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The failing heart undergoes profound metabolic changes due to alterations in cardiac gene expression, reactivating glycolytic genes and suppressing oxidative metabolic genes. In this study, we discover that alterations in cardiac DNA methylation encode this fetal-like metabolic preference. We also identify epigenetic interference of NRF1 via hyp…

doi.org/10.1152/ajpheart.00016.2019
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title author date output header-includes mainfont fontsize always_allow_html
WGBS Analysis Pipeline
Mark E. Pepin
11/10/2018
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\usepackage{booktabs}
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Author: Mark E. Pepin, MS Biomedical Engineering | MD/PhD Trainee
Contact: [email protected]
Institution: University of Alabama at Birmingham
Location: 542 Biomedical Research Building 2, Birmingham, AL 35294

Genome Assembly and Alignment

The first task is to align the bisulfite reduced and sequenced reads to a genome assembly. To accomplish this, I prepared the genome assembly based on Gencode annotation (gencode.v28.annotation.gtf) and sequence (GRCh38.p12.genome.fa). For whole-genome bisulfite sequencing via the Bismark (v0.20.0) aligner and genome preparation, a CT- and GA-converted assemblies are created.

Genome Assembly

./bismark_genome_preparation --path_to_bowtie ../bowtie2-2.3.4.2-linux-x86_64 -- verbose ../../Input/Genome/GRCh38.p12.genome.fa

Adapter Trimming

Once the genome assembly was created, adapter sequences were trimmed and sequencing quality assessed via trim_galore and FastQC, respectively.

module load SAMtools/1.6-intel-2017a module load Bowtie2/2.3.3-intel-2017a module load Trim_Galore/0.4.4-foss-2016b module load FastQC/0.11.7-Java-1.8.0_74

trim_galore -o $INPUT_DIR/fastq_trimmed/ --paired --rrbs --non_directional --length 20 --fastqc $INPUT_DIR/fastq/${VAR}_1.txt.gz $INPUT_DIR/fastq/${VAR}_2.txt.gz

Read Alignment

We then aligned all 34 paired-end .fastq files to the genome assemblies using the following command:

$BISMARK/bismark \ --bowtie2 --bam $GENOME_DIR \ -1 $INPUT_DIR/fastq_trimmed/${VAR}_1.txt.gz_val_1.fq.gz -2 $INPUT_DIR/fastq_trimmed/${VAR}_2.txt.gz_val_2.fq.gz \ --output_dir $RESULTS_DIR/WGBS

Deduplication

Once aligned, we need to "deduplicate" the aligned .bam files to reduce PCR bias.

$BISMARK/deduplicate_bismark \ --output_dir $RESULTS_DIR/WGBS/deduplicate_bismark \ --bam -p \ $RESULTS_DIR/WGBS/${VAR}_1.txt.gz_val_1_bismark_bt2_pe.bam

Methylation Extraction

Once finished, the CpG methylation was extracted as both bedgraph file (for UCSC genome browser) and bed file, which was then used to identify differentially-methylated cytosines (DMCs) and differentially-methylated regions (DMRs).

$BISMARK/bismark_methylation_extractor \ -p --no_overlap --report --bedGraph --gzip \ $RESULTS_DIR/WGBS/deduplicate_bismark/${VAR}_1.txt.gz_val_1_bismark_bt2_pe.deduplicated.bam

The "bismark.cov" files that resulted from this were then read into R () and combined into a single "object" for differential methylation analysis

Differential Methylation Analysis

Combining sample methylation

#Conditions to be used in differential methylation analysis (FILL OUT)
library(openxlsx)
RESPONSE=c("CON","NR")
TIMING=c("CON","Pre")

ANALYSIS="HF.v.NF"
### "2" is Pre-LVAD, "3" is Post-LVAD, "1" is CON
library(methylKit)
file.list <- list.files(path = "../2_Input/2_Methyl/Aligned", pattern = "*_1.txt.gz_val_1_bismark_bt2_pe.deduplicated.bismark.cov", full.names = TRUE, all.files = TRUE)
#Generate Column names (remove the extra nonsense from the path names)
colnames <- gsub( "*_1.txt.gz_val_1_bismark_bt2_pe.deduplicated.bismark.cov", "", file.list)
colnames <- gsub( "[.][.]/2_Input/2_Methyl/Aligned/", "", colnames)
colnames <- gsub("\\_.*", "", colnames)
sample_id<-as.list(colnames)
#Import the Index file
Index.raw<-read.xlsx("../2_Input/_Patient/Index_no.outliers.xlsx", sheet = "Index_no.outliers")
Index.raw$Timing<-factor(Index.raw$Timing, levels = c("CON", "Pre", "Post"))
Index.raw$Response<-factor(Index.raw$Response, levels=c("CON", "NR", "R"))
Index.raw$Etiology<-factor(Index.raw$Etiology, levels=c("CON", "NICM", "ICM"))
Index.raw$Pairing<-as.character(Index.raw$Pairing)

## Sort the index according to the .bed file ordering (as imported). This is important for correct annotation of samples.
Index_sorted<-subset(Index.raw, DNA.Meth_ID %in% colnames)
Index_sorted$DNA.meth.ID<-factor(Index_sorted$DNA.Meth_ID, levels = colnames)
Index_sorted<-Index_sorted[order(Index_sorted$DNA.Meth_ID),]
#Created an index based on the desired sample criteria (Phenotype and Timing)
Index_filtered<-subset(Index_sorted, Response %in% RESPONSE & Timing %in% TIMING)
##Change "Pre/Post" variable to a factor of "0" or "1" (needed for differential methylation)
Index_sorted$Timing<-factor(Index_sorted$Timing, levels = c("CON", "Pre", "Post"))
Index_sorted$Timing<-as.integer(Index_sorted$Timing)

