This script loads in the differentially expressed genes in cis of muscle CpGs and analyses if their expression changes with DNAm.
Load packages
library(edgeR)
library(cinaR)
library(limma)
library(tidyverse)
library(GenomicFeatures)
library(AnnotationHub)
library(SummarizedExperiment)
library(lme4)
library(lmerTest)Load df which maps CpGs to genes within 100kb
load('../GOTO_Data/DEGs/Muscle-100kb.Rdata')
head(df)## cpg gene_ens gene
## 1 cg26998535 ENSG00000120029 C10orf76
## 2 cg26998535 ENSG00000120049 KCNIP2
## 3 cg03045139 ENSG00000085365 SCAMP1
## 4 cg03045139 ENSG00000145685 LHFPL2
## 5 cg19003556 ENSG00000171408 PDE7B
## 6 cg00353432 ENSG00000064393 HIPK2Load results of DEG analysis
load('../GOTO_Data/DEGs/Muscle-DEG.Rdata')Merge
deg$gene_ens <- rownames(deg)
deg <- deg %>%
dplyr::select(gene_ens,
logFC, deg_p = pvalue,
deg_padj = padj)
df <- inner_join(df, deg, by='gene_ens')
head(df)## cpg gene_ens gene logFC deg_p deg_padj
## 1 cg26998535 ENSG00000120029 C10orf76 -0.15702014 4.918806e-06 5.868644e-05
## 2 cg03045139 ENSG00000085365 SCAMP1 -0.06742307 1.008167e-02 2.422401e-02
## 3 cg03045139 ENSG00000145685 LHFPL2 0.36905967 1.564875e-07 4.540984e-06
## 4 cg03982376 ENSG00000055208 TAB2 -0.17684282 2.927471e-08 2.070958e-06
## 5 cg03982376 ENSG00000055211 GINM1 -0.09815442 3.029579e-04 1.397646e-03
## 6 cg13290921 ENSG00000196233 LCOR 0.11530761 6.993477e-03 1.805017e-02Load top CpG results and methData
load('../GOTO_Data/GOTO_methData-filtered.Rdata')Save only muscle samples
methData <- methData[ , methData$tissue == 'muscle']Convert betas to data frame
beta_rows <- methData$Basename
betas <- as.data.frame(t(assay(methData)))
rownames(betas) <- beta_rowsSave only CpGs to be tested
betas <- betas %>% dplyr::select(any_of(df$cpg))
dim(betas)## [1] 160 95load("../GOTO_Data/GOTO_targets-filtered.Rdata")Filter muscle
targets <- targets %>%
filter(tissue == 'muscle')Save covariates
targets <- targets %>%
dplyr::select(IOP2_ID, timepoint, age, sex, smoke,
plate, array_row, Basename) %>%
mutate(ID = paste0(IOP2_ID, '_', timepoint))Load functions from Erik
source('../GOTO_Data/RNAseq/goto.rnaseq.functions.R')Load RNAseq data
pathIN_dat <- "../GOTO_Data/RNAseq/merge.gene.counts_biopet_13052016.RData"
pathIN_cov <- "../GOTO_Data/RNAseq/muscle_QC_covariates_filesv2.csv"
filt.samp <- "tissue_muscle|qc_sexswitch|qc_multdim2|qc_rep1|complete_pairs"
