Characterising DNA methylation patterns of ovarian cancer
Mark Ziemann
2024-12-13
Source: https://github.com/markziemann/TODO
Introduction
Here we are establishing the methylation profile of ovarian cancer versus healthy tissue in order to understand whether this signature can be observed in the blood of individuals who will soon be diagnosed with the disease.
baseDir=getwd()
dataDir=paste(baseDir,"/idat",sep="")
suppressPackageStartupMessages({
library("missMethyl")
library("limma")
library("minfi")
library("IlluminaHumanMethylation450kanno.ilmn12.hg19")
library("IlluminaHumanMethylationEPICanno.ilm10b4.hg19")
library("RColorBrewer")
library("matrixStats")
library("gplots")
library("WGCNA")
library("FlowSorted.Blood.450k")
library("reshape2")
library("ggplot2")
library("missMethyl")
library("DMRcate")
#library("FlowSorted.Blood.EPIC")
library("mitch")
library("kableExtra")
library("vioplot")
library("RhpcBLASctl")
})
source("../../meth_functions.R")
RhpcBLASctl::blas_set_num_threads(1)Load data
Load the annotation data and the Epic methylation data.
This analysis is to be conducted on Ubuntu with R4.
ann <- getAnnotation(IlluminaHumanMethylationEPICanno.ilm10b4.hg19)
anno <- ann
ann_sub <- ann[,c("chr","pos","strand","Name","Islands_Name",
"Relation_to_Island","UCSC_RefGene_Name","UCSC_RefGene_Group")]
targets_gen <- read.metharray.sheet(base=dataDir,pattern = "samplesheet.csv")## [read.metharray.sheet] Found the following CSV files:## [1] "/home/mark.ziemann@domain.internal.burnet.edu.au/projects/cancer_biomarkers/ovarian/GSE133556/idat/samplesheet.csv"targets_gen$Basename <- paste(targets_gen$SampleID,targets_gen$ChipID,sep="_")
#targets$ID = paste(targets$Sample_Group,targets_gen$Sample_Name,sep=".")
rgSet <- read.metharray.exp(base=dataDir, targets = targets_gen)
sampleNames(rgSet) <- targets_gen$SampleIDTesting MZ status
snpBetas <- getSnpBeta(rgSet)## Loading required package: IlluminaHumanMethylationEPICmanifestd <- dist(t(snpBetas))
hr <- hclust(d, method = "complete", members=NULL)
plot(hr,cex=0.5)
Quality control
detP <- detectionP(rgSet)
qcReport(rgSet, sampNames = targets_gen$Sample_Name, pdf="qc-report.pdf")## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
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## 2cols <- brewer.pal(4,"Set1")
barplot(apply(detP,2,mean),
col=as.numeric(factor(targets_gen$Sample_Name)),
las=2,cex.names= 0.8, cex.axis=0.75,
main="Mean detection p-values of probe signals",
ylab="Mean detection p-value")
barplot(apply(detP,2,mean),
col=as.numeric(factor(targets_gen$Sample_Name)),
las=2,cex.names= 0.8, cex.axis=0.75,ylim=c(0,0.010),
main="Mean detection p-values of probe signals",
ylab="Mean detection p-value")
Preprocessing
mset.raw <- preprocessRaw(rgSet)
mset <- preprocessQuantile(rgSet)## [preprocessQuantile] Mapping to genome.## Warning in .getSex(CN = CN, xIndex = xIndex, yIndex = yIndex, cutoff = cutoff):
## An inconsistency was encountered while determining sex. One possibility is that
## only one sex is present. We recommend further checks, for example with the
## plotSex function.## [preprocessQuantile] Fixing outliers.## [preprocessQuantile] Quantile normalizing.Data exploration
Using Multi-dimensional scaling (MDS) plots before filtering.
mdsPlot(mset.raw, numPositions = 1000, sampGroups = targets_gen$Sample_Name,
sampNames=targets_gen$SampleID,legendPos="bottom")
mdsPlot(getBeta(mset), numPositions = 1000, sampGroups = targets_gen$Sample_Name,
sampNames=targets_gen$SampleID,legendPos="bottom")
Filteringhead
None of the p-values are less than 0.05.
