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
foreach	foreach	1.5.1	1.5.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/foreach	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/foreach	TRUE	FALSE	2020-10-15	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
foreign	foreign	0.8.81	0.8-81	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/foreign	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/foreign	FALSE	FALSE	2020-12-22	CRAN (R 4.0.4)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
Formula	Formula	1.2.4	1.2-4	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/Formula	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/Formula	TRUE	FALSE	2020-10-16	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
fs	fs	1.5.0	1.5.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/fs	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/fs	FALSE	FALSE	2020-07-31	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
genefilter	genefilter	1.72.1	1.72.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/genefilter	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/genefilter	FALSE	FALSE	2021-01-21	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
geneplotter	geneplotter	1.68.0	1.68.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/geneplotter	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/geneplotter	FALSE	FALSE	2020-10-27	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
generics	generics	0.1.0	0.1.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/generics	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/generics	FALSE	FALSE	2020-10-31	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
GenomeInfoDb	GenomeInfoDb	1.26.4	1.26.4	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/GenomeInfoDb	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/GenomeInfoDb	TRUE	FALSE	2021-03-10	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
GenomeInfoDbData	GenomeInfoDbData	1.2.4	1.2.4	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/GenomeInfoDbData	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/GenomeInfoDbData	FALSE	FALSE	2021-02-16	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
GenomicAlignments	GenomicAlignments	1.26.0	1.26.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/GenomicAlignments	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/GenomicAlignments	FALSE	FALSE	2020-10-27	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
GenomicFeatures	GenomicFeatures	1.42.2	1.42.2	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/GenomicFeatures	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/GenomicFeatures	TRUE	FALSE	2021-03-12	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
GenomicRanges	GenomicRanges	1.42.0	1.42.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/GenomicRanges	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/GenomicRanges	TRUE	FALSE	2020-10-27	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
GEOquery	GEOquery	2.58.0	2.58.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/GEOquery	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/GEOquery	FALSE	FALSE	2020-10-27	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
GetoptLong	GetoptLong	1.0.5	1.0.5	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/GetoptLong	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/GetoptLong	FALSE	FALSE	2020-12-15	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
ggplot2	ggplot2	3.3.3	3.3.3	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/ggplot2	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/ggplot2	TRUE	FALSE	2020-12-30	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
ggpubr	ggpubr	0.4.0	0.4.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/ggpubr	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/ggpubr	TRUE	FALSE	2020-06-27	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
ggrepel	ggrepel	0.9.1	0.9.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/ggrepel	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/ggrepel	TRUE	FALSE	2021-01-15	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
ggsignif	ggsignif	0.6.1	0.6.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/ggsignif	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/ggsignif	TRUE	FALSE	2021-02-23	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
ggthemes	ggthemes	4.2.4	4.2.4	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/ggthemes	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/ggthemes	FALSE	FALSE	2021-01-20	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
GlobalOptions	GlobalOptions	0.1.2	0.1.2	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/GlobalOptions	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/GlobalOptions	FALSE	FALSE	2020-06-10	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
glue	glue	1.4.2	1.4.2	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/glue	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/glue	FALSE	FALSE	2020-08-27	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
GO.db	GO.db	3.12.1	3.12.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/GO.db	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/GO.db	FALSE	FALSE	2021-02-23	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
gridExtra	gridExtra	2.3	2.3	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/gridExtra	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/gridExtra	TRUE	FALSE	2017-09-09	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
gtable	gtable	0.3.0	0.3.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/gtable	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/gtable	FALSE	FALSE	2019-03-25	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
gtools	gtools	3.8.2	3.8.2	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/gtools	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/gtools	TRUE	FALSE	2020-03-31	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
Gviz	Gviz	1.34.1	1.34.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/Gviz	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/Gviz	FALSE	FALSE	2021-03-15	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
Haplin	Haplin	7.2.3	7.2.3	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/Haplin	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/Haplin	TRUE	FALSE	2020-09-07	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