dat1 <- read.gotornaseq(pathIN_dat = pathIN_dat,
pathIN_cov,
filt.samp = filt.samp,
type = 'voom-export',
quiet = FALSE)## ||| PREPARING GOTO RNASEQ DATA
## || READING DATA
## | Loading RNASEQ .. OK!
## [555 samples x 56520 features]
## | Reading COVARIATES .. OK!
## [maintaining 174 samples x 117 features]
## | Merging data .. OK!
## [555 samples x 56637 features]
## || SUBSETTING SAMPLES
## | Subsetting SAMPLES on ['tissue_muscle']; PASS: 174 out of 555
## | Subsetting SAMPLES on ['qc_sexswitch']; PASS: 172 out of 174
## | Subsetting SAMPLES on ['qc_multdim2']; PASS: 168 out of 172
## | Subsetting SAMPLES on ['qc_rep1']; PASS: 168 out of 168
## | Subsetting SAMPLES on ['complete_pairs']; PASS: 162 out of 168
## || TRANSFORMING DATA
## | VOOM .. OK!
## | DONE!goto_exp <- dat1[["dat"]]Check if RNAseq data exists for all genes
check <- df$gene_ens %in% colnames(goto_exp)
xtabs(~check)## check
## TRUE
## 170Save genes we will check
av_genes <- df$gene_ens
head(av_genes)## [1] "ENSG00000120029" "ENSG00000085365" "ENSG00000145685" "ENSG00000055208"
## [5] "ENSG00000055211" "ENSG00000196233"Filter covariates and genes
goto_exp <- goto_exp %>%
dplyr::select(IOP2_ID = sampID2,
timepoint = intervention,
flowcell,
all_of(av_genes)) %>%
mutate(
ID = str_c(IOP2_ID, '_', timepoint)
)Merge with samples we have DNAm data for
ID_list <- targets %>%
mutate(
ID = paste0(IOP2_ID, '_', timepoint)) %>%
dplyr::select(ID, Basename)exp_df <- inner_join(ID_list, goto_exp, by = 'ID')
dim(exp_df)## [1] 148 171Ensure DNAm data for all samples
targets <- targets %>% filter(Basename %in% rownames(betas))
print(paste0('We have complete covariate and DNAm data for ',
nrow(targets), ' samples'))## [1] "We have complete covariate and DNAm data for 160 samples"Ensure expression data for all samples
targets <- targets %>% filter(Basename %in% exp_df$Basename)
print(paste0('We have complete data for everything for ',
nrow(targets), ' samples'))## [1] "We have complete data for everything for 148 samples"Order uuid alphabetically
targets <- targets[order(targets$Basename),]
rownames(targets) <- targets$Basename
dim(targets)## [1] 148 9Order DNAm data
betas <- betas[rownames(betas) %in% targets$Basename, ]
betas <- betas[order(rownames(betas)), ]
dim(betas)## [1] 148 95Order expression data frame
exp_df <- exp_df[exp_df$Basename %in% targets$Basename, ]
exp_df <- exp_df[order(exp_df$Basename), ]
dim(exp_df)## [1] 148 171Save
save(betas, exp_df, targets, df,
file='../GOTO_Data/eQTM/all_eQTMobjects-muscle.Rdata')Bind data frames
lm_df <- cbind(targets,
betas,
exp_df)Run models
for(i in 1:nrow(df)){
cpg <- df$cpg[i]
gene <- df$gene[i]
gene_ens <- df$gene_ens[i]
fit <- lmer(substitute(cpg ~ gene_ens + age + sex + smoke +
plate + array_row + flowcell + (1|IOP2_ID),
list(cpg = as.name(cpg),
gene_ens = as.name(gene_ens))),
data=lm_df)
if(i == 1){
out <- data.frame(
cpg = cpg,
gene = gene,
gene_ens = gene_ens,
es = coef(summary(fit))[2,1],
se = coef(summary(fit))[2,2],
p = coef(summary(fit))[2,5]
)
} else {
out <- rbind(out,
data.frame(
cpg = cpg,