Now filter for high detection p-value and overlap with SNP.
Need to get a copy of the Xreact probes from Namitha. In the mean time I have downloaded from github - link below.
detP <- detectionP(rgSet)
badPcnt <- apply(detP,2,function(x) { length(which(x>0.01)) } )
badPcnt <- badPcnt[order(badPcnt)]
barplot(badPcnt,horiz=TRUE,las=1,cex.names=0.5)
barplot(tail(badPcnt,20),horiz=TRUE,las=1,cex.names=0.6)
badP <- apply(detP,1,function(x) { length(which(x>0.01)) } )
dim(detP)## [1] 866091 111length(which(badP>10))## [1] 6882keep <- which(badP<10)
mset[rownames(mset) %in% names(keep),]## class: GenomicRatioSet
## dim: 858245 111
## metadata(0):
## assays(2): M CN
## rownames(858245): cg14817997 cg26928153 ... cg07587934 cg16855331
## rowData names(0):
## colnames(111): GSM3911862 GSM3911863 ... GSM3912031 GSM3912032
## colData names(11): SampleID ChipID ... yMed predictedSex
## Annotation
## array: IlluminaHumanMethylationEPIC
## annotation: ilm10b4.hg19
## Preprocessing
## Method: Raw (no normalization or bg correction)
## minfi version: 1.50.0
## Manifest version: 0.3.0gmset <- mapToGenome(mset)
#remove SNPs
gmset <- dropLociWithSnps(gmset, snps = c("CpG", "SBE"))
#Removing cross-reactive probes
XURL="https://raw.githubusercontent.com/sirselim/illumina450k_filtering/master/EPIC/13059_2016_1066_MOESM1_ESM.csv"
Xreact <- read.csv(XURL)
noXreact <- !(featureNames(gmset) %in% Xreact$X)
gmset <- gmset[noXreact,]
#Removing probes on X and Y chromosomes
autosomes <- !(featureNames(gmset) %in% ann$Name[ann$chr %in% c("chrX","chrY")])
gmset <- gmset[autosomes,]
#getBeta
beta <- getM(gmset)
saveRDS(beta,"bl_beta.rds")
df <- data.frame(t(t(colMeans(beta))))
df## t.t.colMeans.beta...
## GSM3911862 -0.4474743
## GSM3911863 -0.4532306
## GSM3911864 -0.4496271
## GSM3911865 -0.4496127
## GSM3911866 -0.4469514
## GSM3911867 -0.4421429
## GSM3911868 -0.4485742
## GSM3911869 -0.4521518
## GSM3911870 -0.4494008
## GSM3911871 -0.4463365
## GSM3911872 -0.4447413
## GSM3911873 -0.4541568
## GSM3911874 -0.4509595
## GSM3911875 -0.4420105
## GSM3911876 -0.4478256
## GSM3911877 -0.4530201
## GSM3911878 -0.4480999
## GSM3911879 -0.4499674
## GSM3911880 -0.4472988
## GSM3911881 -0.4502712
## GSM3911882 -0.4457314
## GSM3911883 -0.4486873
## GSM3911884 -0.4463951
## GSM3911885 -0.4515549
## GSM3911886 -0.4443727
## GSM3911887 -0.4436849
## GSM3911888 -0.4457299
## GSM3911889 -0.4468304
## GSM3911890 -0.4415188
## GSM3911891 -0.4442675
## GSM3911892 -0.4474649
## GSM3911893 -0.4572643
## GSM3911894 -0.4442860
## GSM3911895 -0.4538831
## GSM3911896 -0.4585926
## GSM3911897 -0.4450196
## GSM3911898 -0.4489041
## GSM3911899 -0.4501816
## GSM3911900 -0.4540031
## GSM3911901 -0.4443511
## GSM3911902 -0.4549322
## GSM3911903 -0.4553253
## GSM3911904 -0.4531865
## GSM3911905 -0.4493668
## GSM3911906 -0.4445540
## GSM3911907 -0.4490076
## GSM3911908 -0.4475309
## GSM3911909 -0.4441517
## GSM3911910 -0.4386959
## GSM3911911 -0.4571929
## GSM3911912 -0.4485718
## GSM3911913 -0.4482994
## GSM3911914 -0.4451191
## GSM3911915 -0.4514618
## GSM3911916 -0.4509893
## GSM3911917 -0.4446942
## GSM3911918 -0.4533041
## GSM3911919 -0.4464384
## GSM3911920 -0.4492698
## GSM3911921 -0.4436142
## GSM3911922 -0.4504614
## GSM3911923 -0.4506988
## GSM3911924 -0.4494134
## GSM3911925 -0.4396179
## GSM3911926 -0.4461498
## GSM3911927 -0.4496587
## GSM3911928 -0.4507816
## GSM3911929 -0.4456993
## GSM3911930 -0.4502994
## GSM3911931 -0.4426471
## GSM3911932 -0.4470735
## GSM3911933 -0.4426173
## GSM3911934 -0.4436359
## GSM3911935 -0.4437165
## GSM3911936 -0.4397457
## GSM3911937 -0.4420491
## GSM3911938 -0.4418171
## GSM3911939 -0.4492959
## GSM3911940 -0.4481805
## GSM3911941 -0.4441673
## GSM3911942 -0.4552438
## GSM3911943 -0.4510021
## GSM3911944 -0.4403518
## GSM3911945 -0.4409476
## GSM3911946 -0.4482015
## GSM3911947 -0.4515890
## GSM3911948 -0.4485105
## GSM3911949 -0.4493624
## GSM3911950 -0.4509996
## GSM3911951 -0.4439952
## GSM3912012 -0.4534565
## GSM3912013 -0.4438707
## GSM3912014 -0.4523521
## GSM3912015 -0.4552149
## GSM3912016 -0.4518657
## GSM3912017 -0.4374802
## GSM3912018 -0.4531850
## GSM3912019 -0.4467999
## GSM3912020 -0.4465207
## GSM3912021 -0.4489707
## GSM3912022 -0.4404712
## GSM3912023 -0.4445440
## GSM3912024 -0.4552310
## GSM3912025 -0.4408353
## GSM3912026 -0.4401412
## GSM3912027 -0.4534165
## GSM3912028 -0.4468481
## GSM3912029 -0.4490814
## GSM3912030 -0.4467013
## GSM3912031 -0.4522135
## GSM3912032 -0.4503872#Relative log expression (RLE plot)
mvals <- getM(gmset)
medSq <- apply(mvals, 1, median)
YSq <- mvals - medSq
boxplot(YSq,outline=FALSE,ylim=c(-1.5,1.5),
ylab="Relative Log Methylation Value", cex=0.2 )
MDS plots generation after filtering
pal <- brewer.pal(8, "Dark2")
cols <- gsub("2","blue",gsub("1","red",as.character(as.numeric(factor(targets_gen$Sample_Name)))))
mds1Sq <- plotMDS(getBeta(gmset), top=1000, gene.selection="common",dim.plot=c(1,2), col=cols)
mds2Sq <- plotMDS(getBeta(gmset), top=1000, gene.selection="common",dim.plot=c(1,3), col=cols)
mds3Sq <- plotMDS(getBeta(gmset), top=1000, gene.selection="common",dim.plot=c(2,3), col=cols)