haven	haven	2.3.1	2.3.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/haven	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/haven	FALSE	FALSE	2020-06-01	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
HDF5Array	HDF5Array	1.18.1	1.18.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/HDF5Array	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/HDF5Array	FALSE	FALSE	2021-02-04	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
highr	highr	0.8	0.8	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/highr	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/highr	FALSE	FALSE	2019-03-20	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
Hmisc	Hmisc	4.5.0	4.5-0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/Hmisc	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/Hmisc	TRUE	FALSE	2021-02-28	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
hms	hms	1.0.0	1.0.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/hms	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/hms	FALSE	FALSE	2021-01-13	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
htmlTable	htmlTable	2.1.0	2.1.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/htmlTable	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/htmlTable	FALSE	FALSE	2020-09-16	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
htmltools	htmltools	0.5.1.1	0.5.1.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/htmltools	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/htmltools	FALSE	FALSE	2021-01-22	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
htmlwidgets	htmlwidgets	1.5.3	1.5.3	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/htmlwidgets	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/htmlwidgets	FALSE	FALSE	2020-12-10	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
httpuv	httpuv	1.5.5	1.5.5	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/httpuv	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/httpuv	FALSE	FALSE	2021-01-13	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
httr	httr	1.4.2	1.4.2	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/httr	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/httr	FALSE	FALSE	2020-07-20	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
IlluminaHumanMethylation450kanno.ilmn12.hg19	IlluminaHumanMethylation450kanno.ilmn12.hg19	0.6.0	0.6.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/IlluminaHumanMethylation450kanno.ilmn12.hg19	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/IlluminaHumanMethylation450kanno.ilmn12.hg19	TRUE	FALSE	2021-02-23	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
IlluminaHumanMethylation450kmanifest	IlluminaHumanMethylation450kmanifest	0.4.0	0.4.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/IlluminaHumanMethylation450kmanifest	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/IlluminaHumanMethylation450kmanifest	TRUE	FALSE	2021-02-22	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
IlluminaHumanMethylationEPICanno.ilm10b4.hg19	IlluminaHumanMethylationEPICanno.ilm10b4.hg19	0.6.0	0.6.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/IlluminaHumanMethylationEPICanno.ilm10b4.hg19	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/IlluminaHumanMethylationEPICanno.ilm10b4.hg19	TRUE	FALSE	2021-02-22	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
IlluminaHumanMethylationEPICanno.ilm10b5.hg38	IlluminaHumanMethylationEPICanno.ilm10b5.hg38	0.0.1	0.0.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/IlluminaHumanMethylationEPICanno.ilm10b5.hg38	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/IlluminaHumanMethylationEPICanno.ilm10b5.hg38	TRUE	FALSE	2021-02-22	Github (achilleasNP/IlluminaHumanMethylationEPICanno.ilm10b5.hg38@3db0691)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
IlluminaHumanMethylationEPICmanifest	IlluminaHumanMethylationEPICmanifest	1.0.0	1.0.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/IlluminaHumanMethylationEPICmanifest	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/IlluminaHumanMethylationEPICmanifest	TRUE	FALSE	2021-02-22	Github (achilleasNP/IlluminaHumanMethylationEPICmanifest@a9ffbad)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
illuminaio	illuminaio	0.32.0	0.32.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/illuminaio	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/illuminaio	FALSE	FALSE	2020-10-27	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
interactiveDisplayBase	interactiveDisplayBase	1.28.0	1.28.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/interactiveDisplayBase	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/interactiveDisplayBase	FALSE	FALSE	2020-10-27	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
IRanges	IRanges	2.24.1	2.24.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/IRanges	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/IRanges	TRUE	FALSE	2020-12-12	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
iterators	iterators	1.0.13	1.0.13	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/iterators	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/iterators	TRUE	FALSE	2020-10-15	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
jpeg	jpeg	0.1.8.1	0.1-8.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/jpeg	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/jpeg	FALSE	FALSE	2019-10-24	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
jquerylib	jquerylib	0.1.3	0.1.3	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/jquerylib	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/jquerylib	FALSE	FALSE	2020-12-17	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
jsonlite	jsonlite	1.7.2	1.7.2	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/jsonlite	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/jsonlite	FALSE	FALSE	2020-12-09	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
kableExtra	kableExtra	1.3.4	1.3.4	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/kableExtra	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/kableExtra	TRUE	FALSE	2021-02-20	CRAN (R 4.0.4)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
KernSmooth	KernSmooth	2.23.18	2.23-18	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/KernSmooth	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/KernSmooth	FALSE	FALSE	2020-10-29	CRAN (R 4.0.4)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