Index_sorted$Response<-factor(Index_sorted$Response, levels = c("CON", "NR", "R"))
Index_sorted$Response<-as.integer(Index_sorted$Response)
# Index_sorted<-Index_sorted[!is.na(Index_sorted$Timing),]
##Create a methlRawlistDB
file.list<-as.list(file.list)
myobj<-methRead(file.list, sample.id = sample_id, assembly = "hg38", treatment = Index_sorted$Response, pipeline = "bismarkCoverage", header = FALSE, context = "CpG")
##Example of smaple statistics (can spot check these)
getMethylationStats(myobj[[3]], plot = F, both.strands = F)
## methylation statistics per base
## summary:
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   0.000   1.639  37.963  43.351  86.486 100.000 
## percentiles:
##         0%        10%        20%        30%        40%        50% 
##   0.000000   0.000000   0.000000   3.076923  10.000000  37.962963 
##        60%        70%        80%        90%        95%        99% 
##  64.705882  81.318681  90.243902  95.454545  98.245614 100.000000 
##      99.5%      99.9%       100% 
## 100.000000 100.000000 100.000000

#Subset the methylRawList to include only the sample_id's for the desired analysis
myobj_filtered<-reorganize(myobj, sample.ids = Index_filtered$DNA.Meth_ID, Index_filtered$Response)

Once the samples have been compiled, it is valuable to perform some basic visualizations and statistics to determine whether quality filtering is necessary. The distribution of methylation change is plotted as a histogram (typically bimodal at the extremes), as well as a distribution of the read coverage per based, again plotted as a histogram. For the latter plot, it is important to determine whether PCR duplication biases the read coverage. If so, a secondary peak would emerge on the right-most portion of the histogram. In the current analysis, coverage distribution exhibits a one-tailed distribution, suggesting that the "deduplication" step in the alignment effectively eliminated the PCR amplification bias in coverage.

library(graphics)
getMethylationStats(myobj_filtered[[2]], plot = T, both.strands = F)

getCoverageStats(myobj_filtered[[2]], plot = T, both.strands = F)

#Save these files in an output folder
ifelse(!dir.exists(file.path("../3_Output/", ANALYSIS)), dir.create(file.path("../3_Output/", ANALYSIS)), FALSE)
## [1] FALSE

pdf(file=paste0("../3_Output/", ANALYSIS, "/", ANALYSIS, "_Methylation.Stats.pdf"))
getMethylationStats(myobj_filtered[[2]], plot = T, both.strands = F)
dev.off()
## quartz_off_screen 
##                 2

pdf(file=paste0("../3_Output/", ANALYSIS, "/", ANALYSIS, "_Coverage.Stats.pdf"))
getCoverageStats(myobj_filtered[[2]], plot = T, both.strands = F)
dev.off()
## quartz_off_screen 
##                 2

Although most important in the context of correcting PCR-bias (duplication), filtering samples based on coverage also reduces false discovery based on low-coverage genomic regions. If PCR bias exists, an artificially high coverage would exist. Low coverage is also a concern due to low statistical power associated with low-coverage regions. Below, we discard bases with coverage below 10X, but also discard bases with coverage > 99.9th percentile.

#remove exceedingly high-coverage (risk of PCR bias) or low-coverage DMPs (low statistical power) 
filtered.myobj <- filterByCoverage(myobj_filtered, lo.count = 10, lo.perc = NULL, hi.count = NULL, hi.perc = 99.9)

##Visualizing Methylation

#destrand and unite the sample data
meth<-unite(filtered.myobj, destrand = FALSE) #When calculating DMRs, it is not helpful to "destrand"
clusterSamples(meth, dist = "correlation", method = "ward", plot = TRUE)

## 
## Call:
## hclust(d = d, method = HCLUST.METHODS[hclust.method])
## 
## Cluster method   : ward.D 
## Distance         : pearson 
## Number of objects: 10

PCASamples(meth, screeplot = TRUE)

PCASamples(meth)

#Create a folder in which to generate all documents/tables for this analysyis
ifelse(!dir.exists(file.path("../3_Output/", ANALYSIS)), dir.create(file.path("../3_Output/", ANALYSIS)), FALSE)
## [1] FALSE

#Create dendrogram and PCA plots
pdf(file=paste0("../3_Output/", ANALYSIS, "/", ANALYSIS, "_Clustering.pdf"))
clusterSamples(meth, dist = "correlation", method = "ward", plot = TRUE)
## 
## Call:
## hclust(d = d, method = HCLUST.METHODS[hclust.method])
## 
## Cluster method   : ward.D 
## Distance         : pearson 
## Number of objects: 10

PCASamples(meth, screeplot = TRUE)
PCASamples(meth)
dev.off()
## quartz_off_screen 
##                 2

Differentially-Methylated Cytosines (DMCs)

#Calculate the differential methylation (TAKES A LONG TIME!)
library(openxlsx)
myDiff<-calculateDiffMeth(meth, overdispersion = "MN", test = "Chisq", mc.cores = 7)
myDiff_md<-as(myDiff,"methylDiff")
myDiff_filtered<-dplyr::select(myDiff_md, chr, start, end, strand, meth.diff, pvalue, qvalue)

#Calculate percent methylation for each sample/site
Methylation<-as.data.frame(meth)
f = function(Cyt, cov, col_name) {
  require(lazyeval)
  require(dplyr)
    mutate_call = lazyeval::interp(~ (a / b)*100, a = as.name(Cyt), b = as.name(cov))
    Methylation %>% mutate_(.dots = setNames(list(mutate_call), col_name))
}
for(i in seq_along(Index_filtered$DNA.Meth_ID)){
  COVERAGE=paste0("coverage", i)
  mC=paste0("numCs", i)
  perc.mC=paste0("perc.mC_", Index_filtered$DNA.Meth_ID[i])
  Methylation<-f(Cyt=mC, cov=COVERAGE, col_name=perc.mC)
}
Methylation<-dplyr::select(Methylation, chr, start, end, contains("perc.mC"))

#Merge with the percent methylation (by cytosine)
myDiff_filtered<-left_join(myDiff_filtered, Methylation)

#Subset by statistical threshold
myDiff_p05<-dplyr::filter(myDiff_filtered, pvalue<0.05)
myDiff_q05<-dplyr::filter(myDiff_filtered, qvalue<0.05)