mds4Sq <- plotMDS(getBeta(gmset), top=1000, gene.selection="common",dim.plot=c(3,4), col=cols)
plotMDS(mds1Sq, xlab="Dimension 1", ylab="Dimension 2",
col=cols, dim=c(1,2), labels=targets_gen$Sample_ID)
legend("bottomright",bg="white",col=pal,cex=.7,pch=1,legend=0:1)
plotMDS(mds2Sq, xlab="Dimension 1", ylab="Dimension 3",
col=cols, dim=c(1,3), labels=targets_gen$Sample_ID)
legend("bottomright",bg="white",col=pal,cex=.7,pch=1,legend=0:1)
plotMDS(mds3Sq, xlab="Dimension 2", ylab="Dimension 3",
col=cols,dim=c(2,3), labels=targets_gen$Sample_ID)
legend("bottomright",bg="white",col=pal,cex=.7,pch=1,legend=0:1)
plotMDS(mds4Sq, xlab="Dimension 3", ylab="Dimension 4",
col=cols,dim=c(3,4), labels=targets_gen$Sample_ID)
legend("bottomright",bg="white",col=pal,cex=.7,pch=1,legend=0:1)
Regression analysis for cancer
cancer <- factor(grepl("High",targets_gen$Sample_Name))
design <- model.matrix(~ cancer )
fit <- lmFit(mvals, design)
fit <- eBayes(fit)
summary(decideTests(fit))## (Intercept) cancerTRUE
## Down 317944 143483
## NotSig 86204 475988
## Up 372373 157050top <- topTable(fit,coef="cancerTRUE",num=Inf, sort.by = "P")
nrow(subset(top,adj.P.Val < 0.05))## [1] 300533nrow(subset(top,P.Value < 1e-4))## [1] 113285output <-merge(ann_sub,top,by.x="Name",by.y="row.names")
output <- output[order(output$P.Value),]
write.csv(output, file="GSE133556_limma.csv",row.names=FALSE)
output <- subset(output,P.Value<1e-4)
head(output,30) %>% kbl() %>% kable_paper("hover", full_width = F)| Name | chr | pos | strand | Islands_Name | Relation_to_Island | UCSC_RefGene_Name | UCSC_RefGene_Group | logFC | AveExpr | t | P.Value | adj.P.Val | B |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| cg15294607 | chr2 | 28613033 |
|
chr2:28612964-28616748 | Island | -5.909159 | -2.3164465 | -45.33774 | 0 | 0 | 148.75169 | ||
| cg05677464 | chr17 | 49171512 |
|
OpenSea | SPAG9;SPAG9;SPAG9 | Body;Body;Body | 4.068537 | 0.9576384 | 20.25892 | 0 | 0 | 77.75095 | |
| cg20111584 | chr21 | 16774725 |
|
OpenSea | 3.095340 | 1.4091866 | 18.31758 | 0 | 0 | 69.46174 | |||
| cg25059588 | chr2 | 220286185 |
|
chr2:220283200-220283750 | S_Shelf | DES | Body | 2.016165 | 2.7122903 | 17.94317 | 0 | 0 | 67.80341 |
| cg19740353 | chr6 | 53394492 |
|
OpenSea | GCLC | Body | 2.812570 | 1.1206037 | 17.76753 | 0 | 0 | 67.01877 | |
| cg22216738 | chr19 | 18721711 |
|
chr19:18721523-18721869 | Island | -2.664257 | -4.0405298 | -17.74857 | 0 | 0 | 66.93380 | ||
| cg21718261 | chr8 | 71086222 |
|
OpenSea | NCOA2 | Body | 2.852690 | 2.4928273 | 17.50528 | 0 | 0 | 65.83933 | |
| cg26562007 | chr6 | 111920964 |
|
