Estill 2016 reanalysis
The methylation in ART meta-analysis group
2022-06-08
- Introduction
- Loading packages and functions
- Data import
- Normalisation
- Filter probes
- Extracting Beta and M-values
- MDS analysis
- Differential analysis
- Venn diagrams of the differential methylated probes for each contrast
- Spearman correlations of each contrast
- Enrichment analysis
- Run 1D enrichment analysis
- Run multi-dimensional enrichment analysis
Introduction
In this report, I will take you through a re-analysis methylation data first described by Estill et al (2016).
In their study, they analysed the DNA methylation patterns of 137 neonatal blood spots conceived naturally (NAT), through through insemination (IUI), or through ICSI using fresh or cryopreserved (frozen) embryo transfer (FH and FZ respectively).
The platform used in the study is the Illumina Infinium HumanMethylation450k BeadChip assay. The authors used a pipeline based on ChAMP (Tian et al, 2017), together with Adjacent Site Clustering (Sofer et al, 2013).
The methylation data have been deposited to NCBI GEO repository accession number GSE79257.
The main conclusions from the original study were:
The methylation profiles of assisted reproductive technology and IUI newborns were dramatically different from those of naturally (in vivo) conceived newborns.
Profiles of ICSI-frozen (FET) and IUI infants were strikingly similar, suggesting that cryopreservation may temper some of the epigenetic aberrations induced by IVF or ICSI.
The DNA methylation changes associated with IVF/ICSI culture conditions and/or parental infertility were detected at metastable epialleles, suggesting a lasting impact on a child’s epigenome.
Both infertility and ICSI alter DNA methylation at specific genomic loci, an effect that is mitigated to some extent by FET (freezing).
The aim of this work is to;
develop the analytical pipelines required for efficient re-analysis of 450K array data,
to confirm that we are able to obtain differential methylation results that are similar to those obtained in the original study, and
to critically evaluate the conclusions made in the original study.
In this report I will be using the missMethyl vignette as a guide to analyse this dataset (Phipson et al, 2015).
Previously I curated a list of good and bad probes using the script “filter_probes.Rmd”.
Loading packages and functions
These packackes will help us to perform vital steps such as normalisation, filtering, differential analysis, etc, and provide information about the array probe annotaions.
These functions provide shortcuts to help with charts and other analysis. They will eventually be shoved into another Rscript or package but can stay here for now.
source("meth_functions.R")
# Annotation
ann450k <- getAnnotation(IlluminaHumanMethylation450kanno.ilmn12.hg19)
myann <- data.frame(ann450k[,c("UCSC_RefGene_Name","Regulatory_Feature_Group","Islands_Name","Relation_to_Island")])
promoters <- grep("Prom",myann$Regulatory_Feature_Group)Data import
TODO- add explanation
dir.create("GSE79257")## Warning in dir.create("GSE79257"): 'GSE79257' already existsARRAY_SAMPLESHEET="GSE79257/GSE79257_Illumina_samplesheet.csv"
# only download it if it is not present on the system
if ( !file.exists(ARRAY_SAMPLESHEET ) ) {
DLFILE=paste(ARRAY_SAMPLESHEET,".gz",sep="")
download.file("https://ftp.ncbi.nlm.nih.gov/geo/series/GSE79nnn/GSE79257/suppl/GSE79257_Illumina_samplesheet.csv.gz",
destfile = DLFILE)
gunzip(DLFILE, overwrite = TRUE )
}
ARRAY_DATA="GSE79257/GSE79257_RAW.tar"
# only download it if it is not present on the system
if ( !dir.exists("GSE79257/IDAT") ) {
dir.create("GSE79257/IDAT")
if ( !file.exists(ARRAY_DATA) ) {
system('wget -O idats.tar.gz "https://www.ncbi.nlm.nih.gov/geo/download/?acc=GSE79257&format=file"')
file.rename("idats.tar.gz",ARRAY_DATA)
untar(exdir = "GSE79257/IDAT", tarfile = ARRAY_DATA)
}
}
baseDir <- "GSE79257"
targets <- read.metharray.sheet(baseDir)## [read.metharray.sheet] Found the following CSV files:## [1] "GSE79257/GSE79257_Illumina_samplesheet.csv"head(targets)## Sample_Name Sample_Plate Sample_Group Pool_ID Project_ID Sample_Well Array
## 1 20001_D 2 NAT_Male <NA> NA R04C01 R04C01
## 2 20028_D 2 NAT_Male <NA> NA R01C01 R01C01
## 3 20029_D 2 NAT_Male <NA> NA R02C02 R02C02
## 4 20030_D 2 NAT_Male <NA> NA R04C02 R04C02
## 5 20031_D 2 NAT_Male <NA> NA R01C02 R01C02
## 6 20032_D 1 NAT_Male <NA> NA R03C02 R03C02
## Slide Basename
## 1 9266441185 GSE79257/IDAT/GSM2089923_9266441185_R04C01
## 2 9266441185 GSE79257/IDAT/GSM2089924_9266441185_R01C01
## 3 9266441185 GSE79257/IDAT/GSM2089925_9266441185_R02C02
## 4 9266441186 GSE79257/IDAT/GSM2089926_9266441186_R04C02
## 5 9285451032 GSE79257/IDAT/GSM2089927_9285451032_R01C02
## 6 9297949059 GSE79257/IDAT/GSM2089928_9297949059_R03C02rgSet <- read.metharray.exp(targets = targets)## Warning in readChar(con, nchars = n): truncating string with embedded nuls## Warning in readChar(con, nchars = n): truncating string with embedded nuls
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## Warning in readChar(con, nchars = n): truncating string with embedded nulsNormalisation
mSet <- preprocessRaw(rgSet)
mSetSw <- SWAN(mSet,verbose=FALSE)## [SWAN] Preparing normalization subset
## 450k
## [SWAN] Normalizing methylated channel
## [SWAN] Normalizing unmethylated channelpar(mfrow=c(1,2), cex=0.8)
densityByProbeType(mSet[,1], main = "Raw")
densityByProbeType(mSetSw[,1], main = "SWAN")
Figure 1. Normalisation of bead-array data with SWAN.
Filter probes
Here we are running parallel analyses, both including and excluding sex chromosomes.
# include sex chromosomes
detP <- detectionP(rgSet)
keep <- rowSums(detP < 0.01) == ncol(rgSet)
mSetSw <- mSetSw[keep,]
# exclude SNP probes
mSetSw <- mapToGenome(mSetSw)
mSetSw_nosnp <- dropLociWithSnps(mSetSw)
dim(mSetSw)## [1] 443589 137dim(mSetSw_nosnp)## [1] 428936 137mSetSw <- mSetSw_nosnp
# exclude sex chromosomes
keep <- !(featureNames(mSetSw) %in% ann450k$Name[ann450k$chr %in% c("chrX","chrY")])
mSetFlt <- mSetSw[keep,]
head(mSetFlt)## class: GenomicMethylSet
## dim: 6 137
## metadata(0):
## assays(2): Meth Unmeth
## rownames(6): cg13869341 cg14008030 ... cg00381604 cg20253340
## rowData names(0):
## colnames(137): GSM2089923_9266441185_R04C01
## GSM2089924_9266441185_R01C01 ... GSM2090058_9611519080_R05C01
## GSM2090059_9611519080_R05C02
## colData names(10): Sample_Name Sample_Plate ... Basename filenames
## Annotation
## array: IlluminaHumanMethylation450k
## annotation: ilmn12.hg19
## Preprocessing
## Method: SWAN (based on a MethylSet
## preprocessed as 'Raw (no normalization or bg correction)')
## minfi version: 1.42.0
## Manifest version: 0.4.0dim(mSetFlt)## [1] 418833 137Extracting Beta and M-values
# include sex chromosomes
meth <- getMeth(mSetSw)
unmeth <- getUnmeth(mSetSw)
Mval <- log2((meth + 100)/(unmeth + 100))
beta <- getBeta(mSetSw)
# exclude sex chromosomes
meth <- getMeth(mSetFlt)
unmeth <- getUnmeth(mSetFlt)
Mval_flt <- log2((meth + 100)/(unmeth + 100))
beta_flt <- getBeta(mSetFlt)MDS analysis
[Multidimensional scaling(https://en.wikipedia.org/wiki/Multidimensional_scaling) plot is a method used to identify the major sources of variation in a dataset. In the MDS plots below, I will be plotting the first two dimensions (principal components [PCs]), with each sample label coloured either by ART classification, sex, ART and sex, and then array chip and then sample plate.
We wil begin with MDS analysis including the sex chromosomes and then exclude them.
First, let’s quantify the contribution of the major principal components. with a scree plot, we can see whether most of the variation is captured in the first two PCs or whether it is spread over more PCs. As we can see in Figure 2, the main source of variation is what is shown in PC1, and a much lesser extent on the other dimensions. Interestingly, excluding sex chromosomes does not seem to change the relative contributions of PCs very much.
par(mfrow=c(2,1))
myscree(Mval,main="incl sex chr")
myscree(Mval_flt,main="excl sex chr")
Figure 2. Screeplot shows contribution of top principal components to the overall variation in the experiment.
Here is the MDS plot by ART classification (Figure 3). You can see that there are four clusters of samples when MDS is projected this way. Natural birth infants (NAT) are mostly seen in the two clusters at the bottom of the chart and the IVF frozen, IVF fresh and in intrauterine insemination (IUI) appear mostly as two clusters at the top of the chart. IUI seem to have a few samples that appear as intermediate between the upper and lower clusters.
When excluding sex chromosomes, the samples tend to form two clusters with the NAT samples on the left of the chart and others on the right of the chart. IUI and FH have some samples which have intermediate methylation, which points to a slight effect of freezing.
targets$sex <- factor(sapply(strsplit(targets$Sample_Group,"_"),"[[",2))
targets$art <- factor(sapply(strsplit(targets$Sample_Group,"_"),"[[",1))
sample_groups <- factor(targets$art)
colour_palette=brewer.pal(n = length(levels(sample_groups)), name = "Paired")
colours <- colour_palette[as.integer(factor(targets$art))]
plot(1,axes = FALSE,xlab="",ylab="",main="MDS by ART type")
legend("center",legend=levels(sample_groups),pch=16,cex=1.2,col=colour_palette)
Figure 3. MDS plot coloured by ART classification.
mydist <- plotMDS(Mval, labels=targets$Sample_Name,col=colours,main="sex chromosomes included")
Figure 3. MDS plot coloured by ART classification.
mydist_flt <- plotMDS(Mval_flt, labels=targets$Sample_Name,col=colours,main="sex chromosomes excluded")
Figure 3. MDS plot coloured by ART classification.
Next, we created an MDS plot by sex (Figure 4). The female samples appear in the two cluster on the right hand side of the chart and the male sample on the left. This could be simply due to the chromosomal makeup of the male and female samples or could also be due to autosomes. To check this, the MDS plot needs to be repeated with autosomes excluded.
When excluding the sex chromosomes, there is no clear clustering of the samples by sex. It appears that ART classification is the dominant source of variation.
sample_groups <- factor(targets$sex)
colour_palette=brewer.pal(n = length(levels(sample_groups)), name = "Paired")## Warning in brewer.pal(n = length(levels(sample_groups)), name = "Paired"): minimal value for n is 3, returning requested palette with 3 different levelscolours <- colour_palette[as.integer(factor(targets$sex))]
plot(1,axes = FALSE,xlab="",ylab="",main="MDS by sex")
legend("center",legend=levels(sample_groups),pch=16,cex=1.2,col=colour_palette)
Figure 4. MDS plot coloured by sex.
plotMDS(mydist, labels=targets$Sample_Name,col=colours,main="sex chromosomes included")
Figure 4. MDS plot coloured by sex.
plotMDS(mydist_flt, labels=targets$Sample_Name,col=colours,main="sex chromosomes excluded")
Figure 4. MDS plot coloured by sex.
For completeness, we show the MDS by ART and sex (Figure 5). It confirms the trends seen above where the samples are split on the x axis by sex and on the y axis by ART classification.
When sex chromosomes are removed, it is clear that ART classification is the dominant source of variance.
sample_groups <- factor(targets$Sample_Group)
colour_palette=brewer.pal(n = length(levels(sample_groups)), name = "Paired")
colours <- colour_palette[as.integer(factor(targets$Sample_Group))]
plot(1,axes = FALSE,xlab="",ylab="",main="MDS by ART and sex")
legend("center",legend=levels(sample_groups),pch=16,cex=1.2,col=colour_palette)
Figure 5. MDS plot coloured by ART classification and sex.
plotMDS(mydist, labels=targets$Sample_Name,col=colours,main="sex chromosomes included")
Figure 5. MDS plot coloured by ART classification and sex.
plotMDS(mydist_flt, labels=targets$Sample_Name,col=colours,main="sex chromosomes excluded")
Figure 5. MDS plot coloured by ART classification and sex.
To acertain whether technical factors like batch effects account for variance on the two main PCs, we create an MDS by array chip (Figure 6). There appears to be no relationship between array chip number and the top two PCs in the two MDS plots shown below.
sample_groups <- factor(targets$Array)
colour_palette=brewer.pal(n = length(levels(sample_groups)), name = "Paired")
colours <- colour_palette[as.integer(factor(targets$Array))]
plot(1,axes = FALSE,xlab="",ylab="",main="MDS by array chip")
legend("center",legend=levels(sample_groups),pch=16,cex=1.2,col=colour_palette)
Figure 6. MDS plot coloured by array chip.
plotMDS(mydist, labels=targets$Sample_Name,col=colours,main="sex chromosomes included")
Figure 6. MDS plot coloured by array chip.
plotMDS(mydist_flt, labels=targets$Sample_Name,col=colours,main="sex chromosomes excluded")
Figure 6. MDS plot coloured by array chip.
This was also performed for sample plate (Figure 7). It appears that the laboratory did not randomise samples over plates 1 and 2. For example the samples on plate 2 appear to be mostly NAT. This does not appear to be a major concern because NAT samples from plate 2 seem to cluster with NAT samples on plate 1 in both MDS plots.
sample_groups <- factor(targets$Sample_Plate)
colour_palette=brewer.pal(n = length(levels(sample_groups)), name = "Paired")## Warning in brewer.pal(n = length(levels(sample_groups)), name = "Paired"): minimal value for n is 3, returning requested palette with 3 different levelscolours <- colour_palette[as.integer(factor(targets$Sample_Plate))]
plot(1,axes = FALSE,xlab="",ylab="",main="MDS by sample plate")
legend("center",legend=levels(sample_groups),pch=16,cex=1.2,col=colour_palette)
Figure 7. MDS plot coloured by sample plate.
plotMDS(mydist, labels=targets$Sample_Name,col=colours,main="sex chromosomes included")
Figure 7. MDS plot coloured by sample plate.
plotMDS(mydist_flt, labels=targets$Sample_Name,col=colours,main="sex chromosomes excluded")
Figure 7. MDS plot coloured by sample plate.
Differential analysis
There are several differential contrasts that would be of interest to us in this study:
NAT (43) vs FH (38): to see what probes are affected by IVF where the embryos are not frozen.
NAT (43) vs FZ (38): to see what probes are affected by IVF where the embryos are frozen.
NAT (43) vs IUI (18): It will be interesting to see if the probes affected by IVF are also sensitive to IUI, a process that involves in utero injection of sperm.
FH (38) vs FZ (38): to see whether freezing results in differential methylation.
NAT (43) vs FH+FZ (76): if freezing results in only a small effect, then these groups can be lumped together and provide more power to determine subtle effects of IVF.
IUI (18) vs FH+FZ (76): It will be interesting to see whether the process of extracting eggs, culturing and fertilising in vitro has an effect that is separate to IUI.
The differential analysis is centred around limma to identify differentially methylated probes. TopConfects was also run to obtain the probes with the largest confident effect (topconfect). There are five outputs below:
Volcano plot (limma result).
Beeswarm plot (top probes by limma p-value).
Heatmap (top probes by limma p-value).
Beeswarm plot (top probes by topconfect ranking).
Heatmap (top probes by topconfect ranking). (TODO)
NAT vs FH
In this first analysis I will look at the effect of natural conception versus IVF (fresh). I have conducted the differential analysis in parallel, both includng and excluding sex chromosomes. Even when excluding autosomes, there are ~2000 probes on the autosomes that show differential methylation between sexes. Based on this data and MDS analysis, we will now exclude sex chromosomes from downstream analysis.
In the comparison of NAT and FH samples there were ~150k probes with differential methylation (FDR<0.05), with ~120k loss of methylation and ~30k with increased methylation.