#Save a copy of the differential Methylation analysis
wb_countData<-createWorkbook()
addWorksheet(wb_countData, "P < 0.05")
  writeData(wb_countData, "P < 0.05", myDiff_p05, rowNames = F)
addWorksheet(wb_countData, "Q < 0.05")
  writeData(wb_countData, "Q < 0.05", myDiff_q05, rowNames = F)
saveWorkbook(wb_countData, file = paste0("../3_Output/", ANALYSIS, "/", ANALYSIS, "_DiffMeth.xlsx"), overwrite = TRUE)
write.table(myDiff_p05, file=paste0("../3_Output/", ANALYSIS, "/", ANALYSIS, "_DiffMeth.bed"), quote=F, sep="\t", row.names=F, col.names=F)

Annotate DMPs with Genomic and CpG Loci

library(openxlsx)
library(annotatr)
library(AnnotationHub)
library(rtracklayer)
library(TxDb.Hsapiens.UCSC.hg38.knownGene)

#convert to GRanges object (correct format for annotatr)
myDiff_p05_GR<-makeGRangesFromDataFrame(myDiff_p05, seqnames.field = "chr", strand.field="strand", start.field = "start", end.field = "end", keep.extra.columns = T)
#create annotations from the following sources
annots = c('hg38_cpgs', 'hg38_basicgenes')
# Build the annotations (a single GRanges object)
annotations = build_annotations(genome = 'hg38', annotations = annots)
# myDiff_GR<-as(myDiff, "GRanges")
# Intersect the regions read in with the annotations
dm_annotated = annotate_regions(
    regions = myDiff_p05_GR,
    annotations = annotations,
    ignore.strand = TRUE,
    quiet = FALSE)
#convert to a data.frame
df_dm_annotated = data.frame(dm_annotated)
# A GRanges object is returned
print(dm_annotated)
## GRanges object with 1123777 ranges and 14 metadata columns:
##             seqnames    ranges strand |         meth.diff
##                <Rle> <IRanges>  <Rle> |         <numeric>
##         [1]     chr1    629116      * |  24.6454673055515
##         [2]     chr1    629116      * |  24.6454673055515
##         [3]     chr1    629116      * |  24.6454673055515
##         [4]     chr1    629116      * |  24.6454673055515
##         [5]     chr1    629116      * |  24.6454673055515
##         ...      ...       ...    ... .               ...
##   [1123773]     chrX 155613303      * | -9.23270768161279
##   [1123774]     chrX 155613303      * | -9.23270768161279
##   [1123775]     chrX 155613303      * | -9.23270768161279
##   [1123776]     chrY  11332637      * | -25.5145682972467
##   [1123777]     chrY  11332637      * | -25.5145682972467
##                          pvalue            qvalue  perc.mC_14362X1
##                       <numeric>         <numeric>        <numeric>
##         [1]  0.0213191728665335 0.619668197783736 60.5263157894737
##         [2]  0.0213191728665335 0.619668197783736 60.5263157894737
##         [3]  0.0213191728665335 0.619668197783736 60.5263157894737
##         [4]  0.0213191728665335 0.619668197783736 60.5263157894737
##         [5]  0.0213191728665335 0.619668197783736 60.5263157894737
##         ...                 ...               ...              ...
##   [1123773]  0.0103176333771496 0.531249175577326 92.1052631578947
##   [1123774]  0.0103176333771496 0.531249175577326 92.1052631578947
##   [1123775]  0.0103176333771496 0.531249175577326 92.1052631578947
##   [1123776] 0.00356243772226756 0.391088667684166 31.5789473684211
##   [1123777] 0.00356243772226756 0.391088667684166 31.5789473684211
##             perc.mC_14362X10 perc.mC_14362X13  perc.mC_14362X2
##                    <numeric>        <numeric>        <numeric>
##         [1] 23.8095238095238 25.9259259259259 58.8235294117647
##         [2] 23.8095238095238 25.9259259259259 58.8235294117647
##         [3] 23.8095238095238 25.9259259259259 58.8235294117647
##         [4] 23.8095238095238 25.9259259259259 58.8235294117647
##         [5] 23.8095238095238 25.9259259259259 58.8235294117647
##         ...              ...              ...              ...
##   [1123773]  96.551724137931 80.1724137931034 85.7142857142857
##   [1123774]  96.551724137931 80.1724137931034 85.7142857142857
##   [1123775]  96.551724137931 80.1724137931034 85.7142857142857
##   [1123776] 38.4615384615385 41.9354838709677               50
##   [1123777] 38.4615384615385 41.9354838709677               50
##              perc.mC_14362X4  perc.mC_14362X6  perc.mC_14362X9
##                    <numeric>        <numeric>        <numeric>
##         [1] 56.4102564102564 59.4594594594595 23.5294117647059
##         [2] 56.4102564102564 59.4594594594595 23.5294117647059
##         [3] 56.4102564102564 59.4594594594595 23.5294117647059
##         [4] 56.4102564102564 59.4594594594595 23.5294117647059
##         [5] 56.4102564102564 59.4594594594595 23.5294117647059
##         ...              ...              ...              ...
##   [1123773]               80 78.3783783783784 77.0833333333333
##   [1123774]               80 78.3783783783784 77.0833333333333
##   [1123775]               80 78.3783783783784 77.0833333333333
##   [1123776]           46.875 36.3636363636364               65
##   [1123777]           46.875 36.3636363636364               65
##              perc.mC_14907X2  perc.mC_14907X3  perc.mC_14907X4
##                    <numeric>        <numeric>        <numeric>
##         [1] 21.4285714285714 34.1463414634146 10.7142857142857
##         [2] 21.4285714285714 34.1463414634146 10.7142857142857
##         [3] 21.4285714285714 34.1463414634146 10.7142857142857
##         [4] 21.4285714285714 34.1463414634146 10.7142857142857
##         [5] 21.4285714285714 34.1463414634146 10.7142857142857
##         ...              ...              ...              ...
##   [1123773] 91.1392405063291             92.5 94.4444444444444
##   [1123774] 91.1392405063291             92.5 94.4444444444444
##   [1123775] 91.1392405063291             92.5 94.4444444444444
##   [1123776] 63.6363636363636 61.1111111111111 85.7142857142857
##   [1123777] 63.6363636363636 61.1111111111111 85.7142857142857
##                                  annot
##                              <GRanges>
##         [1]       chr1:629009-630008:-
##         [2]       chr1:629011-630010:-
##         [3]       chr1:625896-629895:+
##         [4]       chr1:601578-711710:-
##         [5]       chr1:608057-698926:-
##         ...                        ...
##   [1123773] chrX:155612896-155613895:-
##   [1123774] chrX:155545278-155669757:-
##   [1123775] chrX:155613059-155615058:*
##   [1123776]   chrY:11328688-11332687:+
##   [1123777]   chrY:11332328-11333652:*
##   -------
##   seqinfo: 93 sequences from an unspecified genome; no seqlengths