OpenSea | TRAF3IP2-AS1;TRAF3IP2-AS1;TRAF3IP2;TRAF3IP2;TRAF3IP2 | Body;Body;Body;5’UTR;5’UTR | 3.067066 | 0.6996643 | 17.13922 | 0 | 0 | 64.17720 | |
| cg08972369 | chr3 | 124687302 |
|
OpenSea | HEG1 | 3’UTR | 2.200948 | 2.2091299 | 16.96616 | 0 | 0 | 63.38499 | |
| cg19545040 | chr10 | 103700513 |
|
OpenSea | C10orf76 | Body | 2.556039 | 1.5762841 | 16.96554 | 0 | 0 | 63.38215 | |
| cg04282788 | chr1 | 210407473 |
|
chr1:210407401-210407637 | Island | C1orf133;SERTAD4 | TSS200;5’UTR | -1.408015 | -3.5279296 | -16.77345 | 0 | 0 | 62.49804 |
| cg21383646 | chr8 | 38599320 |
|
OpenSea | TACC1 | 5’UTR | 3.369892 | 1.2981562 | 16.74994 | 0 | 0 | 62.38950 | |
| cg18951537 | chr17 | 11950516 |
|
OpenSea | MAP2K4 | Body | 3.259542 | 1.0628564 | 16.72585 | 0 | 0 | 62.27821 | |
| cg22880112 | chr3 | 64331448 |
|
OpenSea | 3.719726 | 0.9094945 | 16.67969 | 0 | 0 | 62.06471 | |||
| cg26248588 | chr3 | 123409971 |
|
OpenSea | MYLK-AS2;MYLK;MYLK;MYLK;MYLK | Body;Body;Body;Body;Body | 1.796483 | 0.8749342 | 16.63662 | 0 | 0 | 61.86523 | |
| cg25576112 | chr6 | 43878504 |
|
OpenSea | LINC01512 | Body | 2.042443 | 0.3286338 | 16.58295 | 0 | 0 | 61.61635 | |
| cg19062021 | chr19 | 18617954 |
|
OpenSea | ELL | Body | 3.313115 | -0.8476797 | 16.43450 | 0 | 0 | 60.92582 | |
| cg03243768 | chr11 | 9115107 |
|
chr11:9112461-9113459 | S_Shore | FLJ46111 | TSS1500 | 3.204014 | 2.4494332 | 16.41020 | 0 | 0 | 60.81255 |
| cg05324501 | chr9 | 73090414 |
|
OpenSea | 2.808055 | 1.5967976 | 16.40526 | 0 | 0 | 60.78947 | |||
| cg17179492 | chr16 | 3149397 |
|
OpenSea | ZSCAN10;ZSCAN10;ZSCAN10 | TSS200;TSS200;TSS200 | 1.851535 | 0.7958453 | 16.22013 | 0 | 0 | 59.92351 | |
| cg08669766 | chr10 | 120016854 |
|
OpenSea | 3.084577 | 1.3221865 | 16.18097 | 0 | 0 | 59.73972 | |||
| cg27492625 | chr3 | 150987430 |
|
OpenSea | MED12L;P2RY14 | Body;5’UTR | 2.375843 | 1.0411449 | 15.91333 | 0 | 0 | 58.47832 | |
| cg18053228 | chr11 | 68853915 |
|
chr11:68849897-68850551 | S_Shelf | TPCN2 | Body | 2.204718 | 1.4630320 | 15.77200 | 0 | 0 | 57.80840 |
| cg20563852 | chr15 | 85396333 |
|
OpenSea | ALPK3 | Body | 3.242126 | 2.0539980 | 15.73514 | 0 | 0 | 57.63325 | |
| cg18239484 | chr4 | 152097630 |
|
OpenSea | SH3D19;SH3D19;SH3D19;SH3D19 | TSS1500;5’UTR;5’UTR;5’UTR | 2.680887 | 1.7962270 | 15.73409 | 0 | 0 | 57.62824 | |
| cg25990200 | chr1 | 233750794 |
|
chr1:233749373-233750314 | S_Shore | KCNK1 | Body | -2.083622 | -3.7323778 | -15.70048 | 0 | 0 | 57.46838 |
| cg11514338 | chr1 | 164543254 |
|
chr1:164545540-164545917 | N_Shelf | PBX1 | Body | 2.560031 | 1.9775870 | 15.59797 | 0 | 0 | 56.97991 |