# include sex chromosomes
samplesheet <- subset(targets,art=="NAT" | art=="FH")
samplesheet$Basename <- sapply(strsplit(samplesheet$Basename, "/"), "[[", 3)
groups <- factor(samplesheet$art,levels=c("NAT","FH"))
sex <- factor(samplesheet$sex,levels=c("Male","Female"))
top_nat_vs_fh_inc <- dm_analysis(samplesheet=samplesheet,
sex=sex,groups=groups,mx=Mval,name="top_nat_vs_fh_inc",
myann=myann ,beta= beta)## Your contrast returned 149799 individually significant probes. We recommend the default setting of pcutoff in dmrcate().## Fitting chr1...## Fitting chr2...## Fitting chr3...## Fitting chr4...## Fitting chr5...## Fitting chr6...## Fitting chr7...## Fitting chr8...## Fitting chr9...## Fitting chr10...## Fitting chr11...## Fitting chr12...## Fitting chr13...## Fitting chr14...## Fitting chr15...## Fitting chr16...## Fitting chr17...## Fitting chr18...## Fitting chr19...## Fitting chr20...## Fitting chr21...## Fitting chr22...## Fitting chrX...## Fitting chrY...## Demarcating regions...## Done!## snapshotDate(): 2022-04-26## see ?DMRcatedata and browseVignettes('DMRcatedata') for documentation## loading from cache
##
## RCircos.Core.Components initialized.
## Type ?RCircos.Reset.Plot.Parameters to see how to modify the core components.## Tiles plot may use more than one track. Please select correct area for next track if necessary.
head(top_nat_vs_fh_inc$dma)## Row.names UCSC_RefGene_Name Regulatory_Feature_Group
## 328980 cg20757019 Unclassified
## 141894 cg08260549 XPO7;XPO7 Promoter_Associated
## 274682 cg16905586 GMPPB;GMPPB;GMPPB;GMPPB Promoter_Associated
## 35094 cg01931797 KCTD21;USP35 Promoter_Associated
## 135935 cg07897831 CASKIN2;TSEN54 Unclassified
## 293640 cg18233405 TSPYL5;TSPYL5 Promoter_Associated
## Islands_Name Relation_to_Island logFC AveExpr
## 328980 chr2:95872891-95873548 Island -1.2204194 -4.112831
## 141894 chr8:21776842-21777942 Island -0.9881965 -4.154736
## 274682 chr3:49760863-49761895 Island -0.7473933 -3.581648
## 35094 chr11:77899564-77899990 Island -1.0105356 -4.476141
## 135935 chr17:73511016-73513176 Island -0.8859771 -4.316226
## 293640 chr8:98289604-98290404 Island -0.9289544 -4.609038
## t P.Value adj.P.Val B
## 328980 -22.27964 9.125943e-37 3.914445e-31 71.71540
## 141894 -19.07210 4.519288e-32 6.726989e-27 61.60746
## 274682 -19.06077 4.704890e-32 6.726989e-27 61.56949
## 35094 -18.49388 3.594114e-31 3.235396e-26 59.64843
## 135935 -18.48058 3.771420e-31 3.235396e-26 59.60287
## 293640 -18.30579 7.116671e-31 5.087661e-26 59.00174head(top_nat_vs_fh_inc$dmr)## GRanges object with 6 ranges and 8 metadata columns:
## seqnames ranges strand | no.cpgs min_smoothed_fdr
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## [1] chr14 101290195-101294147 * | 35 2.35521e-108
## [2] chr11 2016747-2022386 * | 65 3.78464e-106
## [3] chr6 32154116-32167020 * | 142 5.08475e-51
## [4] chr6 31864574-31870840 * | 112 2.85919e-71
## [5] chr6 30709428-30713442 * | 82 4.12865e-115
## [6] chr6 33280571-33291947 * | 208 7.33524e-49
## Stouffer HMFDR Fisher maxdiff meandiff
## <numeric> <numeric> <numeric> <numeric> <numeric>
## [1] 2.16917e-133 2.28022e-13 1.93905e-127 -0.0666918 -0.04134227
## [2] 7.65673e-86 4.05081e-08 1.29712e-111 -0.0758465 -0.02483262
## [3] 4.03583e-80 9.67239e-07 2.22737e-107 -0.0952814 -0.01096963
## [4] 4.10488e-96 1.25924e-11 2.96670e-106 -0.1413308 -0.01508391
## [5] 4.34947e-69 1.18789e-17 6.74499e-105 -0.0620120 -0.00843931
## [6] 1.88168e-68 4.49419e-06 1.01182e-102 -0.0553689 -0.00849132
## overlapping.genes
## <character>
## [1] MEG3
## [2] H19
## [3] PBX2, GPSM3, NOTCH4
## [4] C2, EHMT2, ZBTB12
## [5] XXbac-BPG252P9.10, F..
## [6] TAPBP, ZBTB22, DAXX
## -------
## seqinfo: 23 sequences from an unspecified genome; no seqlengthstop_nat_vs_fh_inc$comp## $up_comp
## all up OR fisherPval
## Intergenic 266413 20491 1.6402216 2.093892e-304
## Gene_Associated 1206 89 1.1263281 2.703269e-01
## Gene_Associated_Cell_type_specific 1767 152 1.3317609 1.087112e-03
## NonGene_Associated 1293 79 0.9193418 5.012865e-01
## NonGene_Associated_Cell_type_specific 190 10 0.7849761 5.588588e-01
## Promoter_Associated 89336 4280 0.6597122 4.590402e-143
## Promoter_Associated_Cell_type_specific 5690 256 0.6626581 1.181350e-11
## Unclassified 29032 1342 0.6692062 1.541970e-49
## Unclassified_Cell_type_specific 34009 1649 0.7028045 1.152795e-45
## lowerCI upperCI
## Intergenic 1.5967867 1.6849481
## Gene_Associated 0.8968789 1.3991468
## Gene_Associated_Cell_type_specific 1.1197387 1.5742900
## NonGene_Associated 0.7224845 1.1553063
## NonGene_Associated_Cell_type_specific 0.3700526 1.4775782
## Promoter_Associated 0.6378613 0.6821332
## Promoter_Associated_Cell_type_specific 0.5819736 0.7517691
## Unclassified 0.6322168 0.7079148
## Unclassified_Cell_type_specific 0.6674772 0.7396417
##
## $dn_comp
## all dn OR fisherPval
## Intergenic 266413 59158 0.4592678 0.000000e+00
## Gene_Associated 1206 299 0.8342145 6.497084e-03
## Gene_Associated_Cell_type_specific 1767 300 0.5165492 8.828568e-29
## NonGene_Associated 1293 474 1.4670900 7.553320e-11
## NonGene_Associated_Cell_type_specific 190 57 1.0850765 6.289724e-01
## Promoter_Associated 89336 36821 2.1124124 0.000000e+00
## Promoter_Associated_Cell_type_specific 5690 1973 1.3495519 8.140456e-26
## Unclassified 29032 10744 1.5346804 4.509041e-242
## Unclassified_Cell_type_specific 34009 11625 1.3481958 1.128551e-133
## lowerCI upperCI
## Intergenic 0.4530859 0.4655507
## Gene_Associated 0.7294148 0.9519033
## Gene_Associated_Cell_type_specific 0.4546057 0.5853468
## NonGene_Associated 1.3071584 1.6451108
## NonGene_Associated_Cell_type_specific 0.7811687 1.4906457
## Promoter_Associated 2.0799731 2.1451608
## Promoter_Associated_Cell_type_specific 1.2766704 1.4262451
## Unclassified 1.4968721 1.5732873
## Unclassified_Cell_type_specific 1.3167940 1.3802807top_nat_vs_fh_inc$cgi## $up_comp
## all up OR fisherPval lowerCI upperCI
## Island 146442 6241 0.5243170 0.000000e+00 0.5093142 0.5396748
## N_Shelf 20298 1714 1.3228218 2.693796e-25 1.2563112 1.3922011
## N_Shore 57958 3904 1.0238975 1.863647e-01 0.9884049 1.0603927
## OpenSea 140668 12036 1.5600291 4.397728e-271 1.5221724 1.5987265
## S_Shelf 18006 1539 1.3390891 8.379927e-25 1.2682561 1.4130966
## S_Shore 45564 2914 0.9615272 5.301095e-02 0.9238660 1.0004577
##
## $dn_comp
## all dn OR fisherPval lowerCI upperCI
## Island 146442 60043 2.5020096 0.000000e+00 2.4677536 2.5364661
## N_Shelf 20298 4066 0.6215179 2.791362e-169 0.5999686 0.6437290
## N_Shore 57958 15780 0.9393169 3.705902e-10 0.9209893 0.9579933
## OpenSea 140668 25342 0.4393491 0.000000e+00 0.4325160 0.4462992
## S_Shelf 18006 3578 0.6165559 1.246078e-155 0.5938735 0.6400029
## S_Shore 45564 12642 0.9689612 4.313832e-03 0.9481081 0.9902583names(top_nat_vs_fh_inc)## [1] "dma" "dm_up" "dm_dn" "confects" "dmr" "comp" "cgi"
## [8] "fit"head(top_nat_vs_fh_inc$confects$table$name)## [1] "cg15415945" "cg06834912" "cg06545389" "cg04922029" "cg20757019"
## [6] "cg12697337"# exclude sex chromosomes
samplesheet <- subset(targets,art=="NAT" | art=="FH")
samplesheet$Basename <- sapply(strsplit(samplesheet$Basename, "/"), "[[", 3)
groups <- factor(samplesheet$art,levels=c("NAT","FH"))
sex <- factor(samplesheet$sex,levels=c("Male","Female"))
top_nat_vs_fh <- dm_analysis(samplesheet=samplesheet,
sex=sex,groups=groups,mx=Mval_flt,name="top_nat_vs_fh",
myann=myann, beta=beta_flt)## Your contrast returned 146730 individually significant probes. We recommend the default setting of pcutoff in dmrcate().## Fitting chr1...## Fitting chr2...## Fitting chr3...## Fitting chr4...## Fitting chr5...## Fitting chr6...## Fitting chr7...## Fitting chr8...## Fitting chr9...## Fitting chr10...## Fitting chr11...## Fitting chr12...## Fitting chr13...## Fitting chr14...## Fitting chr15...## Fitting chr16...## Fitting chr17...## Fitting chr18...## Fitting chr19...## Fitting chr20...## Fitting chr21...## Fitting chr22...## Demarcating regions...## Done!## snapshotDate(): 2022-04-26## see ?DMRcatedata and browseVignettes('DMRcatedata') for documentation## loading from cache
##
## RCircos.Core.Components initialized.
## Type ?RCircos.Reset.Plot.Parameters to see how to modify the core components.## Tiles plot may use more than one track. Please select correct area for next track if necessary.
head(top_nat_vs_fh$dma)## Row.names UCSC_RefGene_Name Regulatory_Feature_Group
## 321265 cg20757019 Unclassified
## 138436 cg08260549 XPO7;XPO7 Promoter_Associated
## 268273 cg16905586 GMPPB;GMPPB;GMPPB;GMPPB Promoter_Associated
## 34194 cg01931797 KCTD21;USP35 Promoter_Associated
## 132599 cg07897831 CASKIN2;TSEN54 Unclassified
## 286730 cg18233405 TSPYL5;TSPYL5 Promoter_Associated
## Islands_Name Relation_to_Island logFC AveExpr
## 321265 chr2:95872891-95873548 Island -1.2204194 -4.112831
## 138436 chr8:21776842-21777942 Island -0.9881965 -4.154736
## 268273 chr3:49760863-49761895 Island -0.7473933 -3.581648
## 34194 chr11:77899564-77899990 Island -1.0105356 -4.476141
## 132599 chr17:73511016-73513176 Island -0.8859771 -4.316226
## 286730 chr8:98289604-98290404 Island -0.9289544 -4.609038
## t P.Value adj.P.Val B
## 321265 -22.28318 9.137484e-37 3.827080e-31 71.72984
## 138436 -19.07579 4.505529e-32 6.439518e-27 61.62133
## 268273 -19.06919 4.612472e-32 6.439518e-27 61.59920
## 34194 -18.49684 3.590088e-31 3.136821e-26 59.65971
## 132599 -18.48519 3.744716e-31 3.136821e-26 59.61979
## 286730 -18.30959 7.085398e-31 4.945997e-26 59.01588 head(top_nat_vs_fh$dmr)## GRanges object with 6 ranges and 8 metadata columns:
## seqnames ranges strand | no.cpgs min_smoothed_fdr
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## [1] chr14 101290195-101294147 * | 35 1.92961e-108
## [2] chr11 2016747-2022386 * | 65 2.81416e-106
## [3] chr6 32154116-32167020 * | 142 4.77005e-51
## [4] chr6 31864574-31870840 * | 112 2.69778e-71
## [5] chr6 30709428-30713442 * | 82 3.89138e-115
## [6] chr6 33280571-33291947 * | 208 6.76176e-49
## Stouffer HMFDR Fisher maxdiff meandiff
## <numeric> <numeric> <numeric> <numeric> <numeric>
## [1] 1.33691e-133 2.18908e-13 1.17514e-127 -0.0666918 -0.04134227
## [2] 5.21022e-86 3.95789e-08 7.55645e-112 -0.0758465 -0.02483262
## [3] 2.96971e-80 9.52394e-07 1.45821e-107 -0.0952814 -0.01096963
## [4] 3.00095e-96 1.23515e-11 2.05665e-106 -0.1413308 -0.01508391
## [5] 3.52268e-69 1.15993e-17 4.99213e-105 -0.0620120 -0.00843931
## [6] 1.39429e-68 4.41978e-06 6.45686e-103 -0.0553689 -0.00849132
## overlapping.genes
## <character>
## [1] MEG3
## [2] H19
## [3] PBX2, GPSM3, NOTCH4
## [4] C2, EHMT2, ZBTB12
## [5] XXbac-BPG252P9.10, F..
## [6] TAPBP, ZBTB22, DAXX
## -------
## seqinfo: 22 sequences from an unspecified genome; no seqlengthstop_nat_vs_fh$comp## $up_comp
## all up OR fisherPval
## Intergenic 260490 20354 1.6389953 1.011100e-300
## Gene_Associated 1196 88 1.1029990 3.852285e-01
## Gene_Associated_Cell_type_specific 1756 151 1.3078609 2.211273e-03
## NonGene_Associated 1278 78 0.9021801 4.015385e-01
## NonGene_Associated_Cell_type_specific 189 10 0.7755479 5.596110e-01
## Promoter_Associated 87082 4237 0.6587014 1.111818e-142
## Promoter_Associated_Cell_type_specific 5171 243 0.6818478 1.032831e-09
## Unclassified 28583 1338 0.6659552 1.291864e-50
## Unclassified_Cell_type_specific 33088 1641 0.7074446 1.019459e-43
## lowerCI upperCI
## Intergenic 1.5953349 1.6838209
## Gene_Associated 0.8771598 1.3718233
## Gene_Associated_Cell_type_specific 1.0989848 1.5468826
## NonGene_Associated 0.7078579 1.1353553
## NonGene_Associated_Cell_type_specific 0.3655737 1.4600240
## Promoter_Associated 0.6367916 0.6811792
## Promoter_Associated_Cell_type_specific 0.5966552 0.7762140
## Unclassified 0.6290371 0.7045582
## Unclassified_Cell_type_specific 0.6717719 0.7446424
##
## $dn_comp
## all dn OR fisherPval
## Intergenic 260490 57862 0.4590084 0.000000e+00
## Gene_Associated 1196 297 0.8360103 7.610858e-03
## Gene_Associated_Cell_type_specific 1756 299 0.5183472 2.606769e-28
## NonGene_Associated 1278 470 1.4745690 5.762451e-11
## NonGene_Associated_Cell_type_specific 189 57 1.0933052 5.723889e-01
## Promoter_Associated 87082 35917 2.1149516 0.000000e+00
## Promoter_Associated_Cell_type_specific 5171 1796 1.3526206 5.664800e-24
## Unclassified 28583 10571 1.5334094 3.189895e-237
## Unclassified_Cell_type_specific 33088 11321 1.3502172 1.714898e-131
## lowerCI upperCI
## Intergenic 0.4527757 0.4653379
## Gene_Associated 0.7306187 0.9544174
## Gene_Associated_Cell_type_specific 0.4560778 0.5875304
## NonGene_Associated 1.3129712 1.6544449
## NonGene_Associated_Cell_type_specific 0.7868289 1.5025900
## Promoter_Associated 2.0820337 2.1483256
## Promoter_Associated_Cell_type_specific 1.2761226 1.4332916
## Unclassified 1.4953597 1.5723528
## Unclassified_Cell_type_specific 1.3183531 1.3828251top_nat_vs_fh$cgi## $up_comp
## all up OR fisherPval lowerCI upperCI
## Island 142197 6193 0.5284279 0.000000e+00 0.5132676 0.5439757
## N_Shelf 19854 1702 1.3212312 6.285716e-25 1.2545267 1.3908383
## N_Shore 56505 3862 1.0215048 2.366507e-01 0.9859430 1.0581222
## OpenSea 138314 11973 1.5496045 3.543432e-261 1.5118521 1.5881996
## S_Shelf 17602 1529 1.3391778 1.250299e-24 1.2680567 1.4134990
## S_Shore 44361 2881 0.9602327 4.596665e-02 0.9224056 0.9993340
##
## $dn_comp
## all dn OR fisherPval lowerCI upperCI
## Island 142197 58256 2.4880777 0.000000e+00 2.4537473 2.5231063
## N_Shelf 19854 3979 0.6218922 3.177349e-165 0.6000964 0.6443693
## N_Shore 56505 15483 0.9488969 2.096818e-07 0.9301707 0.9679574
## OpenSea 138314 24971 0.4398311 0.000000e+00 0.4329302 0.4468371
## S_Shelf 17602 3523 0.6223082 6.170738e-147 0.5991907 0.6461736
## S_Shore 44361 12378 0.9774925 4.195087e-02 0.9561970 0.9992031# Allele
top_nat_vs_fh$dma$unmeth <- "T"
top_nat_vs_fh$dma$meth <- "C"
top_nat_vs_fh$fit$SE <- sqrt(top_nat_vs_fh$fit$s2.post) * top_nat_vs_fh$fit$stdev.unscaled
# Extract required columns from dma
top_nat_vs_fh_metal <-top_nat_vs_fh$dma[,c("Row.names", "meth", "unmeth", "AveExpr", "P.Value")]
head(top_nat_vs_fh_metal)## Row.names meth unmeth AveExpr P.Value
## 321265 cg20757019 C T -4.112831 9.137484e-37
## 138436 cg08260549 C T -4.154736 4.505529e-32
## 268273 cg16905586 C T -3.581648 4.612472e-32
## 34194 cg01931797 C T -4.476141 3.590088e-31
## 132599 cg07897831 C T -4.316226 3.744716e-31
## 286730 cg18233405 C T -4.609038 7.085398e-31# Convert fit outputs to dataframes
fitCE <- as.data.frame(top_nat_vs_fh$fit$coefficients)
fitCE$Row.names <- row.names(fitCE)
fitCE <- fitCE[,c("Row.names", "groupsFH")]
names(fitCE)[2]<- "coefficient"
fitSE <- as.data.frame(top_nat_vs_fh$fit$SE)
fitSE$Row.names <- row.names(fitSE)
fitSE <- fitSE[,c("Row.names", "groupsFH")]
names(fitSE)[2]<- "SE"
# Merge Datasets
top_nat_vs_fh_metal <- merge(top_nat_vs_fh_metal, fitCE)
top_nat_vs_fh_metal <- merge(top_nat_vs_fh_metal, fitSE)
# Number of effective participants
controls <- samplesheet[samplesheet$art == "NAT",]
cases <- samplesheet[samplesheet$art == "FH",]
ctrl <- nrow(controls)
cses <- nrow(cases)
Neff <- 4/((1/cses)+(1/ctrl))
top_nat_vs_fh_metal$N <- Neff
# Output for Meta-analysis
dir.create("NATvsFH")## Warning in dir.create("NATvsFH"): 'NATvsFH' already existswrite.table(top_nat_vs_fh_metal, file="NATvsFH/estill_top_nat_vs_fh_metal.tsv",sep="\t",quote=FALSE, row.names = FALSE) NAT vs FZ
In the comparison of NAT and FZ samples, there were ~130k probes with lower methylation and ~37k with higher methylation.
samplesheet <- subset(targets,art=="NAT" | art=="FZ")
samplesheet$Basename <- sapply(strsplit(samplesheet$Basename, "/"), "[[", 3)
groups <- factor(samplesheet$art,levels=c("NAT","FZ"))
sex <- factor(samplesheet$sex,levels=c("Male","Female"))
top_nat_vs_fz <- dm_analysis(samplesheet=samplesheet,
sex=sex,groups=groups,mx=Mval_flt,name="top_nat_vs_fz",
myann=myann, beta=beta_flt)## Your contrast returned 161460 individually significant probes. We recommend the default setting of pcutoff in dmrcate().## Fitting chr1...## Fitting chr2...## Fitting chr3...## Fitting chr4...## Fitting chr5...## Fitting chr6...## Fitting chr7...## Fitting chr8...## Fitting chr9...## Fitting chr10...## Fitting chr11...## Fitting chr12...## Fitting chr13...## Fitting chr14...## Fitting chr15...## Fitting chr16...## Fitting chr17...## Fitting chr18...## Fitting chr19...## Fitting chr20...## Fitting chr21...## Fitting chr22...## Demarcating regions...## Done!## snapshotDate(): 2022-04-26## see ?DMRcatedata and browseVignettes('DMRcatedata') for documentation## loading from cache
##
## RCircos.Core.Components initialized.
## Type ?RCircos.Reset.Plot.Parameters to see how to modify the core components.## Tiles plot may use more than one track. Please select correct area for next track if necessary.