##The issue with this annotation is that each DMP has multiple repeated rows if different annotations. To simplify this, we can condense the annotations into strings. This makes the resulting file more manageable based on the differential-methylation data.
DiffMeth_Annotated<-df_dm_annotated %>% 
  tidyr::fill(annot.symbol) %>% distinct() %>%
  dplyr::group_by(seqnames, start, end, meth.diff, pvalue, qvalue, annot.symbol) %>% 
  dplyr::summarise(Annotation=paste(unique(annot.type), collapse = ";"), Test=paste(unique(annot.id), collapse = ";"))
#Add %Methylation
DiffMeth_Annotated<-dplyr::rename(DiffMeth_Annotated, chr=seqnames)
DiffMeth_Annotated<-dplyr::left_join(DiffMeth_Annotated, Methylation)
#subset the Differential Methylation by statistics
DiffMeth_Annotated_p05<-subset(DiffMeth_Annotated, pvalue<0.05)
DiffMeth_Annotated_q05<-subset(DiffMeth_Annotated, qvalue<0.05)
#Write out the annotated DMP file 
library(openxlsx)
ifelse(!dir.exists(file.path("../3_Output/", ANALYSIS)), dir.create(file.path("../3_Output/", ANALYSIS)), FALSE)
## [1] FALSE

wb_WGBS_Annotate<-createWorkbook()
addWorksheet(wb_WGBS_Annotate, "P < 0.05")
  writeData(wb_WGBS_Annotate, "P < 0.05", DiffMeth_Annotated_p05, rowNames = F)
addWorksheet(wb_WGBS_Annotate, "Q < 0.05")
  writeData(wb_WGBS_Annotate, "Q < 0.05", DiffMeth_Annotated_q05, rowNames = F)
saveWorkbook(wb_WGBS_Annotate, file = paste0("../3_Output/", ANALYSIS, "/", ANALYSIS, "_Annotated_DiffMeth.xlsx"), overwrite = TRUE)
#Provide a summary of the annotation
dm_annsum = summarize_annotations(
    annotated_regions = dm_annotated,
    quiet = TRUE)
print(dm_annsum)
## # A tibble: 10 x 2
##    annot.type               n
##    <chr>                <int>
##  1 hg38_cpg_inter       61984
##  2 hg38_cpg_islands     48199
##  3 hg38_cpg_shelves     12691
##  4 hg38_cpg_shores      43617
##  5 hg38_genes_1to5kb    56694
##  6 hg38_genes_3UTRs      9846
##  7 hg38_genes_5UTRs     11590
##  8 hg38_genes_exons     51351
##  9 hg38_genes_introns   97600
## 10 hg38_genes_promoters 61366

#Plot the annotation distribution
dm_vs_kg_annotations = plot_annotation(
    annotated_regions = dm_annotated,
    plot_title = '# of Sites Tested for DM annotated on chr9',
    x_label = 'knownGene Annotations',
    y_label = 'Count')
print(dm_vs_kg_annotations)

annots_order = c(
    'hg38_genes_1to5kb',
    'hg38_genes_promoters',
    'hg38_genes_5UTRs',
    'hg38_genes_exons',
    'hg38_genes_introns',
    'hg38_genes_3UTRs')
dm_vs_kg_annotations = plot_annotation(
    annotated_regions = dm_annotated,
    annotation_order = annots_order,
    plot_title = '# of Sites Tested for DM annotated on chr9',
    x_label = 'knownGene Annotations',
    y_label = 'Count')
print(dm_vs_kg_annotations)

#Heatmap of Differential Methylation

library(pheatmap)
DiffMeth_hm<-dplyr::filter(DiffMeth_Annotated_p05, grepl("promoter", Annotation))
hm_Data<-as.data.frame(DiffMeth_hm)
hm_Data<-hm_Data[!is.na(hm_Data$annot.symbol),]
rownames(hm_Data)<-make.unique(hm_Data$annot.symbol, sep = ".")
hm_Data<-dplyr::filter(hm_Data)
##Make heatmap
STATISTIC=0.05
hm_Data<-dplyr::filter(hm_Data, qvalue<STATISTIC)

hm_Data<-dplyr::select(hm_Data, contains("perc.mC"))
hm_Data<-data.matrix(hm_Data)

##
##Index file for annotating samples
hm_Index<-Index_filtered
hm_Index$DNA.Meth_ID<-paste0("perc.mC_", hm_Index$DNA.Meth_ID)
rownames(hm_Index)<-hm_Index$DNA.Meth_ID
hm_Index<-as.data.frame(hm_Index)
hm_Index<-dplyr::select(hm_Index, Sample_ID, Timing, Age_yrs, Etiology, Sex, Race)

paletteLength <- 100
myColor <- colorRampPalette(c("dodgerblue4", "white", "gold2"))(paletteLength)
pheatmap(hm_Data,
         cluster_cols=T, 
         border_color=NA, 
         cluster_rows=T, 
         scale = 'row',
         show_colnames = T, 
         show_rownames = F, 
         color = myColor,
         annotation_col = hm_Index, 
         filename = paste0("../3_Output/", ANALYSIS, "/", ANALYSIS, "_Heatmap.Q05.pdf"))

Methylation Distribution using EnrichedHeatmap

#Import the genomic annotation file
library(EnrichedHeatmap)
library(annotatr)
library(TxDb.Hsapiens.UCSC.hg38.knownGene)
annots = c("hg38_basicgenes", "hg38_genes_promoters", "hg38_genes_intergenic",
           "hg38_genes_intronexonboundaries", "hg38_cpgs", "hg38_cpg_islands", "hg38_cpg_shores", "hg38_cpg_shelves", "hg38_cpg_inter")
annotations=build_annotations(genome = "hg38", annotations = annots)
annotations<-keepStandardChromosomes(annotations, pruning.mode = "coarse") #Remove nonstandard chromosomes

myDiff_p05<-dplyr::mutate(myDiff_p05, absolute.meth=abs(meth.diff))
myDiff_p05<-as(myDiff_p05, "GRanges")

meDiff<-as(myDiff, "GRanges")