| cg21424209 | chr6 | 148792600 |
|
OpenSea | SASH1 | Body | 2.834990 | 1.3891108 | 15.49288 | 0 | 0 | 56.47767 | |
| cg06376771 | chr12 | 16137468 |
|
OpenSea | DERA;DERA | Body;Body | 3.118326 | 1.7455728 | 15.45230 | 0 | 0 | 56.28336 | |
| cg09652142 | chr3 | 47932918 |
|
OpenSea | MAP4;MAP4;MAP4;MAP4 | Body;Body;Body;Body | 3.086762 | 1.7089041 | 15.44517 | 0 | 0 | 56.24918 |
saveRDS(design, "GSE133556_design.rds")
saveRDS(mvals, "GSE133556_mvals.rds")
saveRDS(top, "GSE133556_limma.rds")Session information
sessionInfo()## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.5 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
##
## 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
##
## time zone: Australia/Melbourne
## tzcode source: system (glibc)
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] IlluminaHumanMethylationEPICmanifest_0.3.0
## [2] RhpcBLASctl_0.23-42
## [3] vioplot_0.5.0
## [4] zoo_1.8-12
## [5] sm_2.2-6.0
## [6] kableExtra_1.4.0
## [7] mitch_1.19.3
## [8] DMRcate_3.0.10
## [9] ggplot2_3.5.1
## [10] reshape2_1.4.4
## [11] FlowSorted.Blood.450k_1.42.0
## [12] WGCNA_1.73
## [13] fastcluster_1.2.6
## [14] dynamicTreeCut_1.63-1
## [15] gplots_3.2.0
## [16] RColorBrewer_1.1-3
## [17] limma_3.60.6
## [18] missMethyl_1.38.0
## [19] IlluminaHumanMethylationEPICanno.ilm10b4.hg19_0.6.0
## [20] IlluminaHumanMethylation450kanno.ilmn12.hg19_0.6.1
## [21] minfi_1.50.0
## [22] bumphunter_1.46.0
## [23] locfit_1.5-9.10
## [24] iterators_1.0.14
## [25] foreach_1.5.2
## [26] Biostrings_2.72.1
## [27] XVector_0.44.0
## [28] SummarizedExperiment_1.34.0
## [29] Biobase_2.64.0
## [30] MatrixGenerics_1.16.0
## [31] matrixStats_1.4.1
## [32] GenomicRanges_1.56.2
## [33] GenomeInfoDb_1.40.1
## [34] IRanges_2.38.1
## [35] S4Vectors_0.42.1
## [36] BiocGenerics_0.50.0
##
## loaded via a namespace (and not attached):
## [1] ProtGenerics_1.36.0 bitops_1.0-9
## [3] httr_1.4.7 doParallel_1.0.17
## [5] tools_4.4.1 doRNG_1.8.6
## [7] backports_1.5.0 utf8_1.2.4
## [9] R6_2.5.1 HDF5Array_1.32.1
## [11] lazyeval_0.2.2 Gviz_1.48.0
## [13] rhdf5filters_1.16.0 permute_0.9-7
## [15] withr_3.0.2 GGally_2.2.1
## [17] prettyunits_1.2.0 gridExtra_2.3
## [19] base64_2.0.2 preprocessCore_1.66.0
## [21] cli_3.6.3 sass_0.4.9
## [23] readr_2.1.5 genefilter_1.86.0
## [25] askpass_1.2.1 systemfonts_1.1.0
## [27] Rsamtools_2.20.0 foreign_0.8-87
## [29] svglite_2.1.3 siggenes_1.78.0
## [31] illuminaio_0.46.0 R.utils_2.12.3
## [33] dichromat_2.0-0.1 scrime_1.3.5
## [35] BSgenome_1.72.0 rstudioapi_0.17.1
## [37] impute_1.80.0 RSQLite_2.3.8
## [39] generics_0.1.3 