head(top_nat_vs_fz$dma)## Row.names UCSC_RefGene_Name Regulatory_Feature_Group
## 321265 cg20757019 Unclassified
## 115170 cg06834912 FOXF1
## 51218 cg02916102 CYP11A1;CYP11A1;CYP11A1 Unclassified_Cell_type_specific
## 234473 cg14572967 CD72 Promoter_Associated
## 107155 cg06347499 SLC25A33 Promoter_Associated
## 267523 cg16849041 DOCK5;DOCK5
## Islands_Name Relation_to_Island logFC AveExpr t
## 321265 chr2:95872891-95873548 Island -1.091279 -4.055613 -18.75632
## 115170 chr16:86549069-86550512 N_Shore -3.069541 -4.131474 -18.43307
## 51218 chr15:74658038-74658574 Island -1.030696 -4.411861 -18.09825
## 234473 chr9:35616980-35617324 Island -1.044514 -4.649812 -17.81013
## 107155 chr1:9599350-9600444 Island -1.084891 -4.327655 -17.30676
## 267523 chr8:25041795-25042820 Island -1.120709 -4.494514 -17.28354
## P.Value adj.P.Val B
## 321265 1.475964e-31 6.181825e-26 60.55961
## 115170 4.731686e-31 9.908931e-26 59.45493
## 51218 1.602630e-30 2.237448e-25 58.29625
## 234473 4.629347e-30 4.847308e-25 57.28725
## 107155 3.026984e-29 2.305940e-24 55.49783
## 267523 3.303379e-29 2.305940e-24 55.41446head(top_nat_vs_fz$dmr)## GRanges object with 6 ranges and 8 metadata columns:
## seqnames ranges strand | no.cpgs min_smoothed_fdr
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## [1] chr11 2016513-2022386 * | 67 1.68907e-152
## [2] chr14 101290195-101294430 * | 36 9.00418e-121
## [3] chr6 33128825-33139083 * | 99 1.99962e-81
## [4] chr6 32062528-32065890 * | 64 3.46458e-115
## [5] chr20 57425515-57431303 * | 77 1.87244e-133
## [6] chr6 30709428-30713318 * | 81 2.16561e-117
## Stouffer HMFDR Fisher maxdiff meandiff
## <numeric> <numeric> <numeric> <numeric> <numeric>
## [1] 8.76255e-123 2.32134e-14 2.00092e-151 -0.0718761 -0.02698420
## [2] 1.78156e-155 7.31084e-10 3.85288e-151 -0.0649419 -0.04586854
## [3] 1.29387e-115 1.97337e-07 3.30658e-137 -0.0698468 -0.01863042
## [4] 5.12794e-136 2.76599e-06 2.17136e-128 -0.0978325 -0.04695187
## [5] 8.81867e-100 1.05680e-08 6.18733e-122 -0.0767897 -0.02795473
## [6] 1.05045e-94 1.26354e-13 9.92998e-117 0.0616602 -0.00598847
## overlapping.genes
## <character>
## [1] H19
## [2] MEG3
## [3] COL11A2
## [4] TNXB
## [5] GNAS, GNAS-AS1
## [6] XXbac-BPG252P9.10, F..
## -------
## seqinfo: 22 sequences from an unspecified genome; no seqlengthstop_nat_vs_fz$comp## $up_comp
## all up OR fisherPval
## Intergenic 260490 21532 0.9945769 6.395935e-01
## Gene_Associated 1196 97 0.9774378 8.747911e-01
## Gene_Associated_Cell_type_specific 1756 164 1.1415589 1.083776e-01
## NonGene_Associated 1278 120 1.1481786 1.544344e-01
## NonGene_Associated_Cell_type_specific 189 9 0.5536559 8.536962e-02
## Promoter_Associated 87082 7695 1.0944687 3.462952e-11
## Promoter_Associated_Cell_type_specific 5171 417 0.9711013 5.939167e-01
## Unclassified 28583 2267 0.9508406 2.618374e-02
## Unclassified_Cell_type_specific 33088 2385 0.8499997 6.076633e-14
## lowerCI upperCI
## Intergenic 0.9722541 1.0174487
## Gene_Associated 0.7856244 1.2041816
## Gene_Associated_Cell_type_specific 0.9656786 1.3419065
## NonGene_Associated 0.9430711 1.3871241
## NonGene_Associated_Cell_type_specific 0.2491090 1.0747988
## Promoter_Associated 1.0656908 1.1239400
## Promoter_Associated_Cell_type_specific 0.8759617 1.0742870
## Unclassified 0.9091991 0.9940831
## Unclassified_Cell_type_specific 0.8137227 0.8875630
##
## $dn_comp
## all dn OR fisherPval
## Intergenic 260490 68555 0.6142487 0.000000e+00
## Gene_Associated 1196 316 0.8268440 3.718360e-03
## Gene_Associated_Cell_type_specific 1756 415 0.7119838 6.394225e-10
## NonGene_Associated 1278 462 1.3054938 6.262394e-06
## NonGene_Associated_Cell_type_specific 189 63 1.1519492 3.836506e-01
## Promoter_Associated 87082 33728 1.6216945 0.000000e+00
## Promoter_Associated_Cell_type_specific 5171 1744 1.1747761 6.909257e-08
## Unclassified 28583 10078 1.2766985 1.264127e-78
## Unclassified_Cell_type_specific 33088 11413 1.2340985 1.518188e-66
## lowerCI upperCI
## Intergenic 0.6060578 0.6226030
## Gene_Associated 0.7246555 0.9414838
## Gene_Associated_Cell_type_specific 0.6360871 0.7956403
## NonGene_Associated 1.1619131 1.4653640
## NonGene_Associated_Cell_type_specific 0.8373661 1.5709959
## Promoter_Associated 1.5966581 1.6473250
## Promoter_Associated_Cell_type_specific 1.1078913 1.2453738
## Unclassified 1.2447373 1.3093261
## Unclassified_Cell_type_specific 1.2051145 1.2637916# Allele
top_nat_vs_fz$dma$unmeth <- "T"
top_nat_vs_fz$dma$meth <- "C"
top_nat_vs_fz$fit$SE <- sqrt(top_nat_vs_fz$fit$s2.post) * top_nat_vs_fz$fit$stdev.unscaled
# Extract required columns from dma
top_nat_vs_fz_metal <-top_nat_vs_fz$dma[,c("Row.names", "meth", "unmeth", "AveExpr", "P.Value")]
head(top_nat_vs_fh_metal)## Row.names meth unmeth AveExpr P.Value coefficient SE
## 1 cg00000029 C T 0.869973 9.234944e-03 -0.161668265 0.06064944
## 2 cg00000108 C T 3.881001 7.093742e-01 -0.049287803 0.13179294
## 3 cg00000109 C T 2.606871 9.680212e-01 0.007027419 0.17476157
## 4 cg00000165 C T -2.170537 1.789489e-13 -0.617283814 0.07028686
## 5 cg00000236 C T 1.896248 2.440345e-01 0.091707701 0.07816230
## 6 cg00000289 C T 1.294497 3.660847e-01 0.104310502 0.11477729
## N
## 1 80.69136
## 2 80.69136
## 3 80.69136
## 4 80.69136
## 5 80.69136
## 6 80.69136# Convert fit outputs to dataframes
fitCE <- as.data.frame(top_nat_vs_fz$fit$coefficients)
fitCE$Row.names <- row.names(fitCE)
fitCE <- fitCE[,c("Row.names", "groupsFZ")]
names(fitCE)[2]<- "coefficient"
fitSE <- as.data.frame(top_nat_vs_fz$fit$SE)
fitSE$Row.names <- row.names(fitSE)
fitSE <- fitSE[,c("Row.names", "groupsFZ")]
names(fitSE)[2]<- "SE"
# Merge Datasets
top_nat_vs_fz_metal <- merge(top_nat_vs_fz_metal, fitCE)
top_nat_vs_fz_metal <- merge(top_nat_vs_fz_metal, fitSE)
# Number of effective participants
controls <- samplesheet[samplesheet$art == "NAT",]
cases <- samplesheet[samplesheet$art == "FZ",]
ctrl <- nrow(controls)
cses <- nrow(cases)
Neff <- 4/((1/cses)+(1/ctrl))
top_nat_vs_fz_metal$N <- Neff
head(top_nat_vs_fz_metal)## Row.names meth unmeth AveExpr P.Value coefficient SE
## 1 cg00000029 C T 0.8431989 1.013482e-03 -0.22172813 0.06506647
## 2 cg00000108 C T 3.8848226 6.446446e-01 -0.06417093 0.13862586
## 3 cg00000109 C T 2.5417338 4.752919e-01 -0.13936884 0.19433401
## 4 cg00000165 C T -2.1715342 3.826280e-12 -0.60914453 0.07503300
## 5 cg00000236 C T 1.8770512 4.113591e-01 0.06337063 0.07674991
## 6 cg00000289 C T 1.1769801 2.844998e-01 -0.14552405 0.13508959
## N
## 1 80.69136
## 2 80.69136
## 3 80.69136
## 4 80.69136
## 5 80.69136
## 6 80.69136# Output for Meta-analysis
dir.create("NATvsFZ")## Warning in dir.create("NATvsFZ"): 'NATvsFZ' already existswrite.table(top_nat_vs_fz_metal, file="NATvsFZ/estill_top_nat_vs_fz_metal.tsv",sep="\t",quote=FALSE, row.names = FALSE)NAT vs IUI
In the comparison of NAT and IUI samples there were ~114k probes with reduced methylation and ~36k with higher methylation.
samplesheet <- subset(targets,art=="NAT" | art=="IUI")
groups <- factor(samplesheet$art,levels=c("NAT","IUI"))
samplesheet$Basename <- sapply(strsplit(samplesheet$Basename, "/"), "[[", 3)
sex <- factor(samplesheet$sex,levels=c("Male","Female"))
table(groups)## groups
## NAT IUI
## 43 18top_nat_vs_iui <- dm_analysis(samplesheet=samplesheet,
sex=sex,groups=groups,mx=Mval_flt,name="top_nat_vs_iui",
myann=myann, beta=beta_flt)## Your contrast returned 146451 individually significant probes. We recommend the default setting of pcutoff in dmrcate().## Fitting chr1...## Fitting chr2...## Fitting chr3...## Fitting chr4...## Fitting chr5...## Fitting chr6...## Fitting chr7...## Fitting chr8...## Fitting chr9...## Fitting chr10...## Fitting chr11...## Fitting chr12...## Fitting chr13...## Fitting chr14...## Fitting chr15...## Fitting chr16...## Fitting chr17...## Fitting chr18...## Fitting chr19...## Fitting chr20...## Fitting chr21...## Fitting chr22...## Demarcating regions...## Done!## snapshotDate(): 2022-04-26## see ?DMRcatedata and browseVignettes('DMRcatedata') for documentation## loading from cache
##
## RCircos.Core.Components initialized.
## Type ?RCircos.Reset.Plot.Parameters to see how to modify the core components.## Tiles plot may use more than one track. Please select correct area for next track if necessary.
head(top_nat_vs_iui$dma)## Row.names UCSC_RefGene_Name Regulatory_Feature_Group
## 321265 cg20757019 Unclassified
## 307250 cg19732469 PAPOLA Promoter_Associated
## 34194 cg01931797 KCTD21;USP35 Promoter_Associated
## 179618 cg10992925 SEMA3F Unclassified
## 196471 cg12159575 UHRF1;UHRF1 Promoter_Associated
## 66567 cg03817939 FAM176A;FAM176A
## Islands_Name Relation_to_Island logFC AveExpr
## 321265 chr2:95872891-95873548 Island -1.2757353 -3.916546
## 307250 chr14:96968154-96969371 Island -0.9731112 -4.543404
## 34194 chr11:77899564-77899990 Island -1.0722220 -4.325648
## 179618 chr3:50191971-50193130 Island -0.8632152 -3.170265
## 196471 chr19:4909262-4910256 S_Shore -1.2304453 -4.432795
## 66567 chr2:75787717-75788312 Island -1.1610466 -4.117368
## t P.Value adj.P.Val B
## 321265 -18.26873 8.474854e-27 3.549548e-21 49.58088
## 307250 -17.79415 3.394518e-26 7.108681e-21 48.29726
## 34194 -16.89573 5.033140e-25 7.026817e-20 45.78990
## 179618 -16.42057 2.175935e-24 2.278383e-19 44.42176
## 196471 -16.00132 8.097120e-24 5.380811e-19 43.18988
## 66567 -15.96941 8.956748e-24 5.380811e-19 43.09515head(top_nat_vs_iui$dmr)## GRanges object with 6 ranges and 8 metadata columns:
## seqnames ranges strand | no.cpgs min_smoothed_fdr
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## [1] chr11 2016513-2022386 * | 67 2.33404e-84
## [2] chr6 33280454-33292029 * | 213 2.27255e-55
## [3] chr6 33128825-33139083 * | 99 6.02829e-55
## [4] chr6 30709428-30713442 * | 82 1.01582e-114
## [5] chr6 31864574-31870600 * | 110 1.64731e-70
## [6] chr6 33165404-33173927 * | 110 3.54646e-65
## Stouffer HMFDR Fisher maxdiff meandiff
## <numeric> <numeric> <numeric> <numeric> <numeric>
## [1] 1.15722e-71 3.35875e-06 6.84715e-86 -0.0840745 -0.0300249
## [2] 3.83218e-61 3.11541e-06 8.82500e-85 -0.0675681 -0.0110376
## [3] 1.85908e-69 8.04822e-06 1.46553e-83 -0.0843595 -0.0201382
## [4] 2.43495e-59 4.89784e-15 1.27905e-82 -0.0590045 -0.0117001
## [5] 2.01328e-67 9.46142e-09 1.42241e-81 -0.0795821 -0.0143919
## [6] 7.88044e-56 1.67450e-06 7.04568e-69 -0.0548501 -0.0086269
## overlapping.genes
## <character>
## [1] H19
## [2] TAPBP, ZBTB22, DAXX
## [3] COL11A2
## [4] XXbac-BPG252P9.10, F..
## [5] C2, EHMT2, ZBTB12
## [6] RNY4P10, SLC39A7, HS..
## -------
## seqinfo: 22 sequences from an unspecified genome; no seqlengthstop_nat_vs_iui$comp## $up_comp
## all up OR fisherPval
## Intergenic 260490 28456 3.2404054 0.000000e+00
## Gene_Associated 1196 115 1.1959595 7.213805e-02
## Gene_Associated_Cell_type_specific 1756 198 1.4303963 5.396231e-06
## NonGene_Associated 1278 36 0.3249673 2.489278e-15
## NonGene_Associated_Cell_type_specific 189 12 0.7616499 4.260149e-01
## Promoter_Associated 87082 2522 0.2822090 0.000000e+00
## Promoter_Associated_Cell_type_specific 5171 209 0.4700197 6.503536e-33
## Unclassified 28583 1023 0.3991050 4.156244e-232
## Unclassified_Cell_type_specific 33088 1659 0.5723676 9.137611e-120
## lowerCI upperCI
## Intergenic 3.1475373 3.3365714
## Gene_Associated 0.9777470 1.4511051
## Gene_Associated_Cell_type_specific 1.2269800 1.6601539
## NonGene_Associated 0.2264949 0.4524899
## NonGene_Associated_Cell_type_specific 0.3861702 1.3643474
## Promoter_Associated 0.2706806 0.2941280
## Promoter_Associated_Cell_type_specific 0.4071007 0.5401039
## Unclassified 0.3741940 0.4252788
## Unclassified_Cell_type_specific 0.5437536 0.6021372
##
## $dn_comp
## all dn OR fisherPval
## Intergenic 260490 54503 0.4612899 0.000000e+00
## Gene_Associated 1196 273 0.8076596 1.882468e-03
## Gene_Associated_Cell_type_specific 1756 308 0.5799872 6.228126e-20
## NonGene_Associated 1278 489 1.6963820 3.873139e-19
## NonGene_Associated_Cell_type_specific 189 55 1.1214789 4.608349e-01
## Promoter_Associated 87082 34787 2.1847449 0.000000e+00
## Promoter_Associated_Cell_type_specific 5171 1752 1.4064143 1.231473e-29
## Unclassified 28583 9833 1.4744142 1.565491e-189
## Unclassified_Cell_type_specific 33088 10221 1.2434415 5.204549e-67
## lowerCI upperCI
## Intergenic 0.4549103 0.4677646
## Gene_Associated 0.7028884 0.9255836
## Gene_Associated_Cell_type_specific 0.5111150 0.6564828
## NonGene_Associated 1.5119872 1.9016699
## NonGene_Associated_Cell_type_specific 0.8039385 1.5460207
## Promoter_Associated 2.1506916 2.2196177
## Promoter_Associated_Cell_type_specific 1.3263686 1.4908111
## Unclassified 1.4372281 1.5124749
## Unclassified_Cell_type_specific 1.2133073 1.2741799# Allele
top_nat_vs_iui$dma$unmeth <- "T"
top_nat_vs_iui$dma$meth <- "C"
top_nat_vs_iui$fit$SE <- sqrt(top_nat_vs_iui$fit$s2.post) * top_nat_vs_iui$fit$stdev.unscaled
# Extract required columns from dma
top_nat_vs_iui_metal <-top_nat_vs_iui$dma[,c("Row.names", "meth", "unmeth", "AveExpr", "P.Value")]
head(top_nat_vs_iui_metal)## Row.names meth unmeth AveExpr P.Value
## 321265 cg20757019 C T -3.916546 8.474854e-27
## 307250 cg19732469 C T -4.543404 3.394518e-26
## 34194 cg01931797 C T -4.325648 5.033140e-25
## 179618 cg10992925 C T -3.170265 2.175935e-24
## 196471 cg12159575 C T -4.432795 8.097120e-24
## 66567 cg03817939 C T -4.117368 8.956748e-24# Convert fit outputs to dataframes
fitCE <- as.data.frame(top_nat_vs_iui$fit$coefficients)
fitCE$Row.names <- row.names(fitCE)
fitCE <- fitCE[,c("Row.names", "groupsIUI")]
names(fitCE)[2]<- "coefficient"
fitSE <- as.data.frame(top_nat_vs_iui$fit$SE)
fitSE$Row.names <- row.names(fitSE)
fitSE <- fitSE[,c("Row.names", "groupsIUI")]
names(fitSE)[2]<- "SE"
# Merge Datasets
top_nat_vs_iui_metal <- merge(top_nat_vs_iui_metal, fitCE)
top_nat_vs_iui_metal <- merge(top_nat_vs_iui_metal, fitSE)
# Number of effective participants
controls <- samplesheet[samplesheet$art == "NAT",]
cases <- samplesheet[samplesheet$art == "IUI",]
ctrl <- nrow(controls)
cses <- nrow(cases)
Neff <- 4/((1/cses)+(1/ctrl))
top_nat_vs_iui_metal$N <- Neff
head(top_nat_vs_iui_metal)## Row.names meth unmeth AveExpr P.Value coefficient SE N
## 1 cg00000029 C T 0.9362728 2.340375e-01 -0.09510378 0.07914766 50.7541
## 2 cg00000108 C T 3.9675487 6.851662e-02 0.26414316 0.14251054 50.7541
## 3 cg00000109 C T 2.6716717 8.304815e-02 0.37988206 0.21567226 50.7541
## 4 cg00000165 C T -2.0535553 5.577219e-09 -0.61558404 0.09121612 50.7541
## 5 cg00000236 C T 1.9058535 2.497057e-02 0.23666167 0.10303134 50.7541
## 6 cg00000289 C T 1.3164596 1.890663e-02 0.33733448 0.13998240 50.7541# Output for Meta-analysis
dir.create("NATvsIUI")## Warning in dir.create("NATvsIUI"): 'NATvsIUI' already existswrite.table(top_nat_vs_iui_metal, file="NATvsIUI/estill_top_nat_vs_iui_metal.tsv",sep="\t",quote=FALSE, row.names = FALSE)FH vs FZ
In the comparison of FH and FZ, only ~13k probes were differentially methylated with roughly half lower and higher. This suggests that freezing has only a small effect on methylation.