gene = gene,
gene_ens = gene_ens,
es = coef(summary(fit))[2,1],
se = coef(summary(fit))[2,2],
p = coef(summary(fit))[2,5]))
}
}Adjust p-values
out$padj <- p.adjust(out$p, method='fdr')Inspect top
out %>% arrange(padj) %>% head()## cpg gene gene_ens es se p
## 1 cg17411016 MCFD2 ENSG00000180398 0.15740085 0.017187651 3.823161e-15
## 2 cg09312464 FSCN1 ENSG00000075618 -0.03247148 0.004286796 5.587712e-12
## 3 cg13585930 LRRC20 ENSG00000172731 0.19499532 0.025740939 6.250324e-12
## 4 cg12595459 EXTL3 ENSG00000012232 0.08831114 0.012112964 2.871282e-11
## 5 cg02849956 DPP9 ENSG00000142002 0.13413163 0.020775060 3.549895e-09
## 6 cg21730012 INPP5A ENSG00000068383 0.08127165 0.012753967 3.260426e-09
## padj
## 1 6.499374e-13
## 2 3.541850e-10
## 3 3.541850e-10
## 4 1.220295e-09
## 5 1.005803e-07
## 6 1.005803e-07eqtm <- out %>% filter(padj <= 0.05) %>%
arrange(padj)Add DEG data
eqtm <- eqtm %>%
dplyr::select(cpg, gene,
eqtm_es = es, eqtm_se = se, eqtm_p = p,
eqtm_padj = padj)
eqtm <- inner_join(eqtm, df, by=c('cpg', 'gene'))
eqtm <- eqtm %>% arrange(eqtm_padj)
eqtm## cpg gene eqtm_es eqtm_se eqtm_p eqtm_padj
## 1 cg17411016 MCFD2 0.157400854 0.017187651 3.823161e-15 6.499374e-13
## 2 cg09312464 FSCN1 -0.032471479 0.004286796 5.587712e-12 3.541850e-10
## 3 cg13585930 LRRC20 0.194995320 0.025740939 6.250324e-12 3.541850e-10
## 4 cg12595459 EXTL3 0.088311140 0.012112964 2.871282e-11 1.220295e-09
## 5 cg02849956 DPP9 0.134131630 0.020775060 3.549895e-09 1.005803e-07
## 6 cg21730012 INPP5A 0.081271645 0.012753967 3.260426e-09 1.005803e-07
## 7 cg20748397 FLII 0.083742316 0.013458658 6.721914e-09 1.632465e-07
## 8 cg03045139 LHFPL2 -0.029409824 0.004755106 8.095718e-09 1.720340e-07
## 9 cg02233071 RUNX1 -0.019621864 0.003488165 1.303993e-07 2.463097e-06
## 10 cg20748397 ALKBH5 0.093361867 0.016876192 1.684174e-07 2.863095e-06
## 11 cg25981106 HSD11B1 0.036449501 0.006614025 2.271127e-07 3.509924e-06
## 12 cg17411016 TTC7A -0.047559599 0.008648645 2.478714e-07 3.511512e-06
## 13 cg21005024 GRB10 0.083076885 0.015727619 5.416367e-07 7.082942e-06
## 14 cg14426392 ATP1A1 -0.073537402 0.015044233 2.974239e-06 3.444944e-05
## 15 cg17357895 EXOC3L1 -0.055428591 0.011364258 3.039657e-06 3.444944e-05
## 16 cg12402318 TMOD3 -0.034574387 0.007117855 3.313091e-06 3.520159e-05
## 17 cg20748397 GID4 0.075895925 0.015864612 4.591350e-06 4.591350e-05
## 18 cg07827395 FAM220A 0.050244391 0.010592172 5.386461e-06 5.087213e-05
## 19 cg07827395 RAC1 0.087542714 0.019004388 9.766815e-06 8.738729e-05
## 20 cg20617626 ARHGEF17 -0.040559514 0.008880707 1.124324e-05 9.556757e-05
## 21 cg08416530 AGPAT2 0.082866760 0.018230639 1.324342e-05 1.072086e-04
## 22 cg17357895 FHOD1 0.125483416 0.028134007 1.734577e-05 1.340355e-04
## 23 cg17357895 PLEKHG4 -0.048587862 0.010970917 1.969523e-05 1.455734e-04
## 24 cg05501756 SRL -0.070227130 0.015928670 2.138607e-05 1.514846e-04
## 25 cg16350675 CHRND -0.015198542 0.003463090 2.465686e-05 1.676666e-04
## 26 cg09312464 RNF216 0.063427585 0.014627747 2.876347e-05 1.880688e-04
## 27 cg17370665 ZFP36L1 -0.069533355 0.016307907 3.800650e-05 2.393002e-04
## 28 cg17357895 E2F4 0.148782291 0.035351644 4.716693e-05 2.863707e-04
## 29 cg14189116 CAPRIN2 -0.030912362 0.007611246 8.319159e-05 4.876748e-04
## 30 cg11561701 CRKL 0.064388398 0.016147257 1.184102e-04 6.493465e-04
## 31 cg25225070 ZNF143 0.073937556 0.018626511 1.181461e-04 6.493465e-04
## 32 cg04451259 PAMR1 -0.037002200 0.009393477 1.341613e-04 7.127318e-04
## 33 cg11835462 CHRND -0.013728424 0.003537843 1.687402e-04 8.525401e-04
## 34 cg27187848 PCDHGC5 -0.013097760 0.003384701 1.705080e-04 8.525401e-04
## 35 cg22898055 MDH2 0.100031225 0.025944028 1.795611e-04 8.721540e-04
## 36 cg24857465 FDFT1 0.062909019 0.016322164 1.861734e-04 8.791520e-04
## 37 cg04814966 PPIF 0.038804887 0.010111479 1.923360e-04 8.837060e-04
## 38 cg12772738 RPRD1B 0.150386532 0.040554524 3.162793e-04 1.414934e-03
## 39 cg27187848 PCDHGC3 -0.027592124 0.007705247 4.818472e-04 2.100360e-03
## 40 cg12166519 DLL1 -0.025823675 0.007281040 5.490563e-04 2.333489e-03
## 41 cg15982707 ADAM19 -0.047866096 0.014023122 8.589332e-04 3.561430e-03
## 42 cg17357895 KIAA0895L -0.030884478 0.009136357 9.522862e-04 3.854492e-03
## 43 cg27187848 PCDHGA12 -0.020590457 0.006243738 1.256104e-03 4.965994e-03
## 44 cg21017748 PPID 0.064318109 0.019542969 1.293529e-03 4.997727e-03
## 45 cg04275661 FYN -0.049524518 0.015444914 1.735565e-03 6.556578e-03
## 46 cg05501756 GLIS2 0.024895674 0.007870881 1.940911e-03 7.172932e-03
## 47 cg22898055 TMEM120A 0.041337368 0.013180446 2.115480e-03 7.651738e-03
## 48 cg17628730 BRF2 0.045645933 0.014695550 2.428566e-03 8.601171e-03
## 49 cg03045139 SCAMP1 0.045450143 0.015005142 2.953224e-03 1.024588e-02
## 50 cg09232225 FAM96B 0.038613628 0.012756509 3.084980e-03 1.048893e-02
## 51 cg16845233 JMJD1C -0.027427859 0.009399620 4.153732e-03 1.384577e-02
## 52 cg05008948 ZNF394 0.063119538 0.021839091 4.528393e-03 1.480436e-02
## 53 cg12001774 NET1 0.038861200 0.013530389 4.754314e-03 1.524969e-02
## 54 cg11012616 PSMA6 0.039681665 0.014006410 5.377104e-03 1.662014e-02
## 55 cg11561701 THAP7 0.046485725 0.016372217 5.294795e-03 1.662014e-02
## 56 cg05018467 CHSY1 -0.035195720 0.012476454 5.753174e-03 1.715859e-02
## 57 cg05018467 LRRK1 -0.019099899 0.006786301 5.702319e-03 1.715859e-02
## 58 cg17633300 ATP6V0E1 0.043213556 0.015942420 7.610094e-03 2.230545e-02
## 59 cg14343713 SPCS2 0.047336298 0.017736435 8.597398e-03 2.435929e-02
## 60 cg25114611 FKBP5 -0.013592675 0.005093139 8.566963e-03 2.435929e-02
## 61 cg21342383 ADAM12 -0.003687336 0.001404930 9.698942e-03 2.702984e-02
## 62 cg23903301 CD59 0.049560742 0.019512471 1.224948e-02 3.358730e-02
## 63 cg17628730 PROSC 0.058537487 0.023258816 1.308589e-02 3.475940e-02
## 64 cg19728396 UCK2 -0.031706375 0.012578863 1.291102e-02 3.475940e-02
## 65 cg18012268 FPR1 0.004075916 0.001625050 1.337996e-02 3.499374e-02
## 66 cg20948500 WSCD1 -0.020266304 0.008127244 1.388949e-02 3.577596e-02
## 67 cg12402318 LEO1 -0.023672427 0.009542741 1.437149e-02 3.646498e-02
## 68 cg20748397 DRG2 0.051306382 0.020792213 1.488932e-02 