km.ci	km.ci	0.5.2	0.5-2	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/km.ci	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/km.ci	FALSE	FALSE	2009-08-30	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
KMsurv	KMsurv	0.1.5	0.1-5	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/KMsurv	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/KMsurv	FALSE	FALSE	2012-12-03	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
knitr	knitr	1.31	1.31	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/knitr	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/knitr	TRUE	FALSE	2021-01-27	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
labeling	labeling	0.4.2	0.4.2	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/labeling	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/labeling	FALSE	FALSE	2020-10-20	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
later	later	1.1.0.1	1.1.0.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/later	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/later	FALSE	FALSE	2020-06-05	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
lattice	lattice	0.20.41	0.20-41	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/lattice	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/lattice	TRUE	FALSE	2020-04-02	CRAN (R 4.0.4)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
latticeExtra	latticeExtra	0.6.29	0.6-29	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/latticeExtra	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/latticeExtra	FALSE	FALSE	2019-12-19	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
lazyeval	lazyeval	0.2.2	0.2.2	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/lazyeval	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/lazyeval	FALSE	FALSE	2019-03-15	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
lifecycle	lifecycle	1.0.0	1.0.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/lifecycle	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/lifecycle	FALSE	FALSE	2021-02-15	CRAN (R 4.0.4)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
limma	limma	3.46.0	3.46.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/limma	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/limma	TRUE	FALSE	2020-10-27	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
locfit	locfit	1.5.9.4	1.5-9.4	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/locfit	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/locfit	TRUE	FALSE	2020-03-25	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
lubridate	lubridate	1.7.10	1.7.10	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/lubridate	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/lubridate	FALSE	FALSE	2021-02-26	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
magrittr	magrittr	2.0.1	2.0.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/magrittr	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/magrittr	TRUE	FALSE	2020-11-17	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
maps	maps	3.3.0	3.3.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/maps	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/maps	TRUE	FALSE	2018-04-03	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
MASS	MASS	7.3.53.1	7.3-53.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/MASS	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/MASS	TRUE	FALSE	2021-02-12	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
Matrix	Matrix	1.3.2	1.3-2	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/Matrix	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/Matrix	FALSE	FALSE	2021-01-06	CRAN (R 4.0.4)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
MatrixGenerics	MatrixGenerics	1.2.1	1.2.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/MatrixGenerics	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/MatrixGenerics	TRUE	FALSE	2021-01-30	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
matrixStats	matrixStats	0.58.0	0.58.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/matrixStats	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/matrixStats	TRUE	FALSE	2021-01-29	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
mclust	mclust	5.4.7	5.4.7	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/mclust	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/mclust	FALSE	FALSE	2020-11-20	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
memoise	memoise	2.0.0	2.0.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/memoise	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/memoise	FALSE	FALSE	2021-01-26	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
MethylToSNP	MethylToSNP	0.99.0	0.99.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/MethylToSNP	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/MethylToSNP	TRUE	FALSE	2021-02-22	Github (elnitskilab/MethylToSNP@92a4282)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
mgcv	mgcv	1.8.34	1.8-34	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/mgcv	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/mgcv	FALSE	FALSE	2021-02-16	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
mime	mime	0.10	0.10	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/mime	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/mime	FALSE	FALSE	2021-02-13	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
minfi	minfi	1.36.0	1.36.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/minfi	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/minfi	TRUE	FALSE	2020-10-27	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
missMethyl	missMethyl	1.24.0	1.24.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/missMethyl	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/missMethyl	TRUE	FALSE	2020-10-27	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
modelr	modelr	0.1.8	0.1.8	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/modelr	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/modelr	FALSE	FALSE	2020-05-19	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
multtest	multtest	2.46.0	2.46.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/multtest	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/multtest	FALSE	FALSE	2020-10-27	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