samplesheet <- subset(targets,art=="FH" | art=="FZ")
groups <- factor(samplesheet$art,levels=c("FH","FZ"))
samplesheet$Basename <- sapply(strsplit(samplesheet$Basename, "/"), "[[", 3)
sex <- factor(samplesheet$sex,levels=c("Male","Female"))
table(groups)## groups
## FH FZ
## 38 38top_fh_vs_fz <- dm_analysis(samplesheet=samplesheet,
sex=sex,groups=groups,mx=Mval_flt,name="top_fh_vs_fz",
myann=myann, beta=beta_flt)## Your contrast returned 12634 individually significant probes. We recommend the default setting of pcutoff in dmrcate().## Fitting chr1...## Fitting chr2...## Fitting chr3...## Fitting chr4...## Fitting chr5...## Fitting chr6...## Fitting chr7...## Fitting chr8...## Fitting chr9...## Fitting chr10...## Fitting chr11...## Fitting chr12...## Fitting chr13...## Fitting chr14...## Fitting chr15...## Fitting chr16...## Fitting chr17...## Fitting chr18...## Fitting chr19...## Fitting chr20...## Fitting chr21...## Fitting chr22...## Demarcating regions...## Done!## snapshotDate(): 2022-04-26## see ?DMRcatedata and browseVignettes('DMRcatedata') for documentation## loading from cache
##
## RCircos.Core.Components initialized.
## Type ?RCircos.Reset.Plot.Parameters to see how to modify the core components.
head(top_fh_vs_fz$dma)## Row.names UCSC_RefGene_Name
## 216964 cg13601636 FCAR;FCAR;FCAR;FCAR;FCAR;FCAR;FCAR;FCAR;FCAR
## 175512 cg10704137
## 338404 cg22078451 PDE4D;PDE4D;PDE4D
## 201535 cg12538597
## 274520 cg17353079 HSPA1L
## 102728 cg06039171 TUBB1
## Regulatory_Feature_Group Islands_Name Relation_to_Island
## 216964 OpenSea
## 175512 chr6:25732692-25732957 N_Shore
## 338404 chr5:58334836-58335881 Island
## 201535 OpenSea
## 274520 chr6:31782872-31785190 N_Shelf
## 102728 chr20:57599202-57599408 S_Shore
## logFC AveExpr t P.Value adj.P.Val B
## 216964 -0.7080566 1.5973963 -9.328265 2.489384e-14 1.042636e-08 21.57487
## 175512 -0.5687155 1.6710956 -9.061803 8.175166e-14 1.712015e-08 20.48644
## 338404 -0.7785473 -3.3146330 -8.800331 2.629902e-13 3.671632e-08 19.41488
## 201535 -0.5784615 1.3167580 -8.230334 3.361881e-12 3.520167e-07 17.07173
## 274520 -0.5035957 0.8537704 -8.112780 5.682799e-12 4.549535e-07 16.58805
## 102728 -0.3525487 1.6221761 -8.042838 7.764572e-12 4.549535e-07 16.30032head(top_fh_vs_fz$dmr)## GRanges object with 6 ranges and 8 metadata columns:
## seqnames ranges strand | no.cpgs min_smoothed_fdr
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## [1] chr6 32941825-32942808 * | 11 1.20002e-30
## [2] chr11 65190180-65191707 * | 11 4.30115e-22
## [3] chr16 3130216-3131034 * | 4 7.65965e-13
## [4] chr6 31777883-31780068 * | 34 5.45746e-29
## [5] chr16 66412840-66413098 * | 3 3.31368e-18
## [6] chr10 13513915-13516215 * | 11 4.88422e-10
## Stouffer HMFDR Fisher maxdiff meandiff
## <numeric> <numeric> <numeric> <numeric> <numeric>
## [1] 1.01751e-13 2.36204e-03 5.86549e-13 0.0569563 0.0380371
## [2] 1.08006e-10 5.72151e-04 4.88683e-12 0.1313849 0.0474294
## [3] 2.97525e-06 1.47212e-05 5.57244e-10 -0.0498332 -0.0304330
## [4] 4.76576e-02 1.30723e-05 1.41836e-09 -0.0793435 -0.0128844
## [5] 2.86453e-06 2.60455e-06 1.30131e-08 -0.0782088 -0.0390186
## [6] 1.87329e-09 1.31059e-02 8.12315e-08 -0.0255888 -0.0174353
## overlapping.genes
## <character>
## [1] BRD2
## [2] NEAT1
## [3] IL32, RP11-473M20.9
## [4] HSPA1L
## [5] CDH5
## [6] BEND7
## -------
## seqinfo: 22 sequences from an unspecified genome; no seqlengthstop_fh_vs_fz$comp## $up_comp
## all up OR fisherPval
## Intergenic 260490 1820 0.2586729 0.000000e+00
## Gene_Associated 1196 3 0.1722384 7.254003e-05
## Gene_Associated_Cell_type_specific 1756 3 0.1170562 5.413742e-08
## NonGene_Associated 1278 33 1.8243747 1.932610e-03
## NonGene_Associated_Cell_type_specific 189 0 0.0000000 1.214091e-01
## Promoter_Associated 87082 2804 3.4066003 0.000000e+00
## Promoter_Associated_Cell_type_specific 5171 120 1.6439094 6.697009e-07
## Unclassified 28583 690 1.7888522 5.365777e-40
## Unclassified_Cell_type_specific 33088 540 1.1527574 2.050668e-03
## lowerCI upperCI
## Intergenic 0.24460117 0.2734435
## Gene_Associated 0.03548727 0.5047895
## Gene_Associated_Cell_type_specific 0.02413590 0.3428093
## NonGene_Associated 1.24893514 2.5784667
## NonGene_Associated_Cell_type_specific 0.00000000 1.3529010
## Promoter_Associated 3.23498171 3.5867641
## Promoter_Associated_Cell_type_specific 1.35761004 1.9741652
## Unclassified 1.64847851 1.9387259
## Unclassified_Cell_type_specific 1.05252172 1.2604956
##
## $dn_comp
## all dn OR fisherPval
## Intergenic 260490 4829 1.6500975 3.822439e-77
## Gene_Associated 1196 8 0.4185208 7.268863e-03
## Gene_Associated_Cell_type_specific 1756 22 0.7891971 3.362284e-01
## NonGene_Associated 1278 20 0.9897667 1.000000e+00
## NonGene_Associated_Cell_type_specific 189 3 1.0041677 1.000000e+00
## Promoter_Associated 87082 1077 0.7368264 5.438609e-21
## Promoter_Associated_Cell_type_specific 5171 60 0.7284125 1.343611e-02
## Unclassified 28583 269 0.5742200 6.910524e-22
## Unclassified_Cell_type_specific 33088 333 0.6135198 2.067397e-20
## lowerCI upperCI
## Intergenic 1.5621645 1.7436471
## Gene_Associated 0.1802020 0.8278849
## Gene_Associated_Cell_type_specific 0.4929214 1.1997431
## NonGene_Associated 0.6020711 1.5364645
## NonGene_Associated_Cell_type_specific 0.2051656 2.9815518
## Promoter_Associated 0.6893320 0.7869991
## Promoter_Associated_Cell_type_specific 0.5544006 0.9404468
## Unclassified 0.5060596 0.6491912
## Unclassified_Cell_type_specific 0.5474872 0.6854813# Allele
top_fh_vs_fz$dma$unmeth <- "T"
top_fh_vs_fz$dma$meth <- "C"
top_fh_vs_fz$fit$SE <- sqrt(top_fh_vs_fz$fit$s2.post) * top_fh_vs_fz$fit$stdev.unscaled
# Extract required columns from dma
top_fh_vs_fz_metal <-top_fh_vs_fz$dma[,c("Row.names", "meth", "unmeth", "AveExpr", "P.Value")]
head(top_fh_vs_fz_metal)## Row.names meth unmeth AveExpr P.Value
## 216964 cg13601636 C T 1.5973963 2.489384e-14
## 175512 cg10704137 C T 1.6710956 8.175166e-14
## 338404 cg22078451 C T -3.3146330 2.629902e-13
## 201535 cg12538597 C T 1.3167580 3.361881e-12
## 274520 cg17353079 C T 0.8537704 5.682799e-12
## 102728 cg06039171 C T 1.6221761 7.764572e-12# Convert fit outputs to dataframes
fitCE <- as.data.frame(top_fh_vs_fz$fit$coefficients)
fitCE$Row.names <- row.names(fitCE)
fitCE <- fitCE[,c("Row.names", "groupsFZ")]
names(fitCE)[2]<- "coefficient"
fitSE <- as.data.frame(top_fh_vs_fz$fit$SE)
fitSE$Row.names <- row.names(fitSE)
fitSE <- fitSE[,c("Row.names", "groupsFZ")]
names(fitSE)[2]<- "SE"
# Merge Datasets
top_fh_vs_fz_metal <- merge(top_fh_vs_fz_metal, fitCE)
top_fh_vs_fz_metal <- merge(top_fh_vs_fz_metal, fitSE)
# Number of effective participants
controls <- samplesheet[samplesheet$art == "FH",]
cases <- samplesheet[samplesheet$art == "FZ",]
ctrl <- nrow(controls)
cses <- nrow(cases)
Neff <- 4/((1/cses)+(1/ctrl))
head(top_fh_vs_fz_metal)## Row.names meth unmeth AveExpr P.Value coefficient SE
## 1 cg00000029 C T 0.7419975 0.32431399 -0.0590630096 0.05954547
## 2 cg00000108 C T 3.8671746 0.97247941 0.0049337470 0.14255410
## 3 cg00000109 C T 2.5742636 0.39394888 -0.1411799797 0.16469512
## 4 cg00000165 C T -2.5044777 0.98992131 -0.0007714976 0.06087894
## 5 cg00000236 C T 1.9389689 0.46905695 -0.0394226218 0.05418422
## 6 cg00000289 C T 1.2472419 0.04618534 -0.2508111168 0.12379734top_fh_vs_fz_metal$N <- Neff
head(top_fh_vs_fz_metal)## Row.names meth unmeth AveExpr P.Value coefficient SE N
## 1 cg00000029 C T 0.7419975 0.32431399 -0.0590630096 0.05954547 76
## 2 cg00000108 C T 3.8671746 0.97247941 0.0049337470 0.14255410 76
## 3 cg00000109 C T 2.5742636 0.39394888 -0.1411799797 0.16469512 76
## 4 cg00000165 C T -2.5044777 0.98992131 -0.0007714976 0.06087894 76
## 5 cg00000236 C T 1.9389689 0.46905695 -0.0394226218 0.05418422 76
## 6 cg00000289 C T 1.2472419 0.04618534 -0.2508111168 0.12379734 76# Output for Meta-analysis
dir.create("misc")## Warning in dir.create("misc"): 'misc' already existswrite.table(top_fh_vs_fz_metal, file="misc/top_fh_vs_fz_metal.tsv",sep="\t",quote=FALSE, row.names = FALSE)NAT vs FH/FZ
Because the effect of embryo freezing was found to be very small, we compared the NAT samples to all IVF samples (union of FH and FZ). There were ~200k probes with altered methylation ~157k with lower methylation and ~50k higher. This result points to a major effect of IVF on infant genome methylation.
samplesheet <- subset(targets,art=="NAT" | art=="FH" | art=="FZ")
samplesheet$Basename <- sapply(strsplit(samplesheet$Basename, "/"), "[[", 3)
samplesheet$art <- gsub("FH","FX",samplesheet$art)
samplesheet$art <- gsub("FZ","FX",samplesheet$art)
groups <- factor(samplesheet$art,levels=c("NAT","FX"))
sex <- factor(samplesheet$sex,levels=c("Male","Female"))
table(groups)## groups
## NAT FX
## 43 76top_nat_vs_fx <- dm_analysis(samplesheet=samplesheet,
sex=sex,groups=groups,mx=Mval_flt,name="top_nat_vs_fx",
myann=myann, beta=beta_flt)## Your contrast returned 200998 individually significant probes. We recommend the default setting of pcutoff in dmrcate().## Fitting chr1...## Fitting chr2...## Fitting chr3...## Fitting chr4...## Fitting chr5...## Fitting chr6...## Fitting chr7...## Fitting chr8...## Fitting chr9...## Fitting chr10...## Fitting chr11...## Fitting chr12...## Fitting chr13...## Fitting chr14...## Fitting chr15...## Fitting chr16...## Fitting chr17...## Fitting chr18...## Fitting chr19...## Fitting chr20...## Fitting chr21...## Fitting chr22...## Demarcating regions...## Done!## snapshotDate(): 2022-04-26## see ?DMRcatedata and browseVignettes('DMRcatedata') for documentation## loading from cache
##
## RCircos.Core.Components initialized.
## Type ?RCircos.Reset.Plot.Parameters to see how to modify the core components.## Tiles plot may use more than one track. Please select correct area for next track if necessary.
head(top_nat_vs_fx$dma)## Row.names UCSC_RefGene_Name Regulatory_Feature_Group
## 321265 cg20757019 Unclassified
## 132599 cg07897831 CASKIN2;TSEN54 Unclassified
## 26062 cg01447112 Unclassified_Cell_type_specific
## 234473 cg14572967 CD72 Promoter_Associated
## 286730 cg18233405 TSPYL5;TSPYL5 Promoter_Associated
## 51218 cg02916102 CYP11A1;CYP11A1;CYP11A1 Unclassified_Cell_type_specific
## Islands_Name Relation_to_Island logFC AveExpr
## 321265 chr2:95872891-95873548 Island -1.1542534 -4.280613
## 132599 chr17:73511016-73513176 Island -0.8721412 -4.455888
## 26062 chr7:6703503-6704075 Island -1.0002478 -3.896946
## 234473 chr9:35616980-35617324 Island -1.0414923 -4.824333
## 286730 chr8:98289604-98290404 Island -0.9083285 -4.751098
## 51218 chr15:74658038-74658574 Island -1.0045920 -4.570302
## t P.Value adj.P.Val B
## 321265 -23.23637 1.890041e-46 7.916115e-41 94.18962
## 132599 -21.53238 3.213379e-43 4.488935e-38 87.04881
## 26062 -21.53224 3.215316e-43 4.488935e-38 87.04823
## 234473 -21.18933 1.498966e-42 1.569541e-37 85.56578
## 286730 -20.87168 6.320055e-42 5.294095e-37 84.17874
## 51218 -20.03054 3.033064e-40 2.117246e-35 80.44118head(top_nat_vs_fx$dmr)## GRanges object with 6 ranges and 8 metadata columns:
## seqnames ranges strand | no.cpgs min_smoothed_fdr
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## [1] chr6 33128825-33143356 * | 128 1.11204e-120