# #Import the annotated "target" data
# myDiff<-openxlsx::read.xlsx(paste0("../2_Input/WGBS_MethylKit_DiffMeth", ANALYSIS,".xlsx"), sheet = "P < 0.05")
# myDiff_GR<-as(myDiff, "GRanges")

#Annotate GRanges using hg38 genome
dm_annotated = annotate_regions(
  regions = myDiff_p05,
  annotations = annotations,
  ignore.strand = TRUE)
#create data.frame
df_dm_annotated <- as.data.frame(dm_annotated)
#
library(GenomicFeatures)
genes<-genes(TxDb.Hsapiens.UCSC.hg38.knownGene)
tss = promoters(genes, upstream = 0, downstream = 1)
mat1 = normalizeToMatrix(myDiff_p05, tss, value_column = "absolute.meth", extend = 5000, mean_mode="w0", w=50, keep = c(0, 0.99))
EnrichedHeatmap(mat1, col = c("white", "black"), name = ANALYSIS)

png(file = paste0("../3_Output/", ANALYSIS, "/","_1Methyl.Gene.Distribution.png"), height = 3, width = 5)
EnrichedHeatmap(mat1, col = c("white", "black"), name = "Heart Failure")
dev.off()
## quartz_off_screen 
##                 2

partition = kmeans(mat1, centers = 3)$cluster
lgd = Legend(at = c("cluster1", "cluster2", "cluster3"), title = "Clusters", 
    type = "lines", legend_gp = gpar(col = 2:4))
ht_list = Heatmap(partition, col = structure(2:4, names = as.character(1:3)), name = "partition",
              show_row_names = FALSE, width = unit(3, "mm")) + EnrichedHeatmap(mat1, col = c("white", "red"), name = "% Methylation - Heart Failure", split = partition, top_annotation = HeatmapAnnotation(lines = anno_enriched(gp = gpar(col = 2:4))), column_title = "|PercentMethylation|")
draw(ht_list, main_heatmap = "% Methylation - Heart Failure")

partition = kmeans(mat1, centers = 3)$cluster
lgd = Legend(at = c("cluster1", "cluster2", "cluster3"), title = "Clusters", 
    type = "lines", legend_gp = gpar(col = 2:4))
ht_list = Heatmap(partition, col = structure(2:4, names = as.character(1:3)), name = "partition",
              show_row_names = FALSE, width = unit(3, "mm")) + EnrichedHeatmap(mat1, col = c("white", "red"), name = "% Methylation - Heart Failure", split = partition, top_annotation = HeatmapAnnotation(lines = anno_enriched(gp = gpar(col = 2:4))), column_title = "|PercentMethylation|")
pdf(file = paste0("../3_Output/", ANALYSIS, "/", ANALYSIS, "_Methyl.Gene.Distribution_Kmeans.pdf"), height = 7, width = 5)
draw(ht_list, main_heatmap = "% Methylation - Heart Failure")
dev.off()
## quartz_off_screen 
##                 2

Supplemental Table: R Session Information

All packages and setting are acquired using the following command:

sinfo<-devtools::session_info()
sinfo$platform
##  setting  value                       
##  version  R version 3.5.1 (2018-07-02)
##  os       macOS  10.14.1              
##  system   x86_64, darwin15.6.0        
##  ui       X11                         
##  language (EN)                        
##  collate  en_US.UTF-8                 
##  ctype    en_US.UTF-8                 
##  tz       America/Chicago             
##  date     2018-12-08