3.668382e-02
## 69 cg25225070 SWAP70 -0.034231570 0.013839906 1.468817e-02 3.668382e-02
## 70 cg11561701 LZTR1 -0.042931914 0.017789563 1.728904e-02 4.198768e-02
## 71 cg01242348 CDH24 0.006117016 0.002559330 1.827413e-02 4.375495e-02
## 72 cg12402318 TMOD2 -0.022064948 0.009277859 1.885242e-02 4.451266e-02
## gene_ens logFC deg_p deg_padj
## 1 ENSG00000180398 -0.14640962 3.179061e-07 7.585708e-06
## 2 ENSG00000075618 0.39904836 8.127653e-07 1.406084e-05
## 3 ENSG00000172731 -0.21152358 4.380047e-08 2.164995e-06
## 4 ENSG00000012232 -0.13394497 1.599257e-04 8.645985e-04
## 5 ENSG00000142002 -0.17176130 2.679621e-07 7.131913e-06
## 6 ENSG00000068383 -0.20530741 1.033761e-07 3.576811e-06
## 7 ENSG00000177731 -0.22135965 6.616717e-08 2.861730e-06
## 8 ENSG00000145685 0.36905967 1.564875e-07 4.540984e-06
## 9 ENSG00000159216 0.75757718 1.574908e-07 4.540984e-06
## 10 ENSG00000091542 -0.12465998 1.845921e-04 9.392480e-04
## 11 ENSG00000117594 -0.46462302 2.070661e-09 3.788328e-07
## 12 ENSG00000068724 0.20079066 6.261185e-04 2.610084e-03
## 13 ENSG00000106070 -0.25439754 2.401900e-05 2.130917e-04
## 14 ENSG00000163399 0.23513217 4.636613e-06 5.868644e-05
## 15 ENSG00000179044 0.25161186 3.430684e-05 2.580471e-04
## 16 ENSG00000138594 0.24084560 3.336614e-07 7.585708e-06
## 17 ENSG00000141034 -0.10998734 1.994585e-03 6.510626e-03
## 18 ENSG00000178397 -0.25835500 4.734234e-06 5.868644e-05
## 19 ENSG00000136238 -0.13163579 1.961784e-05 1.834533e-04
## 20 ENSG00000110237 0.14261724 1.004377e-04 5.991628e-04
## 21 ENSG00000169692 -0.27091304 3.284677e-09 3.788328e-07
## 22 ENSG00000135723 -0.14714827 4.868055e-06 5.868644e-05
## 23 ENSG00000196155 0.23391099 1.959657e-04 9.826684e-04
## 24 ENSG00000185739 -0.15907503 6.464822e-06 7.456095e-05
## 25 ENSG00000135902 0.56304027 5.792258e-05 3.711335e-04
## 26 ENSG00000011275 -0.11510384 1.120697e-05 1.211753e-04
## 27 ENSG00000185650 0.17685073 3.790112e-05 2.750769e-04
## 28 ENSG00000205250 -0.08330454 1.590077e-03 5.393791e-03
## 29 ENSG00000110888 0.31069078 1.163706e-05 1.220128e-04
## 30 ENSG00000099942 -0.15994816 4.127605e-07 8.400890e-06
## 31 ENSG00000166478 -0.13769864 5.086119e-05 3.384225e-04
## 32 ENSG00000149090 0.24028197 1.624942e-02 3.748200e-02
## 33 ENSG00000135902 0.56304027 5.792258e-05 3.711335e-04
## 34 ENSG00000240764 0.17092061 1.004845e-03 3.658594e-03
## 35 ENSG00000146701 -0.17739010 2.212181e-05 2.014249e-04
## 36 ENSG00000079459 -0.19705638 8.047537e-07 1.406084e-05
## 37 ENSG00000108179 -0.20059622 3.816095e-05 2.750769e-04
## 38 ENSG00000101413 -0.08847153 2.490939e-05 2.154662e-04
## 39 ENSG00000240184 0.26960947 4.269216e-05 2.954298e-04
## 40 ENSG00000198719 0.24586824 4.219877e-05 2.954298e-04
## 41 ENSG00000135074 0.14330686 4.529491e-03 1.263874e-02
## 42 ENSG00000196123 0.35528187 3.132751e-06 4.428785e-05
## 43 ENSG00000253159 0.19566731 5.548541e-04 2.393818e-03
## 44 ENSG00000171497 -0.08558962 3.381517e-03 1.008625e-02
## 45 ENSG00000010810 0.25645632 9.578755e-08 3.576811e-06
## 46 ENSG00000126603 0.14621073 2.303904e-02 