## [2] chr14 101290195-101294430 * | 36 7.01711e-187
## [3] chr11 2016513-2022386 * | 67 2.36564e-206
## [4] chr6 33280052-33291947 * | 223 8.47883e-68
## [5] chr6 32153540-32167900 * | 147 6.35555e-82
## [6] chr6 30709428-30713442 * | 82 1.40950e-183
## Stouffer HMFDR Fisher maxdiff meandiff
## <numeric> <numeric> <numeric> <numeric> <numeric>
## [1] 1.16133e-217 8.58765e-14 2.76147e-268 -0.0815729 -0.01769404
## [2] 2.00889e-244 1.80326e-17 4.53914e-244 -0.0639395 -0.04354390
## [3] 2.16797e-176 1.74839e-13 2.11000e-228 -0.0745761 -0.02591185
## [4] 1.31829e-147 1.18523e-09 7.71242e-207 -0.0479277 -0.00667628
## [5] 2.44731e-145 2.17126e-09 2.84012e-204 -0.0860044 -0.00960421
## [6] 2.34049e-155 6.35232e-23 8.24963e-202 -0.0571755 -0.00721317
## overlapping.genes
## <character>
## [1] COL11A2
## [2] MEG3
## [3] H19
## [4] TAPBP, ZBTB22, DAXX
## [5] PBX2, XXbac-BPG300A1..
## [6] XXbac-BPG252P9.10, F..
## -------
## seqinfo: 22 sequences from an unspecified genome; no seqlengthstop_nat_vs_fx$comp## $up_comp
## all up OR fisherPval
## Intergenic 260490 33806 1.6102220 0.000000e+00
## Gene_Associated 1196 139 1.0348267 6.805632e-01
## Gene_Associated_Cell_type_specific 1756 250 1.3077876 1.326075e-04
## NonGene_Associated 1278 121 0.8224476 4.150422e-02
## NonGene_Associated_Cell_type_specific 189 18 0.8282091 5.643006e-01
## Promoter_Associated 87082 7244 0.6620668 9.741902e-225
## Promoter_Associated_Cell_type_specific 5171 443 0.7347516 1.537948e-10
## Unclassified 28583 2361 0.6931152 1.370782e-67
## Unclassified_Cell_type_specific 33088 2846 0.7238278 8.762384e-62
## lowerCI upperCI
## Intergenic 1.5766237 1.6446585
## Gene_Associated 0.8605376 1.2364009
## Gene_Associated_Cell_type_specific 1.1389347 1.4966004
## NonGene_Associated 0.6761022 0.9927888
## NonGene_Associated_Cell_type_specific 0.4792844 1.3488251
## Promoter_Associated 0.6448594 0.6797097
## Promoter_Associated_Cell_type_specific 0.6647817 0.8104786
## Unclassified 0.6634871 0.7237932
## Unclassified_Cell_type_specific 0.6954255 0.7531745
##
## $dn_comp
## all dn OR fisherPval
## Intergenic 260490 79297 0.4928579 0.000000e+00
## Gene_Associated 1196 396 0.8528612 9.764957e-03
## Gene_Associated_Cell_type_specific 1756 464 0.6179084 4.910981e-20
## NonGene_Associated 1278 589 1.4753409 7.353526e-12
## NonGene_Associated_Cell_type_specific 189 74 1.1092534 4.974143e-01
## Promoter_Associated 87082 43493 2.0039456 0.000000e+00
## Promoter_Associated_Cell_type_specific 5171 2256 1.3390062 1.017690e-24
## Unclassified 28583 12993 1.4768500 5.347785e-216
## Unclassified_Cell_type_specific 33088 14208 1.3274257 6.233838e-130
## lowerCI upperCI
## Intergenic 0.4865278 0.4993074
## Gene_Associated 0.7540807 0.9634042
## Gene_Associated_Cell_type_specific 0.5544157 0.6877248
## NonGene_Associated 1.3191801 1.6495912
## NonGene_Associated_Cell_type_specific 0.8167827 1.4985155
## Promoter_Associated 1.9738869 2.0344905
## Promoter_Associated_Cell_type_specific 1.2664056 1.4156959
## Unclassified 1.4415209 1.5131644
## Unclassified_Cell_type_specific 1.2975010 1.3580112# Allele
top_nat_vs_fx$dma$unmeth <- "T"
top_nat_vs_fx$dma$meth <- "C"
top_nat_vs_fx$fit$SE <- sqrt(top_nat_vs_fx$fit$s2.post) * top_nat_vs_fx$fit$stdev.unscaled
# Extract required columns from dma
top_nat_vs_fx_metal <-top_nat_vs_fx$dma[,c("Row.names", "meth", "unmeth", "AveExpr", "P.Value")]
head(top_nat_vs_fx_metal)## Row.names meth unmeth AveExpr P.Value
## 321265 cg20757019 C T -4.280613 1.890041e-46
## 132599 cg07897831 C T -4.455888 3.213379e-43
## 26062 cg01447112 C T -3.896946 3.215316e-43
## 234473 cg14572967 C T -4.824333 1.498966e-42
## 286730 cg18233405 C T -4.751098 6.320055e-42
## 51218 cg02916102 C T -4.570302 3.033064e-40# Convert fit outputs to dataframes
fitCE <- as.data.frame(top_nat_vs_fx$fit$coefficients)
fitCE$Row.names <- row.names(fitCE)
fitCE <- fitCE[,c("Row.names", "groupsFX")]
names(fitCE)[2]<- "coefficient"
fitSE <- as.data.frame(top_nat_vs_fx$fit$SE)
fitSE$Row.names <- row.names(fitSE)
fitSE <- fitSE[,c("Row.names", "groupsFX")]
names(fitSE)[2]<- "SE"
# Merge Datasets
top_nat_vs_fx_metal <- merge(top_nat_vs_fx_metal, fitCE)
top_nat_vs_fx_metal <- merge(top_nat_vs_fx_metal, fitSE)
# Number of effective participants
controls <- samplesheet[samplesheet$art == "NAT",]
cases <- samplesheet[samplesheet$art == "FX",]
ctrl <- nrow(controls)
cses <- nrow(cases)
Neff <- 4/((1/cses)+(1/ctrl))
top_nat_vs_fx_metal$N <- Neff
head(top_nat_vs_fx_metal)## Row.names meth unmeth AveExpr P.Value coefficient SE
## 1 cg00000029 C T 0.8199947 3.082849e-04 -0.195597481 0.05264434
## 2 cg00000108 C T 3.8778865 7.233735e-01 -0.041942572 0.11822221
## 3 cg00000109 C T 2.5742900 7.930181e-01 -0.040632665 0.15451252
## 4 cg00000165 C T -2.2775130 9.944203e-19 -0.620548539 0.05910532
## 5 cg00000236 C T 1.9033566 1.800815e-01 0.081432953 0.06039714
## 6 cg00000289 C T 1.2394117 9.485475e-01 -0.007033758 0.10877248
## N
## 1 109.8487
## 2 109.8487
## 3 109.8487
## 4 109.8487
## 5 109.8487
## 6 109.8487# Output for Meta-analysis
dir.create("NATvsICSI")
write.table(top_nat_vs_fx_metal, file="NATvsICSI/estill_top_nat_vs_fx_metal.tsv",sep="\t",quote=FALSE, row.names = FALSE)IUI vs FH/FZ
We know that IVF conceived infants have a major shift in genome methylation, but we don’t know exactly whether it is caused by the IVF process itself. To eliminate whether it might be potentially caused by subfertility we looked at whether the differences between IUI and FH+FZ were large.
We observe only ~1500 differentiallymethylated probes which is drastically smaller than the NAT vs FH/FZ comparison which suggests that the process of IVF accounts for only a small fraction (1%) of the variance in methylation. The difference in methylomes could be due to other factors such as subfertility or even the effect of hormone therapy.
samplesheet <- subset(targets,art=="IUI" | art=="FH" | art=="FZ")
samplesheet$Basename <- sapply(strsplit(samplesheet$Basename, "/"), "[[", 3)
samplesheet$art <- gsub("FH","FX",samplesheet$art)
samplesheet$art <- gsub("FZ","FX",samplesheet$art)
groups <- factor(samplesheet$art,levels=c("IUI","FX"))
sex <- factor(samplesheet$sex,levels=c("Male","Female"))
table(groups)## groups
## IUI FX
## 18 76top_iui_vs_fx <- dm_analysis(samplesheet=samplesheet,
sex=sex,groups=groups,mx=Mval_flt,name="top_iui_vs_fx",
myann=myann, beta=beta_flt)## Your contrast returned 2521 individually significant probes. We recommend the default setting of pcutoff in dmrcate().## Fitting chr1...## Fitting chr2...## Fitting chr3...## Fitting chr4...## Fitting chr5...## Fitting chr6...## Fitting chr7...## Fitting chr8...## Fitting chr9...## Fitting chr10...## Fitting chr11...## Fitting chr12...## Fitting chr13...## Fitting chr14...## Fitting chr15...## Fitting chr16...## Fitting chr17...## Fitting chr18...## Fitting chr19...## Fitting chr20...## Fitting chr21...## Fitting chr22...## Demarcating regions...## Done!## snapshotDate(): 2022-04-26## see ?DMRcatedata and browseVignettes('DMRcatedata') for documentation## loading from cache
##
## RCircos.Core.Components initialized.
## Type ?RCircos.Reset.Plot.Parameters to see how to modify the core components.
head(top_iui_vs_fx$dma)## Row.names UCSC_RefGene_Name Regulatory_Feature_Group
## 276287 cg17476421 ZNF14 Promoter_Associated
## 171967 cg10471644 TPI1;TPI1;TPI1 Promoter_Associated
## 180034 cg11022944 PGGT1B Promoter_Associated_Cell_type_specific
## 311754 cg20040772 MEPCE;ZCWPW1 Promoter_Associated
## 321952 cg20811730 PRPF3 Promoter_Associated
## 152708 cg09197288 IFI27L1;IFI27L1
## Islands_Name Relation_to_Island logFC AveExpr
## 276287 chr19:19843482-19843943 S_Shore 0.4699052 -4.336800
## 171967 chr12:6976392-6977393 Island 0.4729359 -4.653258
## 180034 chr5:114598301-114598657 Island 0.6656076 -2.987914
## 311754 chr7:100025804-100028002 Island 0.4475989 -4.277220
## 321952 chr1:150293835-150294140 Island 0.5156218 -3.718327
## 152708 chr14:94547001-94547665 S_Shelf -0.4183156 1.764911
## t P.Value adj.P.Val B
## 276287 6.994529 3.540484e-10 0.0001482872 12.32627
## 171967 6.826697 7.802779e-10 0.0001634031 11.62449
## 180034 6.701950 1.398501e-09 0.0001845657 11.10597
## 311754 6.652268 1.762667e-09 0.0001845657 10.90024
## 321952 6.583572 2.425129e-09 0.0002031448 10.61656
## 152708 -6.501013 3.553164e-09 0.0002480304 10.27687head(top_iui_vs_fx$dmr)## GRanges object with 6 ranges and 8 metadata columns:
## seqnames ranges strand | no.cpgs min_smoothed_fdr
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## [1] chr7 94284258-94287211 * | 88 2.83764e-86
## [2] chr11 49872169-49872414 * | 3 5.29872e-13
## [3] chr7 4353644-4353846 * | 2 4.09046e-09
## [4] chr4 175750109-175750802 * | 12 2.31688e-16
## [5] chr9 68298597-68298755 * | 2 6.41631e-09
## [6] chr15 52044314-52044428 * | 2 6.33330e-10
## Stouffer HMFDR Fisher maxdiff meandiff
## <numeric> <numeric> <numeric> <numeric> <numeric>
## [1] 8.20506e-04 0.011894654 8.81104e-08 -0.0548386 -0.0172341
## [2] 3.82831e-05 0.000756933 6.30250e-05 -0.0826613 -0.0506069
## [3] 1.76723e-04 0.003208580 3.31587e-04 -0.0605635 -0.0434740
## [4] 5.74207e-05 0.064610535 4.13220e-04 -0.0473785 -0.0228923
## [5] 1.86762e-03 0.000510066 7.19061e-04 -0.0706135 -0.0218884
## [6] 1.65259e-02 0.000510364 1.67122e-03 -0.0392921 -0.0169011
## overlapping.genes
## <character>
## [1] PEG10, SGCE
## [2] RP11-163O19.1
## [3] <NA>
## [4] GLRA3
## [5] <NA>
## [6] TMOD2
## -------
## seqinfo: 22 sequences from an unspecified genome; no seqlengthstop_iui_vs_fx$comp## $up_comp
## all up OR fisherPval
## Intergenic 260490 423 0.2590504 8.025473e-133
## Gene_Associated 1196 1 0.2470286 2.018750e-01
## Gene_Associated_Cell_type_specific 1756 1 0.1679785 3.621215e-02
## NonGene_Associated 1278 10 2.3424841 1.272474e-02
## NonGene_Associated_Cell_type_specific 189 0 0.0000000 1.000000e+00
## Promoter_Associated 87082 710 3.8820319 2.508466e-133
## Promoter_Associated_Cell_type_specific 5171 32 1.8617097 1.458654e-03
## Unclassified 28583 144 1.5545194 2.270687e-06
## Unclassified_Cell_type_specific 33088 90 0.7936967 3.341609e-02
## lowerCI upperCI
## Intergenic 0.230596520 0.2906456
## Gene_Associated 0.006247730 1.3812551
## Gene_Associated_Cell_type_specific 0.004250824 0.9386759
## NonGene_Associated 1.118138128 4.3357652
## NonGene_Associated_Cell_type_specific 0.000000000 5.8355480
## Promoter_Associated 3.491615473 4.3162920
## Promoter_Associated_Cell_type_specific 1.266909195 2.6438562
## Unclassified 1.298638036 1.8491535
## Unclassified_Cell_type_specific 0.633641095 0.9834590
##
## $dn_comp
## all dn OR fisherPval
## Intergenic 260490 801 1.5775033 2.631804e-12
## Gene_Associated 1196 1 0.3143029 3.880038e-01
## Gene_Associated_Cell_type_specific 1756 3 0.6430638 6.394443e-01
## NonGene_Associated 1278 3 0.8851652 1.000000e+00
## NonGene_Associated_Cell_type_specific 189 0 0.0000000 1.000000e+00
## Promoter_Associated 87082 181 0.7417114 1.800703e-04
## Promoter_Associated_Cell_type_specific 5171 17 1.2450300 3.399112e-01
## Unclassified 28583 48 0.6164304 5.332098e-04
## Unclassified_Cell_type_specific 33088 56 0.6187929 2.269454e-04
## lowerCI upperCI
## Intergenic 1.381530991 1.8049009
## Gene_Associated 0.007946673 1.7581018
## Gene_Associated_Cell_type_specific 0.132284522 1.8872588
## NonGene_Associated 0.182015594 2.5994408
## NonGene_Associated_Cell_type_specific 0.000000000 7.4275506
## Promoter_Associated 0.628855788 0.8708114
## Promoter_Associated_Cell_type_specific 0.722060580 2.0047091
## Unclassified 0.451557407 0.8235589
## Unclassified_Cell_type_specific 0.464093248 0.8100607# Allele
top_iui_vs_fx$dma$unmeth <- "T"
top_iui_vs_fx$dma$meth <- "C"
top_iui_vs_fx$fit$SE <- sqrt(top_iui_vs_fx$fit$s2.post) * top_iui_vs_fx$fit$stdev.unscaled
# Extract required columns from dma
top_iui_vs_fx_metal <-top_iui_vs_fx$dma[,c("Row.names", "meth", "unmeth", "AveExpr", "P.Value")]
head(top_iui_vs_fx_metal)## Row.names meth unmeth AveExpr P.Value
## 276287 cg17476421 C T -4.336800 3.540484e-10
## 171967 cg10471644 C T -4.653258 7.802779e-10
## 180034 cg11022944 C T -2.987914 1.398501e-09
## 311754 cg20040772 C T -4.277220 1.762667e-09
## 321952 cg20811730 C T -3.718327 2.425129e-09
## 152708 cg09197288 C T 1.764911 3.553164e-09# Convert fit outputs to dataframes
fitCE <- as.data.frame(top_iui_vs_fx$fit$coefficients)
fitCE$Row.names <- row.names(fitCE)
fitCE <- fitCE[,c("Row.names", "groupsFX")]
names(fitCE)[2]<- "coefficient"
fitSE <- as.data.frame(top_iui_vs_fx$fit$SE)
fitSE$Row.names <- row.names(fitSE)
fitSE <- fitSE[,c("Row.names", "groupsFX")]
names(fitSE)[2]<- "SE"
# Merge Datasets
top_iui_vs_fx_metal <- merge(top_iui_vs_fx_metal, fitCE)
top_iui_vs_fx_metal <- merge(top_iui_vs_fx_metal, fitSE)
# Number of effective participants
controls <- samplesheet[samplesheet$art == "IUI",]
cases <- samplesheet[samplesheet$art == "FX",]
ctrl <- nrow(controls)
cses <- nrow(cases)
Neff <- 4/((1/cses)+(1/ctrl))
head(top_iui_vs_fx_metal)## Row.names meth unmeth AveExpr P.Value coefficient SE
## 1 cg00000029 C T 0.7693287 0.10602151 -0.11385405 0.06977786
## 2 cg00000108 C T 3.9187502 0.13334918 -0.23211149 0.15332722
## 3 cg00000109 C T 2.6374418 0.09929371 -0.29531098 0.17743074
## 4 cg00000165 C T -2.4991855 0.55922892 -0.03999354 0.06824492
## 5 cg00000236 C T 1.9625627 0.02570806 -0.13932494 0.06149104
## 6 cg00000289 C T 1.3020723 0.04650171 -0.27568150 0.13669040top_iui_vs_fx_metal$N <- Neff
head(top_iui_vs_fx_metal)## Row.names meth unmeth AveExpr P.Value coefficient SE N
## 1 cg00000029 C T 0.7693287 0.10602151 -0.11385405 0.06977786 58.21277
## 2 cg00000108 C T 3.9187502 0.13334918 -0.23211149 0.15332722 58.21277
## 3 cg00000109 C T 2.6374418 0.09929371 -0.29531098 0.17743074 58.21277
## 4 cg00000165 C T -2.4991855 0.55922892 -0.03999354 0.06824492 58.21277
## 5 cg00000236 C T 1.9625627 0.02570806 -0.13932494 0.06149104 58.21277
## 6 cg00000289 C T 1.3020723 0.04650171 -0.27568150 0.13669040 58.21277# Output for Meta-analysis
write.table(top_iui_vs_fx_metal, file="misc/top_iui_vs_fx_metal.tsv",sep="\t",quote=FALSE, row.names = FALSE) Venn diagrams of the differential methylated probes for each contrast
First, we look at the similarity of DMPs altered by fresh and frozen procedures. The overlap is very large. This indicates a high degree of similarity between the profiles.
v1 <- list("FH up" = top_nat_vs_fh$dm_up ,
"FZ up" = top_nat_vs_fz$dm_up ,
"FH dn" = top_nat_vs_fh$dm_dn ,
"FZ dn" = top_nat_vs_fz$dm_dn )
plot(euler(v1, shape = "ellipse"), quantities = TRUE)
Comparison of probes altered by fresh and frozen IVF procedures
Now to take a look at whether IUI is similar to IVF fresh and frozen. The overlap is just a large as FH vs FZ.
v2 <- list("FH up" = top_nat_vs_fh$dm_up ,
"FH dn" = top_nat_vs_fh$dm_dn ,
"IUI up" = top_nat_vs_iui$dm_up ,
"IUI dn" = top_nat_vs_iui$dm_dn )
plot(euler(v2, shape = "ellipse"), quantities = TRUE)
Comparison of probes altered by IUI, fresh and frozen IVF procedures
v3 <- list("FZ up" = top_nat_vs_fz$dm_up ,
"FZ dn" = top_nat_vs_fz$dm_dn ,
"IUI up" = top_nat_vs_iui$dm_up ,
"IUI dn" = top_nat_vs_iui$dm_dn )
plot(euler(v3, shape = "ellipse"), quantities = FALSE)
Comparison of probes altered by IUI, fresh and frozen IVF procedures
Spearman correlations of each contrast
While the above Venn diagrams are suggestive of similarity between contrasts, the best way to assess this is with correlation analysis. I have chosen Spearman as based on the directional p-value ranking metric.
mycontrasts <- list("top_nat_vs_fh"=top_nat_vs_fh, "top_nat_vs_fz"=top_nat_vs_fz, "top_nat_vs_fx"=top_nat_vs_fx ,
"top_nat_vs_iui"=top_nat_vs_iui, "top_fh_vs_fz"=top_fh_vs_fz, "top_iui_vs_fx"=top_iui_vs_fx)
myrnks <- lapply(X = mycontrasts, FUN = myranks)
df <- join_all(myrnks,by="rn")
rownames(df) <- df$rn
df$rn=NULL
colnames(df) <- names(mycontrasts)
head(df)## top_nat_vs_fh top_nat_vs_fz top_nat_vs_fx top_nat_vs_iui
## cg20757019 0.03287621 0.03966856 0.02493673 0.04890017
## cg08260549 0.03818084 0.04500167 0.02896379 0.05846054
## cg16905586 0.03818084 0.04764366 0.02934659 0.05501695
## cg01931797 0.03921029 0.04400678 0.02954104 0.05221048
## cg07897831 0.03921029 0.04314926 0.02677530 0.06661138
## cg18233405 0.03951672 0.04324729 0.02756628 0.06863616
## top_fh_vs_fz top_iui_vs_fx
## cg20757019 -1.992349 -2.799702
## cg08260549 13.800651 -26.044979
## cg16905586 -0.742125 -1.049266
## cg01931797 40.611380 -10.396365
## cg07897831 -11.417465 -4.512431
## cg18233405 -7.067448 5.292853mycors <- cor(df,method = "spearman")
my_palette <- colorRampPalette(c("darkred","red", "orange", "yellow","white"))(n = 25)
heatmap.2(mycors,scale="none",margin=c(10, 10),cexRow=0.8,trace="none",cexCol=0.8,
col=my_palette,main="Spearman correlations")
mycors## top_nat_vs_fh top_nat_vs_fz top_nat_vs_fx top_nat_vs_iui
## top_nat_vs_fh 1.00000000 0.40396512 0.64398948 0.39911034
## top_nat_vs_fz 0.40396512 1.00000000 0.64229192 0.30728370
## top_nat_vs_fx 0.64398948 0.64229192 1.00000000 0.39035156
## top_nat_vs_iui 0.39911034 0.30728370 0.39035156 1.00000000
## top_fh_vs_fz -0.02105861 0.04277322 0.00778595 -0.01713246
## top_iui_vs_fx -0.04486026 -0.02426028 -0.03223147 -0.10875851
## top_fh_vs_fz top_iui_vs_fx
## top_nat_vs_fh -0.02105861 -0.04486026
## top_nat_vs_fz 0.04277322 -0.02426028
## top_nat_vs_fx 0.00778595 -0.03223147
## top_nat_vs_iui -0.01713246 -0.10875851
## top_fh_vs_fz 1.00000000 0.03322067
## top_iui_vs_fx 0.03322067 1.00000000Enrichment analysis
We will be using the recently published package mitch to perform enrichment analysis, using average promoter methylation change as an indicaator of gene activity. Enrichment will be tested with the mitch package.