BiocIO_1.14.0
## [41] gtools_3.9.5 dplyr_1.1.4
## [43] GO.db_3.19.1 Matrix_1.7-1
## [45] interp_1.1-6 fansi_1.0.6
## [47] abind_1.4-8 R.methodsS3_1.8.2
## [49] lifecycle_1.0.4 edgeR_4.2.2
## [51] yaml_2.3.10 rhdf5_2.48.0
## [53] SparseArray_1.4.8 BiocFileCache_2.12.0
## [55] grid_4.4.1 blob_1.2.4
## [57] promises_1.3.1 ExperimentHub_2.12.0
## [59] crayon_1.5.3 lattice_0.22-6
## [61] echarts4r_0.4.5 GenomicFeatures_1.56.0
## [63] annotate_1.82.0 KEGGREST_1.44.1
## [65] pillar_1.9.0 knitr_1.49
## [67] beanplot_1.3.1 rjson_0.2.23
## [69] codetools_0.2-20 glue_1.8.0
## [71] data.table_1.16.2 vctrs_0.6.5
## [73] png_0.1-8 gtable_0.3.6
## [75] cachem_1.1.0 xfun_0.49
## [77] mime_0.12 S4Arrays_1.4.1
## [79] survival_3.7-0 statmod_1.5.0
## [81] nlme_3.1-166 bit64_4.5.2
## [83] bsseq_1.40.0 progress_1.2.3
## [85] filelock_1.0.3 bslib_0.8.0
## [87] nor1mix_1.3-3 KernSmooth_2.23-24
## [89] rpart_4.1.23 colorspace_2.1-1
## [91] DBI_1.2.3 Hmisc_5.2-0
## [93] nnet_7.3-19 tidyselect_1.2.1
## [95] bit_4.5.0 compiler_4.4.1
## [97] curl_6.0.1 httr2_1.0.7
## [99] htmlTable_2.4.3 xml2_1.3.6
## [101] DelayedArray_0.30.1 rtracklayer_1.64.0
## [103] checkmate_2.3.2 scales_1.3.0
## [105] caTools_1.18.3 quadprog_1.5-8
## [107] rappdirs_0.3.3 stringr_1.5.1
## [109] digest_0.6.37 rmarkdown_2.29
## [111] GEOquery_2.72.0 htmltools_0.5.8.1
## [113] pkgconfig_2.0.3 jpeg_0.1-10
## [115] base64enc_0.1-3 sparseMatrixStats_1.16.0
## [117] dbplyr_2.5.0 fastmap_1.2.0
## [119] ensembldb_2.28.1 rlang_1.1.4
## [121] htmlwidgets_1.6.4 UCSC.utils_1.0.0
## [123] shiny_1.9.1 DelayedMatrixStats_1.26.0
## [125] jquerylib_0.1.4 jsonlite_1.8.9
## [127] BiocParallel_1.38.0 mclust_6.1.1
## [129] R.oo_1.27.0 VariantAnnotation_1.50.0
## [131] RCurl_1.98-1.16 magrittr_2.0.3
## [133] Formula_1.2-5 GenomeInfoDbData_1.2.12
## [135] Rhdf5lib_1.26.0 munsell_0.5.1
## [137] Rcpp_1.0.13-1 stringi_1.8.4
## [139] zlibbioc_1.50.0 MASS_7.3-61
## [141] AnnotationHub_3.12.0 plyr_1.8.9
## [143] org.Hs.eg.db_3.19.1 ggstats_0.7.0
## [145] deldir_2.0-4 splines_4.4.1
## [147] multtest_2.60.0 hms_1.1.3
## [149] rngtools_1.5.2 biomaRt_2.60.1
## [151] BiocVersion_3.19.1 XML_3.99-0.17
## [153] evaluate_1.0.1 latticeExtra_0.6-30
## [155] biovizBase_1.52.0 BiocManager_1.30.25
## [157] httpuv_1.6.15 tzdb_0.4.0
## [159] tidyr_1.3.1 openssl_2.2.2
## [161] purrr_1.0.2 reshape_0.8.9
## [163] xtable_1.8-4 restfulr_0.0.15
## [165] AnnotationFilter_1.28.0 later_1.4.0
## [167] viridisLite_0.4.2 tibble_3.2.1
## [169] beeswarm_0.4.0 memoise_2.0.1.9000
## [171] AnnotationDbi_1.66.0 GenomicAlignments_1.40.0
## [173] cluster_2.1.6