munsell	munsell	0.5.0	0.5.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/munsell	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/munsell	FALSE	FALSE	2018-06-12	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
nlme	nlme	3.1.152	3.1-152	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/nlme	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/nlme	FALSE	FALSE	2021-02-04	CRAN (R 4.0.4)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
nnet	nnet	7.3.15	7.3-15	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/nnet	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/nnet	FALSE	FALSE	2021-01-24	CRAN (R 4.0.4)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
nor1mix	nor1mix	1.3.0	1.3-0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/nor1mix	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/nor1mix	FALSE	FALSE	2019-06-13	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
openssl	openssl	1.4.3	1.4.3	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/openssl	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/openssl	FALSE	FALSE	2020-09-18	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
openxlsx	openxlsx	4.2.3	4.2.3	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/openxlsx	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/openxlsx	TRUE	FALSE	2020-10-27	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
org.Hs.eg.db	org.Hs.eg.db	3.12.0	3.12.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/org.Hs.eg.db	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/org.Hs.eg.db	FALSE	FALSE	2021-02-23	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
pacman	pacman	0.5.1	0.5.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/pacman	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/pacman	TRUE	FALSE	2019-03-11	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
permute	permute	0.9.5	0.9-5	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/permute	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/permute	FALSE	FALSE	2019-03-12	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
pheatmap	pheatmap	1.0.12	1.0.12	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/pheatmap	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/pheatmap	TRUE	FALSE	2019-01-04	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
pillar	pillar	1.5.1	1.5.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/pillar	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/pillar	FALSE	FALSE	2021-03-05	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
pkgbuild	pkgbuild	1.2.0	1.2.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/pkgbuild	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/pkgbuild	FALSE	FALSE	2020-12-15	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
pkgconfig	pkgconfig	2.0.3	2.0.3	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/pkgconfig	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/pkgconfig	FALSE	FALSE	2019-09-22	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
pkgload	pkgload	1.2.0	1.2.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/pkgload	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/pkgload	FALSE	FALSE	2021-02-23	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
plotly	plotly	4.9.3	4.9.3	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/plotly	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/plotly	TRUE	FALSE	2021-01-10	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
plyr	plyr	1.8.6	1.8.6	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/plyr	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/plyr	TRUE	FALSE	2020-03-03	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
png	png	0.1.7	0.1-7	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/png	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/png	FALSE	FALSE	2013-12-03	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
preprocessCore	preprocessCore	1.52.1	1.52.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/preprocessCore	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/preprocessCore	FALSE	FALSE	2021-01-08	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
prettyunits	prettyunits	1.1.1	1.1.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/prettyunits	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/prettyunits	FALSE	FALSE	2020-01-24	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
processx	processx	3.4.5	3.4.5	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/processx	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/processx	FALSE	FALSE	2020-11-30	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
progress	progress	1.2.2	1.2.2	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/progress	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/progress	FALSE	FALSE	2019-05-16	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
promises	promises	1.2.0.1	1.2.0.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/promises	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/promises	FALSE	FALSE	2021-02-11	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
ProtGenerics	ProtGenerics	1.22.0	1.22.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/ProtGenerics	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/ProtGenerics	FALSE	FALSE	2020-10-27	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
proxy	proxy	0.4.25	0.4-25	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/proxy	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/proxy	FALSE	FALSE	2021-03-05	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
ps	ps	1.6.0	1.6.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/ps	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/ps	FALSE	FALSE	2021-02-28	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
purrr	purrr	0.3.4	0.3.4	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/purrr	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/purrr	TRUE	FALSE	2020-04-17	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
quadprog	quadprog	1.5.8	1.5-8	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/quadprog	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/quadprog	FALSE	FALSE	2019-11-20	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