top_nat_vs_fh\(dma top_nat_vs_fz\)dm top_nat_vs_iui\(dma top_fh_vs_fz\)dma top_nat_vs_fx\(dma top_iui_vs_fx\)dma
library("mitch")## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2# gene sets
download.file("https://reactome.org/download/current/ReactomePathways.gmt.zip",
destfile="ReactomePathways.gmt.zip")
unzip("ReactomePathways.gmt.zip",overwrite = TRUE)
genesets <- gmt_import("ReactomePathways.gmt")Run 1D enrichment analysis
One dimensional enrichment analysis with REACTOME gene sets using average promoter methylation t-statistic.
top_nat_vs_fh_mitch <- run_mitch_1d(dma= top_nat_vs_fh$dma, name="top_nat_vs_fh_mitch")## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.head(top_nat_vs_fh_mitch,50)## set
## 1089 Translation
## 340 G2/M Transition
## 1051 Termination of O-glycan biosynthesis
## 14 APC:Cdc20 mediated degradation of cell cycle proteins prior to satisfation of the cell cycle checkpoint
## 558 Mitotic G2-G2/M phases
## 117 Cdc20:Phospho-APC/C mediated degradation of Cyclin A
## 12 APC/C:Cdc20 mediated degradation of mitotic proteins
## 9 APC/C-mediated degradation of cell cycle proteins
## 842 Regulation of mitotic cell cycle
## 24 Activation of APC/C and APC/C:Cdc20 mediated degradation of mitotic proteins
## 168 Cross-presentation of soluble exogenous antigens (endosomes)
## 1135 Vpu mediated degradation of CD4
## 835 Regulation of cholesterol biosynthesis by SREBP (SREBF)
## 408 Hh mutants are degraded by ERAD
## 1129 Vif-mediated degradation of APOBEC3G
## 836 Regulation of expression of SLITs and ROBOs
## 877 SCF(Skp2)-mediated degradation of p27/p21
## 641 Orc1 removal from chromatin
## 27 Activation of BH3-only proteins
## 73 BBSome-mediated cargo-targeting to cilium
## 11 APC/C:Cdc20 mediated degradation of Securin
## 407 Hh mutants abrogate ligand secretion
## 301 FOXO-mediated transcription
## 517 Membrane Trafficking
## 999 Stabilization of p53
## 973 Signaling by RAF1 mutants
## 13 APC/C:Cdh1 mediated degradation of Cdc20 and other APC/C:Cdh1 targeted proteins in late mitosis/early G1
## 67 Autodegradation of Cdh1 by Cdh1:APC/C
## 484 L13a-mediated translational silencing of Ceruloplasmin expression
## 845 Regulation of pyruvate dehydrogenase (PDH) complex
## 350 GTP hydrolysis and joining of the 60S ribosomal subunit
## 1118 Ubiquitin Mediated Degradation of Phosphorylated Cdc25A
## 1153 p53-Independent DNA Damage Response
## 1154 p53-Independent G1/S DNA damage checkpoint
## 1056 The role of GTSE1 in G2/M progression after G2 checkpoint
## 211 Degradation of GLI1 by the proteasome
## 1128 Vesicle-mediated transport
## 1004 Switching of origins to a post-replicative state
## 554 Mitochondrial translation termination
## 722 Pyruvate metabolism
## 552 Mitochondrial translation elongation
## 725 RAB geranylgeranylation
## 290 FBXL7 down-regulates AURKA during mitotic entry and in early mitosis
## 646 Oxygen-dependent proline hydroxylation of Hypoxia-inducible Factor Alpha
## 882 SLC-mediated transmembrane transport
## 267 ER-Phagosome pathway
## 68 Autodegradation of the E3 ubiquitin ligase COP1
## 50 Antigen processing-Cross presentation
## 1007 Synthesis of DNA
## 165 Cooperation of PDCL (PhLP1) and TRiC/CCT in G-protein beta folding
## setSize pANOVA s.dist p.adjustANOVA
## 1089 225 3.182326e-05 0.16192583 0.0370741
## 340 143 5.130259e-04 0.16891808 0.1668591
## 1051 10 5.592014e-04 0.63021203 0.1668591
## 14 59 7.415387e-04 0.25430641 0.1668591
## 558 145 7.769993e-04 0.16231228 0.1668591
## 117 58 1.212061e-03 0.24601263 0.1668591
## 12 61 1.275037e-03 0.23885009 0.1668591
## 9 73 1.392389e-03 0.21675558 0.1668591
## 842 73 1.392389e-03 0.21675558 0.1668591
## 24 62 1.554464e-03 0.23272195 0.1668591
## 168 37 1.755190e-03 0.29749526 0.1668591
## 1135 43 2.772965e-03 0.26396674 0.1668591
## 835 45 2.789726e-03 -0.25790176 0.1668591
## 408 46 3.016217e-03 0.25305579 0.1668591
## 1129 42 3.169540e-03 0.26341452 0.1668591
## 836 117 3.193452e-03 0.15830462 0.1668591
## 877 51 3.250544e-03 0.23852214 0.1668591
## 641 55 3.300516e-03 0.22936293 0.1668591
## 27 25 3.306733e-03 -0.33961390 0.1668591
## 73 18 3.319978e-03 0.39992780 0.1668591
## 11 54 3.540249e-03 0.22974814 0.1668591
## 407 48 3.565255e-03 0.24342686 0.1668591
## 301 45 4.098898e-03 -0.24759384 0.1668591
## 517 419 4.101039e-03 -0.08272640 0.1668591
## 999 48 4.371569e-03 0.23806311 0.1668591
## 973 25 4.589820e-03 -0.32768949 0.1668591
## 13 60 4.696134e-03 0.21135244 0.1668591
## 67 52 4.748611e-03 0.22665073 0.1668591
## 484 82 4.861509e-03 0.18028341 0.1668591
## 845 11 5.023307e-03 -0.48861792 0.1668591
## 350 83 5.145866e-03 0.17803840 0.1668591
## 1118 43 5.296273e-03 0.24602776 0.1668591
## 1153 43 5.296273e-03 0.24602776 0.1668591
## 1154 43 5.296273e-03 0.24602776 0.1668591
## 1056 51 5.548946e-03 0.22477508 0.1668591
## 211 50 5.571500e-03 0.22689213 0.1668591
## 1128 426 5.724961e-03 -0.07901158 0.1668591
## 1004 71 5.814096e-03 0.18965200 0.1668591
## 554 80 5.964694e-03 0.17820578 0.1668591
## 722 19 6.132011e-03 -0.36333319 0.1668591
## 552 81 6.133048e-03 0.17652328 0.1668591
## 725 39 6.223999e-03 -0.25340471 0.1668591
## 290 45 6.238085e-03 0.23591484 0.1668591
## 646 51 6.301974e-03 0.22139924 0.1668591
## 882 90 6.517278e-03 -0.16631778 0.1687251
## 267 66 6.877671e-03 0.19270761 0.1741845
## 68 43 7.465449e-03 0.23605219 0.1786087
## 50 71 7.639559e-03 0.18343216 0.1786087
## 1007 95 7.849953e-03 0.15822867 0.1786087
## 165 19 8.086212e-03 0.35111591 0.1786087top_nat_vs_fz_mitch <- run_mitch_1d(dma= top_nat_vs_fz$dma, name="top_nat_vs_fz_mitch")## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.head(top_nat_vs_fz_mitch,50)## set
## 1089 Translation
## 836 Regulation of expression of SLITs and ROBOs
## 168 Cross-presentation of soluble exogenous antigens (endosomes)
## 14 APC:Cdc20 mediated degradation of cell cycle proteins prior to satisfation of the cell cycle checkpoint
## 50 Antigen processing-Cross presentation
## 24 Activation of APC/C and APC/C:Cdc20 mediated degradation of mitotic proteins
## 117 Cdc20:Phospho-APC/C mediated degradation of Cyclin A
## 12 APC/C:Cdc20 mediated degradation of mitotic proteins
## 1129 Vif-mediated degradation of APOBEC3G
## 522 Metabolism of amino acids and derivatives
## 641 Orc1 removal from chromatin
## 11 APC/C:Cdc20 mediated degradation of Securin
## 484 L13a-mediated translational silencing of Ceruloplasmin expression
## 407 Hh mutants abrogate ligand secretion
## 267 ER-Phagosome pathway
## 340 G2/M Transition
## 290 FBXL7 down-regulates AURKA during mitotic entry and in early mitosis
## 67 Autodegradation of Cdh1 by Cdh1:APC/C
## 1135 Vpu mediated degradation of CD4
## 408 Hh mutants are degraded by ERAD
## 1051 Termination of O-glycan biosynthesis
## 844 Regulation of ornithine decarboxylase (ODC)
## 558 Mitotic G2-G2/M phases
## 350 GTP hydrolysis and joining of the 60S ribosomal subunit
## 9 APC/C-mediated degradation of cell cycle proteins
## 842 Regulation of mitotic cell cycle
## 13 APC/C:Cdh1 mediated degradation of Cdc20 and other APC/C:Cdh1 targeted proteins in late mitosis/early G1
## 1004 Switching of origins to a post-replicative state
## 646 Oxygen-dependent proline hydroxylation of Hypoxia-inducible Factor Alpha
## 877 SCF(Skp2)-mediated degradation of p27/p21
## 402 Hedgehog ligand biogenesis
## 1056 The role of GTSE1 in G2/M progression after G2 checkpoint
## 109 Cap-dependent Translation Initiation
## 284 Eukaryotic Translation Initiation
## 1118 Ubiquitin Mediated Degradation of Phosphorylated Cdc25A
## 1153 p53-Independent DNA Damage Response
## 1154 p53-Independent G1/S DNA damage checkpoint
## 191 DNA Replication Pre-Initiation
## 1119 Ubiquitin-dependent degradation of Cyclin D
## 1007 Synthesis of DNA
## 16 AUF1 (hnRNP D0) binds and destabilizes mRNA
## 975 Signaling by ROBO receptors
## 349 GSK3B and BTRC:CUL1-mediated-degradation of NFE2L2
## 808 Regulation of Apoptosis
## 68 Autodegradation of the E3 ubiquitin ligase COP1
## 338 G2/M Checkpoints
## 316 Formation of a pool of free 40S subunits
## 91 CDK-mediated phosphorylation and removal of Cdc6
## 521 Metabolism of RNA
## 833 Regulation of activated PAK-2p34 by proteasome mediated degradation
## setSize pANOVA s.dist p.adjustANOVA
## 1089 225 2.455025e-05 0.1642158 0.02860104
## 836 117 7.454113e-05 0.2126461 0.03356268
## 168 37 1.187585e-04 0.3658959 0.03356268
## 14 59 2.712920e-04 0.2744595 0.03356268
## 50 71 3.292340e-04 0.2469016 0.03356268
## 24 62 3.712925e-04 0.2617822 0.03356268
## 117 58 4.093891e-04 0.2686484 0.03356268
## 12 61 4.239170e-04 0.2613154 0.03356268
## 1129 42 4.633309e-04 0.3125118 0.03356268
## 522 210 4.824126e-04 0.1405366 0.03356268
## 641 55 5.164779e-04 0.2710077 0.03356268
## 11 54 5.313269e-04 0.2728920 0.03356268
## 484 82 5.330589e-04 0.2217132 0.03356268
## 407 48 5.357383e-04 0.2891715 0.03356268
## 267 66 5.536172e-04 0.2462025 0.03356268
## 340 143 5.604508e-04 0.1677617 0.03356268
## 290 45 5.699694e-04 0.2971696 0.03356268
## 67 52 5.754742e-04 0.2763353 0.03356268
## 1135 43 5.860943e-04 0.3033056 0.03356268
## 408 46 6.585046e-04 0.2905929 0.03356268
## 1051 10 6.681828e-04 0.6213858 0.03356268
## 844 42 6.745358e-04 0.3034678 0.03356268
## 558 145 6.878668e-04 0.1639297 0.03356268
## 350 83 7.187873e-04 0.2152174 0.03356268
## 9 73 7.490383e-04 0.2285996 0.03356268
## 842 73 7.490383e-04 0.2285996 0.03356268
## 13 60 7.968158e-04 0.2507087 0.03438113
## 1004 71 8.452505e-04 0.2294757 0.03516846
## 646 51 9.511190e-04 0.2678141 0.03602774
## 877 51 9.573302e-04 0.2676662 0.03602774
## 402 51 9.586781e-04 0.2676343 0.03602774
## 1056 51 1.012282e-03 0.2663958 0.03685340
## 109 90 1.167465e-03 0.1984681 0.03818525
## 284 90 1.167465e-03 0.1984681 0.03818525
## 1118 43 1.220769e-03 0.2853193 0.03818525
## 1153 43 1.220769e-03 0.2853193 0.03818525
## 1154 43 1.220769e-03 0.2853193 0.03818525
## 191 79 1.297594e-03 0.2097479 0.03818525
## 1119 43 1.345645e-03 0.2828574 0.03818525
## 1007 95 1.350818e-03 0.1907405 0.03818525
## 16 45 1.352382e-03 0.2764044 0.03818525
## 975 146 1.405822e-03 0.1537104 0.03818525
## 349 45 1.436839e-03 0.2748976 0.03818525
## 808 44 1.442190e-03 0.2778960 0.03818525
## 68 43 1.478374e-03 0.2804617 0.03827347
## 338 107 1.710372e-03 0.1759920 0.04331702
## 316 73 1.800172e-03 0.2116812 0.04462129
## 91 58 1.851035e-03 0.2366757 0.04492616
## 521 499 1.906037e-03 0.0823376 0.04498467
## 833 41 1.932270e-03 0.2801080 0.04498467top_nat_vs_iui_mitch <- run_mitch_1d(dma= top_nat_vs_iui$dma, name="top_nat_vs_iui_mitch")## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.head(top_nat_vs_iui_mitch,50)## set
## 361 Global Genome Nucleotide Excision Repair (GG-NER)
## 185 DNA Damage Recognition in GG-NER
## 581 NOTCH3 Activation and Transmission of Signal to the Nucleus
## 488 Late Phase of HIV Life Cycle
## 393 HIV Life Cycle
## 265 ER Quality Control Compartment (ERQC)
## 1102 Transport of Mature mRNA derived from an Intron-Containing Transcript
## 572 N-glycan trimming in the ER and Calnexin/Calreticulin cycle
## 517 Membrane Trafficking
## 725 RAB geranylgeranylation
## 588 NS1 Mediated Effects on Host Pathways
## 1128 Vesicle-mediated transport
## 835 Regulation of cholesterol biosynthesis by SREBP (SREBF)
## 1051 Termination of O-glycan biosynthesis
## 108 Calnexin/calreticulin cycle
## 637 O-linked glycosylation of mucins
## 418 ISG15 antiviral mechanism
## 962 Signaling by NOTCH3
## 961 Signaling by NOTCH2
## 1133 Vitamin B5 (pantothenate) metabolism
## 1029 TNF signaling
## 854 Resolution of D-loop Structures through Synthesis-Dependent Strand Annealing (SDSA)
## 52 Antiviral mechanism by IFN-stimulated genes
## 825 Regulation of TNFR1 signaling
## 889 SUMOylation of DNA replication proteins
## 301 FOXO-mediated transcription
## 580 NOTCH2 Activation and Transmission of Signal to the Nucleus
## 936 Signaling by FGFR1
## 592 Negative regulation of FGFR1 signaling
## 593 Negative regulation of FGFR2 signaling
## 594 Negative regulation of FGFR3 signaling
## 595 Negative regulation of FGFR4 signaling
## 409 Homologous DNA Pairing and Strand Exchange
## 845 Regulation of pyruvate dehydrogenase (PDH) complex
## 445 Interactions of Vpr with host cellular proteins
## 722 Pyruvate metabolism
## 941 Signaling by FGFR3
## 565 MyD88 cascade initiated on plasma membrane
## 1060 Toll Like Receptor 10 (TLR10) Cascade
## 1064 Toll Like Receptor 5 (TLR5) Cascade
## 1044 TRAF6 mediated induction of NFkB and MAP kinases upon TLR7/8 or 9 activation
## 638 Oncogene Induced Senescence
## 852 Resolution of D-Loop Structures
## 998 Spry regulation of FGF signaling
## 225 Diseases associated with N-glycosylation of proteins
## 346 GPCR ligand binding
## 566 MyD88 dependent cascade initiated on endosome
## 1065 Toll Like Receptor 7/8 (TLR7/8) Cascade
## 973 Signaling by RAF1 mutants
## 1122 Uptake and actions of bacterial toxins
## setSize pANOVA s.dist p.adjustANOVA
## 361 76 0.0005467886 -0.22977331 0.2652254
## 185 35 0.0007279066 -0.33027432 0.2652254
## 581 15 0.0007718337 -0.50158343 0.2652254
## 488 105 0.0010148092 -0.18613282 0.2652254
## 393 117 0.0012946562 -0.17272321 0.2652254
## 265 18 0.0013926318 -0.43528390 0.2652254
## 1102 45 0.0015936290 -0.27230480 0.2652254
## 572 32 0.0018347104 -0.31847992 0.2671797
## 517 419 0.0023760843 -0.08757798 0.3075709
## 725 39 0.0028472215 -0.27636893 0.3131250
## 588 36 0.0029565449 -0.28649736 0.3131250
## 1128 426 0.0035565141 -0.08335335 0.3452782
## 835 45 0.0052368048 -0.24083803 0.4155994
## 1051 10 0.0063119671 0.49885361 0.4155994
## 108 23 0.0068844502 -0.32568945 0.4155994
## 637 26 0.0069893025 0.30580069 0.4155994
## 418 67 0.0072463264 -0.19004264 0.4155994
## 962 30 0.0075205384 -0.28215919 0.4155994
## 961 18 0.0083261991 -0.35937095 0.4155994
## 1133 11 0.0088752662 0.45576299 0.4155994
## 1029 35 0.0091226209 -0.25489945 0.4155994
## 854 20 0.0093295771 0.33595004 0.4155994
## 52 73 0.0094716866 -0.17596739 0.4155994
## 825 30 0.0102060885 -0.27117346 0.4155994
## 889 41 0.0107407322 -0.23048855 0.4155994
## 301 45 0.0113154050 -0.21848006 0.4155994
## 580 12 0.0113879270 -0.42200237 0.4155994
## 936 