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
AnnotationDbi AnnotationDbi 1.44.0 1.44.0 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/AnnotationDbi /Library/Frameworks/R.framework/Versions/3.5/Resources/library/AnnotationDbi TRUE FALSE 2018-10-30 Bioconductor /Library/Frameworks/R.framework/Versions/3.5/Resources/library
AnnotationHub AnnotationHub 2.14.1 2.14.1 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/AnnotationHub /Library/Frameworks/R.framework/Versions/3.5/Resources/library/AnnotationHub TRUE FALSE 2018-11-05 Bioconductor /Library/Frameworks/R.framework/Versions/3.5/Resources/library
annotatr annotatr 1.8.0 1.8.0 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/annotatr /Library/Frameworks/R.framework/Versions/3.5/Resources/library/annotatr TRUE FALSE 2018-10-30 Bioconductor /Library/Frameworks/R.framework/Versions/3.5/Resources/library
assertthat assertthat 0.2.0 0.2.0 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/assertthat /Library/Frameworks/R.framework/Versions/3.5/Resources/library/assertthat FALSE FALSE 2017-04-11 CRAN (R 3.5.0) /Library/Frameworks/R.framework/Versions/3.5/Resources/library
backports backports 1.1.2 1.1.2 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/backports /Library/Frameworks/R.framework/Versions/3.5/Resources/library/backports FALSE FALSE 2017-12-13 CRAN (R 3.5.0) /Library/Frameworks/R.framework/Versions/3.5/Resources/library
base64enc base64enc 0.1.3 0.1-3 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/base64enc /Library/Frameworks/R.framework/Versions/3.5/Resources/library/base64enc FALSE FALSE 2015-07-28 CRAN (R 3.5.0) /Library/Frameworks/R.framework/Versions/3.5/Resources/library
bbmle bbmle 1.0.20 1.0.20 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/bbmle /Library/Frameworks/R.framework/Versions/3.5/Resources/library/bbmle FALSE FALSE 2017-10-30 CRAN (R 3.5.0) /Library/Frameworks/R.framework/Versions/3.5/Resources/library
bindr bindr 0.1.1 0.1.1 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/bindr /Library/Frameworks/R.framework/Versions/3.5/Resources/library/bindr FALSE FALSE 2018-03-13 CRAN (R 3.5.0) /Library/Frameworks/R.framework/Versions/3.5/Resources/library
bindrcpp bindrcpp 0.2.2 0.2.2 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/bindrcpp /Library/Frameworks/R.framework/Versions/3.5/Resources/library/bindrcpp TRUE FALSE 2018-03-29 CRAN (R 3.5.0) /Library/Frameworks/R.framework/Versions/3.5/Resources/library
Biobase Biobase 2.42.0 2.42.0 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/Biobase /Library/Frameworks/R.framework/Versions/3.5/Resources/library/Biobase TRUE FALSE 2018-10-30 Bioconductor /Library/Frameworks/R.framework/Versions/3.5/Resources/library
BiocGenerics BiocGenerics 0.28.0 0.28.0 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/BiocGenerics /Library/Frameworks/R.framework/Versions/3.5/Resources/library/BiocGenerics TRUE FALSE 2018-10-30 Bioconductor /Library/Frameworks/R.framework/Versions/3.5/Resources/library
BiocManager BiocManager 1.30.4 1.30.4 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/BiocManager /Library/Frameworks/R.framework/Versions/3.5/Resources/library/BiocManager FALSE FALSE 2018-11-13 CRAN (R 3.5.1) /Library/Frameworks/R.framework/Versions/3.5/Resources/library
BiocParallel BiocParallel 1.16.0 1.16.0 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/BiocParallel /Library/Frameworks/R.framework/Versions/3.5/Resources/library/BiocParallel FALSE FALSE 2018-10-30 Bioconductor /Library/Frameworks/R.framework/Versions/3.5/Resources/library
biomaRt biomaRt 2.38.0 2.38.0 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/biomaRt /Library/Frameworks/R.framework/Versions/3.5/Resources/library/biomaRt FALSE FALSE 2018-10-30 Bioconductor /Library/Frameworks/R.framework/Versions/3.5/Resources/library
Biostrings Biostrings 2.50.1 2.50.1 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/Biostrings /Library/Frameworks/R.framework/Versions/3.5/Resources/library/Biostrings FALSE FALSE 2018-11-06 Bioconductor /Library/Frameworks/R.framework/Versions/3.5/Resources/library
bit bit 1.1.14 1.1-14 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/bit /Library/Frameworks/R.framework/Versions/3.5/Resources/library/bit FALSE FALSE 2018-05-29 CRAN (R 3.5.0) /Library/Frameworks/R.framework/Versions/3.5/Resources/library
bit64 bit64 0.9.7 0.9-7 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/bit64 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/bit64 FALSE FALSE 2017-05-08 CRAN (R 3.5.0) /Library/Frameworks/R.framework/Versions/3.5/Resources/library
bitops bitops 1.0.6 1.0-6 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/bitops /Library/Frameworks/R.framework/Versions/3.5/Resources/library/bitops FALSE FALSE 2013-08-17 CRAN (R 3.5.0) /Library/Frameworks/R.framework/Versions/3.5/Resources/library
blob blob 1.1.1 1.1.1 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/blob /Library/Frameworks/R.framework/Versions/3.5/Resources/library/blob FALSE FALSE 2018-03-25 CRAN (R 3.5.0) /Library/Frameworks/R.framework/Versions/3.5/Resources/library
BSgenome BSgenome 1.50.0 1.50.0 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/BSgenome /Library/Frameworks/R.framework/Versions/3.5/Resources/library/BSgenome FALSE FALSE 2018-10-30 Bioconductor /Library/Frameworks/R.framework/Versions/3.5/Resources/library
callr callr 3.0.0 3.0.0 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/callr /Library/Frameworks/R.framework/Versions/3.5/Resources/library/callr FALSE FALSE 2018-08-24 CRAN (R 3.5.0) /Library/Frameworks/R.framework/Versions/3.5/Resources/library
circlize circlize 0.4.5 0.4.5 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/circlize /Library/Frameworks/R.framework/Versions/3.5/Resources/library/circlize FALSE FALSE 2018-11-21 CRAN (R 3.5.1) /Library/Frameworks/R.framework/Versions/3.5/Resources/library
cli cli 1.0.1 1.0.1 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/cli /Library/Frameworks/R.framework/Versions/3.5/Resources/library/cli FALSE FALSE 2018-09-25 CRAN (R 3.5.0) /Library/Frameworks/R.framework/Versions/3.5/Resources/library
coda coda 0.19.2 0.19-2 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/coda /Library/Frameworks/R.framework/Versions/3.5/Resources/library/coda FALSE FALSE 2018-10-08 CRAN (R 3.5.0) /Library/Frameworks/R.framework/Versions/3.5/Resources/library