R.methodsS3	R.methodsS3	1.8.1	1.8.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/R.methodsS3	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/R.methodsS3	FALSE	FALSE	2020-08-26	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
R.oo	R.oo	1.24.0	1.24.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/R.oo	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/R.oo	FALSE	FALSE	2020-08-26	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
R.utils	R.utils	2.10.1	2.10.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/R.utils	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/R.utils	FALSE	FALSE	2020-08-26	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
R6	R6	2.5.0	2.5.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/R6	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/R6	FALSE	FALSE	2020-10-28	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
rappdirs	rappdirs	0.3.3	0.3.3	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rappdirs	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rappdirs	FALSE	FALSE	2021-01-31	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
rbibutils	rbibutils	2.0	2.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rbibutils	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rbibutils	FALSE	FALSE	2020-11-18	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
RColorBrewer	RColorBrewer	1.1.2	1.1-2	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RColorBrewer	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RColorBrewer	TRUE	FALSE	2014-12-07	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
Rcpp	Rcpp	1.0.6	1.0.6	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/Rcpp	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/Rcpp	FALSE	FALSE	2021-01-15	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
RCurl	RCurl	1.98.1.3	1.98-1.3	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RCurl	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RCurl	FALSE	FALSE	2021-03-16	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
Rdpack	Rdpack	2.1.1	2.1.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/Rdpack	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/Rdpack	FALSE	FALSE	2021-02-23	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
readr	readr	1.4.0	1.4.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/readr	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/readr	TRUE	FALSE	2020-10-05	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
readxl	readxl	1.3.1	1.3.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/readxl	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/readxl	TRUE	FALSE	2019-03-13	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
remotes	remotes	2.2.0	2.2.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/remotes	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/remotes	FALSE	FALSE	2020-07-21	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
reprex	reprex	1.0.0	1.0.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/reprex	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/reprex	FALSE	FALSE	2021-01-27	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
reshape	reshape	0.8.8	0.8.8	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/reshape	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/reshape	FALSE	FALSE	2018-10-23	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
reshape2	reshape2	1.4.4	1.4.4	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/reshape2	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/reshape2	TRUE	FALSE	2020-04-09	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
rhdf5	rhdf5	2.34.0	2.34.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rhdf5	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rhdf5	FALSE	FALSE	2020-10-27	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
rhdf5filters	rhdf5filters	1.2.0	1.2.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rhdf5filters	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rhdf5filters	FALSE	FALSE	2020-10-27	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
Rhdf5lib	Rhdf5lib	1.12.1	1.12.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/Rhdf5lib	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/Rhdf5lib	FALSE	FALSE	2021-01-26	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
rio	rio	0.5.26	0.5.26	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rio	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rio	FALSE	FALSE	2021-03-01	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
rjson	rjson	0.2.20	0.2.20	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rjson	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rjson	FALSE	FALSE	2018-06-08	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
rlang	rlang	0.4.10	0.4.10	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rlang	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rlang	FALSE	FALSE	2020-12-30	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
rmarkdown	rmarkdown	2.7	2.7	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rmarkdown	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rmarkdown	FALSE	FALSE	2021-02-19	CRAN (R 4.0.4)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
rnaturalearth	rnaturalearth	0.1.0	0.1.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rnaturalearth	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rnaturalearth	TRUE	FALSE	2017-03-21	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
rnaturalearthdata	rnaturalearthdata	0.1.0	0.1.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rnaturalearthdata	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rnaturalearthdata	TRUE	FALSE	2017-02-21	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
rngtools	rngtools	1.5	1.5	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rngtools	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rngtools	FALSE	FALSE	2020-01-23	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
rpart	rpart	4.1.15	4.1-15	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rpart	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rpart	FALSE	FALSE	2019-04-12	CRAN (R 4.0.4)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
rprojroot	rprojroot	2.0.2	2.0.2	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rprojroot	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rprojroot	FALSE	FALSE	2020-11-15	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