27 0.0123092437 -0.27850045 0.4155994
## 592 17 0.0136343691 -0.34570319 0.4155994
## 593 17 0.0136343691 -0.34570319 0.4155994
## 594 17 0.0136343691 -0.34570319 0.4155994
## 595 17 0.0136343691 -0.34570319 0.4155994
## 409 34 0.0143070770 0.24294542 0.4155994
## 845 11 0.0145333717 -0.42563828 0.4155994
## 445 31 0.0150170682 -0.25257448 0.4155994
## 722 19 0.0156462674 -0.32044978 0.4155994
## 941 25 0.0158375622 -0.27893518 0.4155994
## 565 60 0.0159475712 -0.18018422 0.4155994
## 1060 60 0.0159475712 -0.18018422 0.4155994
## 1064 60 0.0159475712 -0.18018422 0.4155994
## 1044 61 0.0164071977 -0.17794057 0.4155994
## 638 27 0.0164584812 -0.26686992 0.4155994
## 852 24 0.0164798403 0.28295912 0.4155994
## 998 15 0.0168321385 -0.35659765 0.4155994
## 225 18 0.0170632273 -0.32489480 0.4155994
## 346 70 0.0172719999 0.16487846 0.4155994
## 566 62 0.0174048179 -0.17491405 0.4155994
## 1065 62 0.0174048179 -0.17491405 0.4155994
## 973 25 0.0181283092 -0.27319650 0.4155994
## 1122 18 0.0191928916 -0.31896075 0.4155994top_fh_vs_fz_mitch <- run_mitch_1d(dma= top_fh_vs_fz$dma, name="top_fh_vs_fz_mitch")## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.head(top_fh_vs_fz_mitch,50)## set
## 520 Metabolism
## 132 Cellular responses to stimuli
## 128 Cellular response to chemical stress
## 133 Cellular responses to stress
## 235 Diseases of metabolism
## 435 Innate Immune System
## 234 Diseases of glycosylation
## 224 Disease
## 470 Intraflagellar transport
## 214 Degradation of the extracellular matrix
## 289 Extracellular matrix organization
## 143 Cilium Assembly
## 307 Fatty acid metabolism
## 648 PCP/CE pathway
## 479 KEAP1-NFE2L2 pathway
## 950 Signaling by Interleukins
## 406 Heparan sulfate/heparin (HS-GAG) metabolism
## 175 Cytokine Signaling in Immune system
## 564 Muscle contraction
## 642 Organelle biogenesis and maintenance
## 65 Asymmetric localization of PCP proteins
## 1139 Zinc transporters
## 419 Immune System
## 131 Cellular response to starvation
## 42 Amino acids regulate mTORC1
## 448 Interferon alpha/beta signaling
## 1077 Transcriptional Regulation by TP53
## 659 PKMTs methylate histone lysines
## 1141 alpha-linolenic (omega3) and linoleic (omega6) acid metabolism
## 1142 alpha-linolenic acid (ALA) metabolism
## 220 Detoxification of Reactive Oxygen Species
## 611 Neutrophil degranulation
## 463 Interleukin-4 and Interleukin-13 signaling
## 836 Regulation of expression of SLITs and ROBOs
## 855 Resolution of Sister Chromatid Cohesion
## 1051 Termination of O-glycan biosynthesis
## 261 EML4 and NUDC in mitotic spindle formation
## 523 Metabolism of carbohydrates
## 239 Disorders of Developmental Biology
## 240 Disorders of Nervous System Development
## 497 Loss of function of MECP2 in Rett syndrome
## 673 Pervasive developmental disorders
## 43 Amplification of signal from unattached kinetochores via a MAD2 inhibitory signal
## 44 Amplification of signal from the kinetochores
## 219 Deregulated CDK5 triggers multiple neurodegenerative pathways in Alzheimer's disease models
## 607 Neurodegenerative Diseases
## 827 Regulation of TP53 Activity through Acetylation
## 177 Cytosolic sensors of pathogen-associated DNA
## 154 Collagen biosynthesis and modifying enzymes
## 549 Mitochondrial protein import
## setSize pANOVA s.dist p.adjustANOVA
## 520 1168 2.120731e-05 0.07647214 0.02470652
## 132 508 1.022261e-04 0.10216281 0.03047541
## 128 139 1.250713e-04 0.18913882 0.03047541
## 133 503 1.253147e-04 0.10132838 0.03047541
## 235 122 1.307957e-04 0.20113215 0.03047541
## 435 584 2.462329e-04 0.09028140 0.04618567
## 234 67 2.794956e-04 0.25711544 0.04618567
## 224 1027 3.171548e-04 0.06852362 0.04618567
## 470 29 8.065105e-04 -0.35968903 0.10439830
## 214 26 1.194032e-03 0.36732508 0.13042900
## 289 75 1.231518e-03 0.21622549 0.13042900
## 143 133 1.642217e-03 -0.15866562 0.15943189
## 307 96 1.814764e-03 0.18466636 0.16263077
## 648 70 2.636229e-03 0.20823867 0.21937188
## 479 85 3.288889e-03 0.18485093 0.25543701
## 950 256 4.338999e-03 0.10428074 0.31593333
## 406 19 5.149958e-03 0.37086878 0.34549815
## 175 411 5.531640e-03 0.08070212 0.34549815
## 564 64 5.634734e-03 0.20042334 0.34549815
## 642 183 5.976990e-03 -0.11842191 0.34815967
## 65 51 7.086843e-03 0.21824712 0.39315104
## 1139 10 9.101540e-03 -0.47641270 0.41705256
## 419 1092 9.166513e-03 0.04827421 0.41705256
## 131 106 9.266260e-03 0.14670048 0.41705256
## 42 34 9.920834e-03 0.25574745 0.41705256
## 448 50 1.018268e-02 0.21032902 0.41705256
## 1077 273 1.029394e-02 0.09091916 0.41705256
## 659 25 1.083360e-02 -0.29458240 0.41705256
## 1141 10 1.084147e-02 0.46537486 0.41705256
## 1142 10 1.084147e-02 0.46537486 0.41705256
## 220 22 1.111677e-02 0.31286763 0.41705256
## 611 289 1.145552e-02 0.08715475 0.41705256
## 463 47 1.271481e-02 0.21033063 0.44427187
## 836 117 1.337414e-02 0.13281964 0.44427187
## 855 90 1.339255e-02 -0.15119839 0.44427187
## 1051 10 1.455289e-02 0.44630212 0.44427187
## 261 85 1.471627e-02 -0.15341263 0.44427187
## 523 172 1.546851e-02 0.10752726 0.44427187
## 239 10 1.626682e-02 0.43891651 0.44427187
## 240 10 1.626682e-02 0.43891651 0.44427187
## 497 10 1.626682e-02 0.43891651 0.44427187
## 673 10 1.626682e-02 0.43891651 0.44427187
## 43 81 1.684670e-02 -0.15395135 0.44427187
## 44 81 1.684670e-02 -0.15395135 0.44427187
## 219 20 1.754207e-02 0.30693612 0.44427187
## 607 20 1.754207e-02 0.30693612 0.44427187
## 827 27 1.797983e-02 0.26324902 0.44567020
## 177 49 1.879974e-02 0.19426210 0.44752213
## 154 13 1.882282e-02 0.37638757 0.44752213
## 549 42 1.927141e-02 0.20892766 0.44902385top_nat_vs_fx_mitch <- run_mitch_1d(dma= top_nat_vs_fx$dma, name="top_nat_vs_fx_mitch")## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.head(top_nat_vs_fx_mitch,50)## set
## 1089 Translation
## 168 Cross-presentation of soluble exogenous antigens (endosomes)
## 1051 Termination of O-glycan biosynthesis
## 14 APC:Cdc20 mediated degradation of cell cycle proteins prior to satisfation of the cell cycle checkpoint
## 340 G2/M Transition
## 836 Regulation of expression of SLITs and ROBOs
## 117 Cdc20:Phospho-APC/C mediated degradation of Cyclin A
## 558 Mitotic G2-G2/M phases
## 12 APC/C:Cdc20 mediated degradation of mitotic proteins
## 24 Activation of APC/C and APC/C:Cdc20 mediated degradation of mitotic proteins
## 9 APC/C-mediated degradation of cell cycle proteins
## 842 Regulation of mitotic cell cycle
## 1129 Vif-mediated degradation of APOBEC3G
## 1135 Vpu mediated degradation of CD4
## 11 APC/C:Cdc20 mediated degradation of Securin
## 522 Metabolism of amino acids and derivatives
## 641 Orc1 removal from chromatin
## 50 Antigen processing-Cross presentation
## 484 L13a-mediated translational silencing of Ceruloplasmin expression
## 517 Membrane Trafficking
## 407 Hh mutants abrogate ligand secretion
## 408 Hh mutants are degraded by ERAD
## 67 Autodegradation of Cdh1 by Cdh1:APC/C
## 350 GTP hydrolysis and joining of the 60S ribosomal subunit
## 877 SCF(Skp2)-mediated degradation of p27/p21
## 267 ER-Phagosome pathway
## 290 FBXL7 down-regulates AURKA during mitotic entry and in early mitosis
## 13 APC/C:Cdh1 mediated degradation of Cdc20 and other APC/C:Cdh1 targeted proteins in late mitosis/early G1
## 1004 Switching of origins to a post-replicative state
## 646 Oxygen-dependent proline hydroxylation of Hypoxia-inducible Factor Alpha
## 844 Regulation of ornithine decarboxylase (ODC)
## 1118 Ubiquitin Mediated Degradation of Phosphorylated Cdc25A
## 1153 p53-Independent DNA Damage Response
## 1154 p53-Independent G1/S DNA damage checkpoint
## 1056 The role of GTSE1 in G2/M progression after G2 checkpoint
## 1128 Vesicle-mediated transport
## 835 Regulation of cholesterol biosynthesis by SREBP (SREBF)
## 109 Cap-dependent Translation Initiation
## 284 Eukaryotic Translation Initiation
## 402 Hedgehog ligand biogenesis
## 349 GSK3B and BTRC:CUL1-mediated-degradation of NFE2L2
## 1007 Synthesis of DNA
## 68 Autodegradation of the E3 ubiquitin ligase COP1
## 808 Regulation of Apoptosis
## 941 Signaling by FGFR3
## 211 Degradation of GLI1 by the proteasome
## 316 Formation of a pool of free 40S subunits
## 91 CDK-mediated phosphorylation and removal of Cdc6
## 999 Stabilization of p53
## 191 DNA Replication Pre-Initiation
## setSize pANOVA s.dist p.adjustANOVA
## 1089 225 0.0000230643 0.16476250 0.02686991
## 168 37 0.0003565244 0.33945176 0.07738676
## 1051 10 0.0003828858 0.64863549 0.07738676
## 14 59 0.0004204744 0.26584317 0.07738676
## 340 143 0.0004941188 0.16940710 0.07738676
## 836 117 0.0005415486 0.18571641 0.07738676
## 117 58 0.0006627692 0.25881580 0.07738676
## 558 145 0.0006629688 0.16441629 0.07738676
## 12 61 0.0006918719 0.25153887 0.07738676
## 24 62 0.0007004304 0.24926716 0.07738676
## 9 73 0.0010525725 0.22216845 0.07738676
## 842 73 0.0010525725 0.22216845 0.07738676
## 1129 42 0.0011400220 0.29042045 0.07738676
## 1135 43 0.0012549390 0.28462332 0.07738676
## 11 54 0.0012847414 0.25359784 0.07738676
## 522 210 0.0013023105 0.12947529 0.07738676
## 641 55 0.0013113501 0.25083571 0.07738676
## 50 71 0.0013741194 0.22002657 0.07738676
## 484 82 0.0013837654 0.20472351 0.07738676
## 517 419 0.0015194318 -0.09138780 0.07738676
## 407 48 0.0015229100 0.26480124 0.07738676
## 408 46 0.0015394143 0.27020183 0.07738676
## 67 52 0.0015966617 0.25336024 0.07738676
## 350 83 0.0016054545 0.20075560 0.07738676
## 877 51 0.0017059589 0.25425076 0.07738676
## 267 66 0.0017270864 0.22341258 0.07738676
## 290 45 0.0018779400 0.26815086 0.08102963
## 13 60 0.0019860199 0.23116481 0.08133714
## 1004 71 0.0021061484 0.21142289 0.08133714
## 646 51 0.0021734547 0.24844192 0.08133714
## 844 42 0.0022264065 0.27300133 0.08133714
## 1118 43 0.0023737879 0.26812363 0.08133714
## 1153 43 0.0023737879 0.26812363 0.08133714
## 1154 43 0.0023737879 0.26812363 0.08133714
## 1056 51 0.0025045846 0.24498618 0.08336689
## 1128 426 0.0026489659 -0.08594717 0.08572348
## 835 45 0.0027820657 -0.25797416 0.08759748
## 109 90 0.0030573493 0.18106668 0.09132851
## 284 90 0.0030573493 0.18106668 0.09132851
## 402 51 0.0032541701 0.23849418 0.09477771
## 349 45 0.0033957453 0.25267992 0.09477831
## 1007 95 0.0034169005 0.17423573 0.09477831
## 68 43 0.0036695760 0.25633001 0.09726581
## 808 44 0.0037084307 0.25312578 0.09726581
## 941 25 0.0037570485 -0.33501321 0.09726581
## 211 50 0.0038718008 0.23641846 0.09805756
## 316 73 0.0040976758 0.19467888 0.09989575
## 91 58 0.0041463270 0.21795606 0.09989575
## 999 48 0.0042542696 0.23878450 0.09989575
## 191 79 0.0043110305 0.18614889 0.09989575top_iui_vs_fx_mitch <- run_mitch_1d(dma= top_iui_vs_fx$dma, name="top_iui_vs_fx_mitch")## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.head(top_iui_vs_fx_mitch,50)## set
## 521 Metabolism of RNA
## 836 Regulation of expression of SLITs and ROBOs
## 1089 Translation
## 975 Signaling by ROBO receptors
## 316 Formation of a pool of free 40S subunits
## 283 Eukaryotic Translation Elongation
## 668 Peptide chain elongation
## 132 Cellular responses to stimuli
## 133 Cellular responses to stress
## 484 L13a-mediated translational silencing of Ceruloplasmin expression
## 1159 rRNA processing in the nucleus and cytosol
## 1158 rRNA processing
## 285 Eukaryotic Translation Termination
## 1056 The role of GTSE1 in G2/M progression after G2 checkpoint
## 884 SRP-dependent cotranslational protein targeting to membrane
## 350 GTP hydrolysis and joining of the 60S ribosomal subunit
## 859 Response of EIF2AK4 (GCN2) to amino acid deficiency
## 109 Cap-dependent Translation Initiation
## 284 Eukaryotic Translation Initiation
## 512 Major pathway of rRNA processing in the nucleolus and cytosol
## 901 Selenocysteine synthesis
## 1131 Viral mRNA Translation
## 900 Selenoamino acid metabolism
## 392 HIV Infection
## 618 Nonsense Mediated Decay (NMD) independent of the Exon Junction Complex (EJC)
## 16 AUF1 (hnRNP D0) binds and destabilizes mRNA
## 429 Influenza Infection
## 131 Cellular response to starvation
## 617 Nonsense Mediated Decay (NMD) enhanced by the Exon Junction Complex (EJC)
## 619 Nonsense-Mediated Decay (NMD)
## 430 Influenza Viral RNA Transcription and Replication
## 833 Regulation of activated PAK-2p34 by proteasome mediated degradation
## 874 SARS-CoV-2 modulates host translation machinery
## 68 Autodegradation of the E3 ubiquitin ligase COP1
## 349 GSK3B and BTRC:CUL1-mediated-degradation of NFE2L2
## 31 Activation of NF-kappaB in B cells
## 963 Signaling by NOTCH4
## 1118 Ubiquitin Mediated Degradation of Phosphorylated Cdc25A
## 1153 p53-Independent DNA Damage Response
## 1154 p53-Independent G1/S DNA damage checkpoint
## 956 Signaling by NOTCH
## 210 Degradation of DVL
## 999 Stabilization of p53
## 641 Orc1 removal from chromatin
## 63 Assembly of the pre-replicative complex
## 1135 Vpu mediated degradation of CD4
## 600 Negative regulation of NOTCH4 signaling
## 1116 UCH proteinases
## 408 Hh mutants are degraded by ERAD
## 213 Degradation of beta-catenin by the destruction complex
## setSize pANOVA s.dist p.adjustANOVA
## 521 499 1.477058e-08 0.1501410 9.111727e-06
## 836 117 1.564245e-08 0.3034057 9.111727e-06
## 1089 225 1.136372e-07 0.2063767 4.412913e-05
## 975 146 1.017944e-06 0.2351990 2.819201e-04
## 316 73 1.318721e-06 0.3279111 2.819201e-04
## 283 67 1.451949e-06 0.3408194 2.819201e-04
## 668 64 3.307468e-06 0.3365902 4.670138e-04
## 132 508 3.380821e-06 0.1221524 4.670138e-04
## 133 503 3.607832e-06 0.1223709 4.670138e-04
## 484 82 5.718087e-06 0.2903349 6.373270e-04
## 1159 145 6.017680e-06 0.2185204 6.373270e-04
## 1158 149 8.761827e-06 0.2117996 7.352540e-04
## 285 66 1.030402e-05 0.3143928 7.352540e-04
## 1056 51 1.093598e-05 0.3563330 7.352540e-04
## 884 81 1.112474e-05 0.2829414 7.352540e-04
## 350 83 1.158888e-05 0.2789753 7.352540e-04
## 859 72 1.173924e-05 0.2991691 7.352540e-04
## 109 90 1.199127e-05 0.2675486 7.352540e-04
## 284 90 1.199127e-05 0.2675486 7.352540e-04
## 512 140 1.303067e-05 0.2141756 7.590364e-04
## 901 65 1.475875e-05 0.3111578 7.935912e-04
## 1131 65 1.498627e-05 0.3109161 7.935912e-04
## 900 73 1.894292e-05 0.2899902 9.594999e-04
## 392 177 2.281986e-05 0.1854087 1.107714e-03
## 618 69 2.409275e-05 0.2944641 1.122722e-03
## 16 45 2.512170e-05 0.3633702 1.125645e-03
## 429 123 3.674004e-05 0.2161330 1.585265e-03
## 131 106 3.979050e-05 0.2315820 1.597555e-03
## 617 81 4.113877e-05 0.2640860 1.597555e-03
## 619 81 4.113877e-05 0.2640860 1.597555e-03
## 430 106 5.156441e-05 0.2281876 1.937824e-03
## 833 41 6.327671e-05 0.3613410 2.303668e-03
## 874 36 7.287986e-05 0.3822885 2.572880e-03
## 68 43 7.748933e-05 0.3486242 2.655149e-03
## 349 45 8.780416e-05 0.3382384 2.843649e-03
## 31 54 8.787241e-05 0.3088949 2.843649e-03
## 963 67 9.532484e-05 0.2761072 2.920980e-03
## 1118 43 1.008906e-04 0.3430185 2.920980e-03
## 1153 43 1.008906e-04 0.3430185 2.920980e-03
## 1154 43 1.008906e-04 0.3430185 2.920980e-03
## 956 128 1.027984e-04 0.1994462 2.920980e-03
## 210 47 1.063306e-04 0.3270876 2.949409e-03
## 999 48 1.125095e-04 0.3225294 3.048224e-03
## 641 55 1.221499e-04 0.2998443 3.234196e-03
## 63 66 1.301305e-04 0.2727644 3.328476e-03
## 1135 43 1.314248e-04 0.3373182 3.328476e-03
## 600 47 1.566040e-04 0.3190493 3.881781e-03
## 1116 69 1.678307e-04 0.2624108 4.004440e-03
## 408 46 1.714461e-04 0.3205553 4.004440e-03
## 213 67 1.718644e-04 0.2658514 4.004440e-03Run multi-dimensional enrichment analysis
xl <- list("nat_vs_fh"=top_nat_vs_fh$dma, "top_nat_vs_fz"=top_nat_vs_fz$dma, "top_nat_vs_iui"=top_nat_vs_iui$dma,