codetools codetools 0.2.15 0.2-15 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/codetools /Library/Frameworks/R.framework/Versions/3.5/Resources/library/codetools FALSE FALSE 2016-10-05 CRAN (R 3.5.1) /Library/Frameworks/R.framework/Versions/3.5/Resources/library
colorspace colorspace 1.3.2 1.3-2 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/colorspace /Library/Frameworks/R.framework/Versions/3.5/Resources/library/colorspace FALSE FALSE 2016-12-14 CRAN (R 3.5.0) /Library/Frameworks/R.framework/Versions/3.5/Resources/library
ComplexHeatmap ComplexHeatmap 1.20.0 1.20.0 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/ComplexHeatmap /Library/Frameworks/R.framework/Versions/3.5/Resources/library/ComplexHeatmap TRUE FALSE 2018-10-30 Bioconductor /Library/Frameworks/R.framework/Versions/3.5/Resources/library
crayon crayon 1.3.4 1.3.4 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/crayon /Library/Frameworks/R.framework/Versions/3.5/Resources/library/crayon FALSE FALSE 2017-09-16 CRAN (R 3.5.0) /Library/Frameworks/R.framework/Versions/3.5/Resources/library
curl curl 3.2 3.2 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/curl /Library/Frameworks/R.framework/Versions/3.5/Resources/library/curl FALSE FALSE 2018-03-28 CRAN (R 3.5.0) /Library/Frameworks/R.framework/Versions/3.5/Resources/library
data.table data.table 1.11.8 1.11.8 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/data.table /Library/Frameworks/R.framework/Versions/3.5/Resources/library/data.table FALSE FALSE 2018-09-30 CRAN (R 3.5.0) /Library/Frameworks/R.framework/Versions/3.5/Resources/library
DBI DBI 1.0.0 1.0.0 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/DBI /Library/Frameworks/R.framework/Versions/3.5/Resources/library/DBI FALSE FALSE 2018-05-02 CRAN (R 3.5.0) /Library/Frameworks/R.framework/Versions/3.5/Resources/library
DelayedArray DelayedArray 0.8.0 0.8.0 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/DelayedArray /Library/Frameworks/R.framework/Versions/3.5/Resources/library/DelayedArray FALSE FALSE 2018-10-30 Bioconductor /Library/Frameworks/R.framework/Versions/3.5/Resources/library
desc desc 1.2.0 1.2.0 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/desc /Library/Frameworks/R.framework/Versions/3.5/Resources/library/desc FALSE FALSE 2018-05-01 CRAN (R 3.5.0) /Library/Frameworks/R.framework/Versions/3.5/Resources/library
devtools devtools 2.0.1 2.0.1 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/devtools /Library/Frameworks/R.framework/Versions/3.5/Resources/library/devtools FALSE FALSE 2018-10-26 CRAN (R 3.5.1) /Library/Frameworks/R.framework/Versions/3.5/Resources/library
digest digest 0.6.18 0.6.18 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/digest /Library/Frameworks/R.framework/Versions/3.5/Resources/library/digest FALSE FALSE 2018-10-10 CRAN (R 3.5.0) /Library/Frameworks/R.framework/Versions/3.5/Resources/library
dplyr dplyr 0.7.8 0.7.8 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/dplyr /Library/Frameworks/R.framework/Versions/3.5/Resources/library/dplyr TRUE FALSE 2018-11-10 CRAN (R 3.5.1) /Library/Frameworks/R.framework/Versions/3.5/Resources/library
emdbook emdbook 1.3.10 1.3.10 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/emdbook /Library/Frameworks/R.framework/Versions/3.5/Resources/library/emdbook FALSE FALSE 2018-05-19 CRAN (R 3.5.0) /Library/Frameworks/R.framework/Versions/3.5/Resources/library
EnrichedHeatmap EnrichedHeatmap 1.12.0 1.12.0 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/EnrichedHeatmap /Library/Frameworks/R.framework/Versions/3.5/Resources/library/EnrichedHeatmap TRUE FALSE 2018-10-31 Bioconductor /Library/Frameworks/R.framework/Versions/3.5/Resources/library
evaluate evaluate 0.12 0.12 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/evaluate /Library/Frameworks/R.framework/Versions/3.5/Resources/library/evaluate FALSE FALSE 2018-10-09 CRAN (R 3.5.0) /Library/Frameworks/R.framework/Versions/3.5/Resources/library
fansi fansi 0.4.0 0.4.0 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/fansi /Library/Frameworks/R.framework/Versions/3.5/Resources/library/fansi FALSE FALSE 2018-10-05 CRAN (R 3.5.0) /Library/Frameworks/R.framework/Versions/3.5/Resources/library
fastseg fastseg 1.28.0 1.28.0 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/fastseg /Library/Frameworks/R.framework/Versions/3.5/Resources/library/fastseg FALSE FALSE 2018-10-30 Bioconductor /Library/Frameworks/R.framework/Versions/3.5/Resources/library
fs fs 1.2.6 1.2.6 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/fs /Library/Frameworks/R.framework/Versions/3.5/Resources/library/fs FALSE FALSE 2018-08-23 CRAN (R 3.5.0) /Library/Frameworks/R.framework/Versions/3.5/Resources/library
GenomeInfoDb GenomeInfoDb 1.18.1 1.18.1 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/GenomeInfoDb /Library/Frameworks/R.framework/Versions/3.5/Resources/library/GenomeInfoDb TRUE FALSE 2018-11-12 Bioconductor /Library/Frameworks/R.framework/Versions/3.5/Resources/library
GenomeInfoDbData GenomeInfoDbData 1.2.0 1.2.0 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/GenomeInfoDbData /Library/Frameworks/R.framework/Versions/3.5/Resources/library/GenomeInfoDbData FALSE FALSE 2018-11-09 Bioconductor /Library/Frameworks/R.framework/Versions/3.5/Resources/library
GenomicAlignments GenomicAlignments 1.18.0 1.18.0 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/GenomicAlignments /Library/Frameworks/R.framework/Versions/3.5/Resources/library/GenomicAlignments FALSE FALSE 2018-10-30 Bioconductor /Library/Frameworks/R.framework/Versions/3.5/Resources/library
GenomicFeatures GenomicFeatures 1.34.1 1.34.1 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/GenomicFeatures /Library/Frameworks/R.framework/Versions/3.5/Resources/library/GenomicFeatures TRUE FALSE 2018-11-03 Bioconductor /Library/Frameworks/R.framework/Versions/3.5/Resources/library
GenomicRanges GenomicRanges 1.34.0 1.34.0 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/GenomicRanges /Library/Frameworks/R.framework/Versions/3.5/Resources/library/GenomicRanges TRUE FALSE 2018-10-30 Bioconductor /Library/Frameworks/R.framework/Versions/3.5/Resources/library
GetoptLong GetoptLong 0.1.7 0.1.7 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/GetoptLong /Library/Frameworks/R.framework/Versions/3.5/Resources/library/GetoptLong FALSE FALSE 2018-06-10 CRAN (R 3.5.0) /Library/Frameworks/R.framework/Versions/3.5/Resources/library
ggplot2 ggplot2 3.1.0 3.1.0 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/ggplot2 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/ggplot2 FALSE FALSE 2018-10-25 CRAN (R 3.5.0) /Library/Frameworks/R.framework/Versions/3.5/Resources/library