Rsamtools	Rsamtools	2.6.0	2.6.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/Rsamtools	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/Rsamtools	FALSE	FALSE	2020-10-27	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
RSQLite	RSQLite	2.2.4	2.2.4	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RSQLite	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RSQLite	FALSE	FALSE	2021-03-12	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
rstatix	rstatix	0.7.0	0.7.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rstatix	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rstatix	FALSE	FALSE	2021-02-13	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
rstudioapi	rstudioapi	0.13	0.13	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rstudioapi	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rstudioapi	FALSE	FALSE	2020-11-12	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
RTCGA	RTCGA	1.20.0	1.20.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RTCGA	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RTCGA	TRUE	FALSE	2020-10-27	Bioconductor (R 4.0.3)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
RTCGA.clinical	RTCGA.clinical	20151101.20.0	20151101.20.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RTCGA.clinical	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RTCGA.clinical	TRUE	FALSE	2020-10-29	Bioconductor (R 4.0.4)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
rtracklayer	rtracklayer	1.50.0	1.50.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rtracklayer	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rtracklayer	FALSE	FALSE	2020-10-27	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
rvest	rvest	1.0.0	1.0.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rvest	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rvest	FALSE	FALSE	2021-03-09	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
S4Vectors	S4Vectors	0.28.1	0.28.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/S4Vectors	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/S4Vectors	TRUE	FALSE	2020-12-09	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
sass	sass	0.3.1	0.3.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/sass	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/sass	FALSE	FALSE	2021-01-24	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
scales	scales	1.1.1	1.1.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/scales	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/scales	FALSE	FALSE	2020-05-11	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
scrime	scrime	1.3.5	1.3.5	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/scrime	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/scrime	FALSE	FALSE	2018-12-01	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
sessioninfo	sessioninfo	1.1.1	1.1.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/sessioninfo	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/sessioninfo	FALSE	FALSE	2018-11-05	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
sf	sf	0.9.7	0.9-7	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/sf	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/sf	TRUE	FALSE	2021-01-06	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
shape	shape	1.4.5	1.4.5	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/shape	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/shape	FALSE	FALSE	2020-09-13	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
shiny	shiny	1.6.0	1.6.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/shiny	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/shiny	TRUE	FALSE	2021-01-25	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
shinyMethyl	shinyMethyl	1.26.0	1.26.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/shinyMethyl	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/shinyMethyl	TRUE	FALSE	2020-10-27	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
siggenes	siggenes	1.64.0	1.64.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/siggenes	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/siggenes	FALSE	FALSE	2020-10-27	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
sp	sp	1.4.5	1.4-5	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/sp	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/sp	FALSE	FALSE	2021-01-10	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
sparseMatrixStats	sparseMatrixStats	1.2.1	1.2.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/sparseMatrixStats	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/sparseMatrixStats	FALSE	FALSE	2021-02-02	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
statmod	statmod	1.4.35	1.4.35	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/statmod	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/statmod	FALSE	FALSE	2020-10-19	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
stringi	stringi	1.5.3	1.5.3	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/stringi	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/stringi	FALSE	FALSE	2020-09-09	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
stringr	stringr	1.4.0	1.4.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/stringr	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/stringr	TRUE	FALSE	2019-02-10	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
SummarizedExperiment	SummarizedExperiment	1.20.0	1.20.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/SummarizedExperiment	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/SummarizedExperiment	TRUE	FALSE	2020-10-27	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
survival	survival	3.2.10	3.2-10	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/survival	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/survival	TRUE	FALSE	2021-03-16	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
survminer	survminer	0.4.9	0.4.9	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/survminer	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/survminer	TRUE	FALSE	2021-03-09	CRAN (R 4.0.4)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
survMisc	survMisc	0.5.5	0.5.5	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/survMisc	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/survMisc	FALSE	FALSE	2018-07-05	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