"top_fh_vs_fz"=top_fh_vs_fz$dma,"top_nat_vs_fx"=top_nat_vs_fx$dma, "top_iui_vs_fx"=top_iui_vs_fx$dma)
xxl <- lapply(X = xl,run_mitch_rank)
xxxl <- lapply(xxl,function(xxl) { xxl$genenames <- rownames(xxl) ; xxl} )
xxll <- join_all(xxxl,by="genenames")
rownames(xxll) <- xxll$genenames
xxll$genenames=NULL
colnames(xxll) <- names(xl)
head(xxll)## nat_vs_fh top_nat_vs_fz top_nat_vs_iui top_fh_vs_fz top_nat_vs_fx
## A1BG -2.328540 -2.7641721 -2.283117 -0.2100917 -3.002424
## AAAS -1.252986 -0.8511462 -1.830471 0.3264645 -1.345819
## AACS -2.726117 -1.4619967 -1.955827 1.4664071 -2.609115
## AAGAB -2.777877 -2.4272634 -2.352994 0.3932834 -3.350195
## AAK1 -3.861492 -3.8903671 -3.604711 -0.1357104 -4.828415
## AAMP 3.857476 4.8644138 1.925273 0.8112798 4.894048
## top_iui_vs_fx
## A1BG 0.3201135
## AAAS 0.8200520
## AACS 0.4492229
## AAGAB 0.2645014
## AAK1 0.3446610
## AAMP 1.3837388capture.output(
res <- mitch_calc(xxll,genesets = genesets,priority = "significance")
, file = "/dev/null", append = FALSE,
type = c("output", "message"), split = FALSE)## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.head(res$enrichment_result,20)## set setSize
## 521 Metabolism of RNA 499
## 1089 Translation 225
## 836 Regulation of expression of SLITs and ROBOs 117
## 132 Cellular responses to stimuli 508
## 133 Cellular responses to stress 503
## 316 Formation of a pool of free 40S subunits 73
## 975 Signaling by ROBO receptors 146
## 484 L13a-mediated translational silencing of Ceruloplasmin expression 82
## 1051 Termination of O-glycan biosynthesis 10
## 283 Eukaryotic Translation Elongation 67
## 350 GTP hydrolysis and joining of the 60S ribosomal subunit 83
## 884 SRP-dependent cotranslational protein targeting to membrane 81
## 109 Cap-dependent Translation Initiation 90
## 284 Eukaryotic Translation Initiation 90
## 1056 The role of GTSE1 in G2/M progression after G2 checkpoint 51
## 1159 rRNA processing in the nucleus and cytosol 145
## 668 Peptide chain elongation 64
## 1158 rRNA processing 149
## 393 HIV Life Cycle 117
## 285 Eukaryotic Translation Termination 66
## pMANOVA s.nat_vs_fh s.top_nat_vs_fz s.top_nat_vs_iui s.top_fh_vs_fz
## 521 1.517731e-08 0.06702217 0.08233760 -0.029194170 0.032000808
## 1089 1.985678e-08 0.16192583 0.16421582 0.035548180 0.005754454
## 836 3.145372e-07 0.15830462 0.21264607 0.011955293 0.132819636
## 132 2.886466e-05 0.01292242 0.06139276 -0.036890677 0.102162809
## 133 3.284829e-05 0.01424516 0.06193892 -0.036005242 0.101328382
## 316 3.913103e-05 0.17280359 0.21168118 -0.013225878 0.080605683
## 975 4.286086e-05 0.10826723 0.15371044 -0.003683594 0.109290295
## 484 6.519158e-05 0.18028341 0.22171317 0.025110610 0.078248190
## 1051 7.296686e-05 0.63021203 0.62138582 0.498853607 0.446302120
## 283 1.009072e-04 0.15337192 0.20101432 -0.036579348 0.124422784
## 350 1.115067e-04 0.17803840 0.21521737 0.026588775 0.066789644
## 884 1.168181e-04 0.15990089 0.16631469 -0.012239708 0.016728357
## 109 1.215632e-04 0.15596013 0.19846806 0.013276056 0.065707499
## 284 1.215632e-04 0.15596013 0.19846806 0.013276056 0.065707499
## 1056 1.279284e-04 0.22477508 0.26639580 0.061440306 0.124474647
## 1159 1.333694e-04 0.08148174 0.10225369 -0.060113926 0.058787393
## 668 1.787408e-04 0.15000191 0.19182584 -0.045027670 0.091652428
## 1158 2.071847e-04 0.08201069 0.10040871 -0.055683013 0.051294837
## 393 2.574990e-04 -0.02823055 -0.01763927 -0.172723208 0.005526189
## 285 3.240532e-04 0.15474921 0.18108542 -0.036264079 0.056011601
## s.top_nat_vs_fx s.top_iui_vs_fx p.nat_vs_fh p.top_nat_vs_fz
## 521 0.07217723 0.1501410 1.150981e-02 1.906037e-03
## 1089 0.16476250 0.2063767 3.182326e-05 2.455025e-05
## 836 0.18571641 0.3034057 3.193452e-03 7.454113e-05
## 132 0.04044698 0.1221524 6.232467e-01 1.958563e-02
## 133 0.04147520 0.1223709 5.898788e-01 1.907977e-02
## 316 0.19467888 0.3279111 1.083802e-02 1.800172e-03
## 975 0.13026170 0.2351990 2.450980e-02 1.405822e-03
## 484 0.20472351 0.2903349 4.861509e-03 5.330589e-04
## 1051 0.64863549 0.2832505 5.592014e-04 6.681828e-04
## 283 0.17898873 0.3408194 3.023406e-02 4.505722e-03
## 350 0.20075560 0.2789753 5.145866e-03 7.187873e-04
## 884 0.16002957 0.2829414 1.304900e-02 9.821960e-03
## 109 0.18106668 0.2675486 1.073928e-02 1.167465e-03
## 284 0.18106668 0.2675486 1.073928e-02 1.167465e-03
## 1056 0.24498618 0.3563330 5.548946e-03 1.012282e-03
## 1159 0.09164426 0.2185204 9.163966e-02 3.426717e-02
## 668 0.17270542 0.3365902 3.829356e-02 8.060663e-03
## 1158 0.09124699 0.2117996 8.531067e-02 3.513848e-02
## 393 -0.02633005 0.1826220 5.991028e-01 7.425595e-01
## 285 0.16923752 0.3143928 2.999143e-02 1.109620e-02
## p.top_nat_vs_iui p.top_fh_vs_fz p.top_nat_vs_fx p.top_iui_vs_fx s.dist
## 521 0.271107526 0.2276928619 0.0065035510 1.477058e-08 0.2022367
## 1089 0.361304863 0.8825226896 0.0000230643 1.136372e-07 0.3524509
## 836 0.823829332 0.0133741373 0.0005415486 1.564245e-08 0.4632583
## 132 0.160770790 0.0001022261 0.1241218493 3.380821e-06 0.1796981
## 133 0.173062522 0.0001253147 0.1165466279 3.607832e-06 0.1797164
## 316 0.845412339 0.2347048656 0.0040976758 1.318721e-06 0.4762014
## 975 0.939013557 0.0231893815 0.0068080546 1.017944e-06 0.3458227
## 484 0.694942639 0.2216864400 0.0013837654 5.718087e-06 0.4632691
## 1051 0.006311967 0.0145528901 0.0003828858 1.210123e-01 1.3161637
## 283 0.605312498 0.0787622938 0.0114367660 1.451949e-06 0.4784805
## 350 0.676134057 0.2940046197 0.0016054545 1.158888e-05 0.4486803
## 884 0.849317361 0.7951227573 0.0129759923 1.112474e-05 0.3991539
## 109 0.828112152 0.2825339944 0.0030573493 1.199127e-05 0.4154206
## 284 0.828112152 0.2825339944 0.0030573493 1.199127e-05 0.4154206
## 1056 0.448497074 0.1246415845 0.0025045846 1.093598e-05 0.5724953
## 1159 0.213345023 0.2236204508 0.0577977259 6.017680e-06 0.2833982
## 668 0.534058752 0.2056065680 0.0170631310 3.307468e-06 0.4613518
## 1158 0.242709650 0.2818510813 0.0555573445 8.761827e-06 0.2751809
## 393 0.001294656 0.9180388121 0.6239211079 6.698228e-04 0.2549824
## 285 0.611095557 0.4322041577 0.0176230977 1.030402e-05 0.4343683
## SD p.adjustMANOVA
## 521 0.05920567 1.156657e-05
## 1089 0.08161376 1.156657e-05
## 836 0.09625153 1.221453e-04
## 132 0.05843274 7.133272e-03
## 133 0.05789281 7.133272e-03
## 316 0.11705340 7.133272e-03
## 975 0.07750373 7.133272e-03
## 484 0.09778916 9.445155e-03
## 1051 0.14204880 9.445155e-03
## 283 0.12222177 9.710958e-03
## 350 0.09557243 9.710958e-03
## 884 0.10914356 9.710958e-03
## 109 0.09264157 9.710958e-03
## 284 0.09264157 9.710958e-03
## 1056 0.10522764 9.710958e-03
## 1159 0.08930447 9.710958e-03
## 668 0.12531578 1.224900e-02
## 1158 0.08619960 1.340945e-02
## 393 0.11355948 1.578876e-02
## 285 0.11941206 1.883178e-02unlink("estill_multi_mitch.pdf")
capture.output(
mitch_plots(res,outfile="estill_multi_mitch.pdf")
, file = "/dev/null", append = FALSE,
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- repeat elements / GWAM
References
Estill MS, Bolnick JM, Waterland RA, Bolnick AD, Diamond MP, Krawetz SA. Assisted reproductive technology alters deoxyribonucleic acid methylation profiles in bloodspots of newborn infants. Fertil Steril. 2016 Sep 1;106(3):629-639.e10. doi: 10.1016/j.fertnstert.2016.05.006. Epub 2016 Jun 8. PubMed PMID: 27288894.
Phipson B, Maksimovic J, Oshlack A. missMethyl: an R package for analyzing data from Illumina’s HumanMethylation450 platform. Bioinformatics. 2016 Jan 15;32(2):286-8. doi: 10.1093/bioinformatics/btv560. Epub 2015 Sep 30. PubMed PMID: 26424855.
Sofer T, Schifano ED, Hoppin JA, Hou L, Baccarelli AA. A-clustering: a novel method for the detection of co-regulated methylation regions, and regions associated with exposure. Bioinformatics. 2013 Nov 15;29(22):2884-91. doi: 10.1093/bioinformatics/btt498. Epub 2013 Aug 29. PubMed PMID: 23990415; PubMed Central PMCID: PMC3810849.
Tian Y, Morris TJ, Webster AP, Yang Z, Beck S, Feber A, Teschendorff AE. ChAMP: updated methylation analysis pipeline for Illumina BeadChips. Bioinformatics. 2017 Dec 15;33(24):3982-3984. doi: 10.1093/bioinformatics/btx513. PubMed PMID: 28961746; PubMed Central PMCID: PMC5860089.
Session info
sessionInfo()## R version 4.2.0 (2022-04-22)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] grid parallel stats4 stats graphics grDevices utils
## [8] datasets methods base
##
## other attached packages:
## [1] mitch_1.8.0
## [2] ENmix_1.32.0
## [3] doParallel_1.0.17
## [4] qqman_0.1.8
## [5] RCircos_1.2.2
## [6] beeswarm_0.4.0
## [7] forestplot_2.0.1
## [8] checkmate_2.1.0
## [9] magrittr_2.0.3
## [10] reshape2_1.4.4
## [11] gplots_3.1.3
## [12] eulerr_6.1.1
## [13] GEOquery_2.64.2
## [14] RColorBrewer_1.1-3
## [15] IlluminaHumanMethylation450kmanifest_0.4.0
## [16] topconfects_1.12.0
## [17] DMRcatedata_2.14.0
## [18] ExperimentHub_2.4.0
## [19] AnnotationHub_3.4.0
## [20] BiocFileCache_2.4.0
## [21] dbplyr_2.1.1
## [22] DMRcate_2.10.0
## [23] limma_3.52.1
## [24] missMethyl_1.30.0
## [25] IlluminaHumanMethylationEPICanno.ilm10b4.hg19_0.6.0
## [26] IlluminaHumanMethylation450kanno.ilmn12.hg19_0.6.1
## [27] minfi_1.42.0
## [28] bumphunter_1.38.0
## [29] locfit_1.5-9.5
## [30] iterators_1.0.14
## [31] foreach_1.5.2
## [32] Biostrings_2.64.0
## [33] XVector_0.36.0
## [34] SummarizedExperiment_1.26.1
## [35] Biobase_2.56.0
## [36] MatrixGenerics_1.8.0
## [37] matrixStats_0.62.0
## [38] GenomicRanges_1.48.0
## [39] GenomeInfoDb_1.32.2
## [40] IRanges_2.30.0
## [41] S4Vectors_0.34.0
## [42] BiocGenerics_0.42.0
## [43] R.utils_2.11.0
## [44] R.oo_1.24.0
## [45] R.methodsS3_1.8.1
## [46] plyr_1.8.7
##
## loaded via a namespace (and not attached):
## [1] rappdirs_0.3.3 rtracklayer_1.56.0
## [3] GGally_2.1.2 tidyr_1.2.0
## [5] ggplot2_3.3.6 bit64_4.0.5
## [7] knitr_1.39 DelayedArray_0.22.0
## [9] data.table_1.14.2 rpart_4.1.16
## [11] KEGGREST_1.36.0 RCurl_1.98-1.6
## [13] AnnotationFilter_1.20.0 generics_0.1.2
## [15] GenomicFeatures_1.48.1 preprocessCore_1.58.0
## [17] RSQLite_2.2.14 bit_4.0.4
## [19] tzdb_0.3.0 xml2_1.3.3
## [21] httpuv_1.6.5 assertthat_0.2.1
## [23] xfun_0.31 hms_1.1.1
## [25] jquerylib_0.1.4 evaluate_0.15
## [27] promises_1.2.0.1 fansi_1.0.3
## [29] restfulr_0.0.13 scrime_1.3.5
## [31] progress_1.2.2 caTools_1.18.2
## [33] readxl_1.4.0 DBI_1.1.2
## [35] geneplotter_1.74.0 htmlwidgets_1.5.4
## [37] reshape_0.8.9 purrr_0.3.4
## [39] ellipsis_0.3.2 dplyr_1.0.9
## [41] backports_1.4.1 permute_0.9-7
## [43] calibrate_1.7.7 annotate_1.74.0
## [45] biomaRt_2.52.0 sparseMatrixStats_1.8.0
## [47] vctrs_0.4.1 ensembldb_2.20.1
## [49] withr_2.5.0 cachem_1.0.6
## [51] Gviz_1.40.1 BSgenome_1.64.0
## [53] GenomicAlignments_1.32.0 prettyunits_1.1.1
## [55] mclust_5.4.9 cluster_2.1.3
## [57] RPMM_1.25 lazyeval_0.2.2
## [59] crayon_1.5.1 genefilter_1.78.0
## [61] labeling_0.4.2 edgeR_3.38.1
## [63] pkgconfig_2.0.3 nlme_3.1-157
## [65] ProtGenerics_1.28.0 nnet_7.3-17
## [67] rlang_1.0.2 lifecycle_1.0.1
## [69] filelock_1.0.2 dichromat_2.0-0.1
## [71] polyclip_1.10-0 cellranger_1.1.0
## [73] rngtools_1.5.2 base64_2.0
## [75] Matrix_1.4-1 Rhdf5lib_1.18.2
## [77] base64enc_0.1-3 png_0.1-7
## [79] rjson_0.2.21 bitops_1.0-7
## [81] KernSmooth_2.23-20 rhdf5filters_1.8.0
## [83] blob_1.2.3 DelayedMatrixStats_1.18.0
## [85] doRNG_1.8.2 stringr_1.4.0
## [87] nor1mix_1.3-0 readr_2.1.2
## [89] jpeg_0.1-9 scales_1.2.0
## [91] memoise_2.0.1 zlibbioc_1.42.0
## [93] compiler_4.2.0 BiocIO_1.6.0
## [95] illuminaio_0.38.0 Rsamtools_2.12.0
## [97] cli_3.3.0 DSS_2.44.0
## [99] htmlTable_2.4.0 Formula_1.2-4
## [101] MASS_7.3-57 tidyselect_1.1.2
## [103] stringi_1.7.6 highr_0.9
## [105] yaml_2.3.5 askpass_1.1
## [107] latticeExtra_0.6-29 sass_0.4.1
## [109] VariantAnnotation_1.42.1 tools_4.2.0
## [111] rstudioapi_0.13 foreign_0.8-82
## [113] bsseq_1.32.0 gridExtra_2.3
## [115] farver_2.1.0 digest_0.6.29
## [117] BiocManager_1.30.17 shiny_1.7.1
## [119] quadprog_1.5-8 Rcpp_1.0.8.3
## [121] siggenes_1.70.0 BiocVersion_3.15.2
## [123] later_1.3.0 org.Hs.eg.db_3.15.0
## [125] httr_1.4.3 AnnotationDbi_1.58.0
## [127] biovizBase_1.44.0 colorspace_2.0-3
## [129] polylabelr_0.2.0 XML_3.99-0.9
## [131] splines_4.2.0 statmod_1.4.36
## [133] multtest_2.52.0 xtable_1.8-4
## [135] jsonlite_1.8.0 dynamicTreeCut_1.63-1
## [137] R6_2.5.1 echarts4r_0.4.3
## [139] Hmisc_4.7-0 pillar_1.7.0
## [141] htmltools_0.5.2 mime_0.12
## [143] glue_1.6.2 fastmap_1.1.0
## [145] BiocParallel_1.30.2 interactiveDisplayBase_1.34.0
## [147] beanplot_1.3.1 codetools_0.2-18
## [149] utf8_1.2.2 lattice_0.20-45
## [151] bslib_0.3.1 tibble_3.1.7
## [153] curl_4.3.2 gtools_3.9.2
## [155] openssl_2.0.1 survival_3.3-1
## [157] rmarkdown_2.14 munsell_0.5.0
## [159] rhdf5_2.40.0 GenomeInfoDbData_1.2.8
## [161] HDF5Array_1.24.0 impute_1.70.0
## [163] gtable_0.3.0END OF REPORT