GlobalOptions GlobalOptions 0.1.0 0.1.0 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/GlobalOptions /Library/Frameworks/R.framework/Versions/3.5/Resources/library/GlobalOptions FALSE FALSE 2018-06-09 CRAN (R 3.5.0) /Library/Frameworks/R.framework/Versions/3.5/Resources/library
glue glue 1.3.0 1.3.0 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/glue /Library/Frameworks/R.framework/Versions/3.5/Resources/library/glue FALSE FALSE 2018-07-17 CRAN (R 3.5.0) /Library/Frameworks/R.framework/Versions/3.5/Resources/library
gtable gtable 0.2.0 0.2.0 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/gtable /Library/Frameworks/R.framework/Versions/3.5/Resources/library/gtable FALSE FALSE 2016-02-26 CRAN (R 3.5.0) /Library/Frameworks/R.framework/Versions/3.5/Resources/library
gtools gtools 3.8.1 3.8.1 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/gtools /Library/Frameworks/R.framework/Versions/3.5/Resources/library/gtools FALSE FALSE 2018-06-26 CRAN (R 3.5.0) /Library/Frameworks/R.framework/Versions/3.5/Resources/library
hms hms 0.4.2 0.4.2 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/hms /Library/Frameworks/R.framework/Versions/3.5/Resources/library/hms FALSE FALSE 2018-03-10 CRAN (R 3.5.0) /Library/Frameworks/R.framework/Versions/3.5/Resources/library
htmltools htmltools 0.3.6 0.3.6 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/htmltools /Library/Frameworks/R.framework/Versions/3.5/Resources/library/htmltools FALSE FALSE 2017-04-28 CRAN (R 3.5.0) /Library/Frameworks/R.framework/Versions/3.5/Resources/library
httpuv httpuv 1.4.5 1.4.5 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/httpuv /Library/Frameworks/R.framework/Versions/3.5/Resources/library/httpuv FALSE FALSE 2018-07-19 CRAN (R 3.5.0) /Library/Frameworks/R.framework/Versions/3.5/Resources/library
httr httr 1.3.1 1.3.1 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/httr /Library/Frameworks/R.framework/Versions/3.5/Resources/library/httr FALSE FALSE 2017-08-20 CRAN (R 3.5.0) /Library/Frameworks/R.framework/Versions/3.5/Resources/library
interactiveDisplayBase interactiveDisplayBase 1.20.0 1.20.0 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/interactiveDisplayBase /Library/Frameworks/R.framework/Versions/3.5/Resources/library/interactiveDisplayBase FALSE FALSE 2018-10-30 Bioconductor /Library/Frameworks/R.framework/Versions/3.5/Resources/library
IRanges IRanges 2.16.0 2.16.0 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/IRanges /Library/Frameworks/R.framework/Versions/3.5/Resources/library/IRanges TRUE FALSE 2018-10-30 Bioconductor /Library/Frameworks/R.framework/Versions/3.5/Resources/library
kableExtra kableExtra 0.9.0 0.9.0 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/kableExtra /Library/Frameworks/R.framework/Versions/3.5/Resources/library/kableExtra TRUE FALSE 2018-05-21 CRAN (R 3.5.0) /Library/Frameworks/R.framework/Versions/3.5/Resources/library
knitr knitr 1.20 1.20 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/knitr /Library/Frameworks/R.framework/Versions/3.5/Resources/library/knitr TRUE FALSE 2018-02-20 CRAN (R 3.5.0) /Library/Frameworks/R.framework/Versions/3.5/Resources/library
labeling labeling 0.3 0.3 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/labeling /Library/Frameworks/R.framework/Versions/3.5/Resources/library/labeling FALSE FALSE 2014-08-23 CRAN (R 3.5.0) /Library/Frameworks/R.framework/Versions/3.5/Resources/library
later later 0.7.5 0.7.5 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/later /Library/Frameworks/R.framework/Versions/3.5/Resources/library/later FALSE FALSE 2018-09-18 CRAN (R 3.5.0) /Library/Frameworks/R.framework/Versions/3.5/Resources/library
lattice lattice 0.20.38 0.20-38 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/lattice /Library/Frameworks/R.framework/Versions/3.5/Resources/library/lattice FALSE FALSE 2018-11-04 CRAN (R 3.5.0) /Library/Frameworks/R.framework/Versions/3.5/Resources/library
lazyeval lazyeval 0.2.1 0.2.1 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/lazyeval /Library/Frameworks/R.framework/Versions/3.5/Resources/library/lazyeval TRUE FALSE 2017-10-29 CRAN (R 3.5.0) /Library/Frameworks/R.framework/Versions/3.5/Resources/library
limma limma 3.38.2 3.38.2 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/limma /Library/Frameworks/R.framework/Versions/3.5/Resources/library/limma FALSE FALSE 2018-11-03 Bioconductor /Library/Frameworks/R.framework/Versions/3.5/Resources/library
locfit locfit 1.5.9.1 1.5-9.1 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/locfit /Library/Frameworks/R.framework/Versions/3.5/Resources/library/locfit FALSE FALSE 2013-04-20 CRAN (R 3.5.0) /Library/Frameworks/R.framework/Versions/3.5/Resources/library
magrittr magrittr 1.5 1.5 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/magrittr /Library/Frameworks/R.framework/Versions/3.5/Resources/library/magrittr FALSE FALSE 2014-11-22 CRAN (R 3.5.0) /Library/Frameworks/R.framework/Versions/3.5/Resources/library
MASS MASS 7.3.51.1 7.3-51.1 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/MASS /Library/Frameworks/R.framework/Versions/3.5/Resources/library/MASS FALSE FALSE 2018-11-01 CRAN (R 3.5.0) /Library/Frameworks/R.framework/Versions/3.5/Resources/library
Matrix Matrix 1.2.15 1.2-15 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/Matrix /Library/Frameworks/R.framework/Versions/3.5/Resources/library/Matrix FALSE FALSE 2018-11-01 CRAN (R 3.5.0) /Library/Frameworks/R.framework/Versions/3.5/Resources/library
matrixStats matrixStats 0.54.0 0.54.0 /Library/Frameworks/R.framework/Versions/3.5/Resources/library/matrixStats /Library/Frameworks/R.framework/Versions/3.5/Resources/library/matrixStats FALSE FALSE 2018-07-23 CRAN (R 3.5.0) /Library/Frameworks/R.framework/Versions/3.5/Resources/library
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The failing heart undergoes profound metabolic changes due to alterations in cardiac gene expression, reactivating glycolytic genes and suppressing oxidative metabolic genes. In this study, we discover that alterations in cardiac DNA methylation encode this fetal-like metabolic preference. We also identify epigenetic interference of NRF1 via hyp…

doi.org/10.1152/ajpheart.00016.2019

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Computational Methods - "Genome-wide DNA methylation in end-stage human heart failure" Latest
Jul 12, 2019

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