svglite	svglite	2.0.0	2.0.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/svglite	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/svglite	FALSE	FALSE	2021-02-20	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
systemfonts	systemfonts	1.0.1	1.0.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/systemfonts	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/systemfonts	FALSE	FALSE	2021-02-09	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
testthat	testthat	3.0.2	3.0.2	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/testthat	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/testthat	FALSE	FALSE	2021-02-14	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
tibble	tibble	3.1.0	3.1.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/tibble	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/tibble	TRUE	FALSE	2021-02-25	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
tidyr	tidyr	1.1.3	1.1.3	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/tidyr	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/tidyr	TRUE	FALSE	2021-03-03	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
tidyselect	tidyselect	1.1.0	1.1.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/tidyselect	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/tidyselect	FALSE	FALSE	2020-05-11	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
tidyverse	tidyverse	1.3.0	1.3.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/tidyverse	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/tidyverse	TRUE	FALSE	2019-11-21	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
units	units	0.7.1	0.7-1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/units	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/units	FALSE	FALSE	2021-03-16	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
usethis	usethis	2.0.1	2.0.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/usethis	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/usethis	FALSE	FALSE	2021-02-10	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
utf8	utf8	1.2.1	1.2.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/utf8	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/utf8	FALSE	FALSE	2021-03-12	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
VariantAnnotation	VariantAnnotation	1.36.0	1.36.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/VariantAnnotation	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/VariantAnnotation	FALSE	FALSE	2020-10-28	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
vctrs	vctrs	0.3.6	0.3.6	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/vctrs	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/vctrs	FALSE	FALSE	2020-12-17	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
viridis	viridis	0.5.1	0.5.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/viridis	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/viridis	FALSE	FALSE	2018-03-29	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
viridisLite	viridisLite	0.3.0	0.3.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/viridisLite	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/viridisLite	FALSE	FALSE	2018-02-01	CRAN (R 4.0.1)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
webshot	webshot	0.5.2	0.5.2	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/webshot	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/webshot	FALSE	FALSE	2019-11-22	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
wesanderson	wesanderson	0.3.6	0.3.6	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/wesanderson	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/wesanderson	TRUE	FALSE	2018-04-20	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
withr	withr	2.4.1	2.4.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/withr	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/withr	FALSE	FALSE	2021-01-26	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
xfun	xfun	0.22	0.22	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/xfun	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/xfun	FALSE	FALSE	2021-03-11	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
XML	XML	3.99.0.6	3.99-0.6	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/XML	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/XML	FALSE	FALSE	2021-03-16	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
xml2	xml2	1.3.2	1.3.2	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/xml2	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/xml2	FALSE	FALSE	2020-04-23	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
xtable	xtable	1.8.4	1.8-4	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/xtable	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/xtable	FALSE	FALSE	2019-04-21	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
XVector	XVector	0.30.0	0.30.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/XVector	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/XVector	TRUE	FALSE	2020-10-28	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
yaml	yaml	2.2.1	2.2.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/yaml	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/yaml	FALSE	FALSE	2020-02-01	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
zip	zip	2.1.1	2.1.1	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/zip	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/zip	FALSE	FALSE	2020-08-27	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
zlibbioc	zlibbioc	1.36.0	1.36.0	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/zlibbioc	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/zlibbioc	FALSE	FALSE	2020-10-28	Bioconductor		/Library/Frameworks/R.framework/Versions/4.0/Resources/library
zoo	zoo	1.8.9	1.8-9	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/zoo	/Library/Frameworks/R.framework/Versions/4.0/Resources/library/zoo	FALSE	FALSE	2021-03-09	CRAN (R 4.0.2)		/Library/Frameworks/R.framework/Versions/4.0/Resources/library

n.d.

Pepin, Mark E., Chae-Myeong Ha, David K. Crossman, Silvio H. Litovsky, Sooryanarayana Varambally, Joseph P. Barchue, Salpy V. Pamboukian, et al. 2018. “Genome-Wide DNA Methylation Encodes Cardiac Transcriptional Reprogramming in Human Ischemic Heart Failure.” Laboratory Investigation 99 (3): 371–86. https://doi.org/10.1038/s41374-018-0104-x.