4.831216e-02
## 47 ENSG00000189077 -0.31730520 2.931310e-05 2.306085e-04
## 48 ENSG00000104221 -0.20974958 2.992713e-08 2.070958e-06
## 49 ENSG00000085365 -0.06742307 1.008167e-02 2.422401e-02
## 50 ENSG00000166595 -0.18103037 1.015776e-06 1.673613e-05
## 51 ENSG00000171988 0.12010751 1.438238e-03 5.039174e-03
## 52 ENSG00000160908 -0.11614587 2.158491e-04 1.066911e-03
## 53 ENSG00000173848 -0.09120973 2.932302e-03 8.899795e-03
## 54 ENSG00000100902 -0.16641829 5.604026e-04 2.393818e-03
## 55 ENSG00000184436 -0.14986757 3.199989e-06 4.428785e-05
## 56 ENSG00000131873 0.13730863 1.451969e-03 5.039174e-03
## 57 ENSG00000154237 0.20837733 2.429897e-03 7.713251e-03
## 58 ENSG00000113732 -0.06018372 7.424689e-03 1.875141e-02
## 59 ENSG00000118363 -0.07818893 2.807821e-03 8.674160e-03
## 60 ENSG00000096060 -0.31176637 8.304411e-04 3.202579e-03
## 61 ENSG00000148848 0.61476058 1.294415e-05 1.248003e-04
## 62 ENSG00000085063 -0.12600490 6.284564e-05 3.953562e-04
## 63 ENSG00000147471 -0.12930591 1.252590e-05 1.248003e-04
## 64 ENSG00000143179 0.18815173 8.330408e-04 3.202579e-03
## 65 ENSG00000171051 -0.39887436 1.526962e-02 3.545830e-02
## 66 ENSG00000179314 -0.17133540 1.118854e-02 2.651531e-02
## 67 ENSG00000166477 0.16631511 3.189635e-05 2.452475e-04
## 68 ENSG00000108591 -0.10358180 2.310726e-04 1.126072e-03
## 69 ENSG00000133789 0.13678629 1.468369e-03 5.039174e-03
## 70 ENSG00000099949 0.08219461 7.800257e-03 1.955717e-02
## 71 ENSG00000139880 0.28527872 1.232179e-04 7.093609e-04
## 72 ENSG00000128872 0.17186111 2.855928e-05 2.306085e-04Save
save(out, eqtm, file='../GOTO_Data/eQTM/GOTO-muscle_eQTM.Rdata')
write_csv(eqtm, file='../GOTO_Data/Tables/ST07.csv')sessionInfo()## R version 4.2.2 (2022-10-31)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Rocky Linux 8.10 (Green Obsidian)
##
## Matrix products: default
## BLAS/LAPACK: /usr/lib64/libopenblas-r0.3.15.so
##
## 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] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] AnnotationHub_3.2.2
## [2] BiocFileCache_2.2.1
## [3] dbplyr_2.2.1
## [4] cinaR_0.2.3
## [5] edgeR_3.40.2
## [6] ggpubr_0.4.0
## [7] GEOquery_2.62.2
## [8] MuSiC_0.2.0
## [9] nnls_1.4
## [10] gplots_3.1.3
## [11] plotly_4.10.1
## [12] SeuratObject_4.1.3
## [13] Seurat_4.3.0
## [14] gridExtra_2.3
## [15] lattice_0.21-8
## [16] bacon_1.22.0
## [17] ellipse_0.4.5
## [18] methylGSA_1.12.0
## [19] sva_3.42.0
## [20] genefilter_1.76.0
## [21] mgcv_1.8-42
## [22] nlme_3.1-162
## [23] limma_3.54.2
## [24] lmerTest_3.1-3
## [25] lme4_1.1-30
## [26] IlluminaHumanMethylationEPICanno.ilm10b4.hg19_0.6.0
## [27] snpStats_1.44.0
## [28] survival_3.5-5
## [29] ggrepel_0.9.1
## [30] ggfortify_0.4.14
## [31] irlba_2.3.5.1
## [32] Matrix_1.5-4.1
## [33] omicsPrint_1.14.0
## [34] MASS_7.3-60
## [35] DNAmArray_2.0.0
## [36] pls_2.8-2
## [37] FDb.InfiniumMethylation.hg19_2.2.0
## [38] org.Hs.eg.db_3.14.0
## [39] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
## [40] GenomicFeatures_1.46.5
## [41] AnnotationDbi_1.56.2
## [42] IlluminaHumanMethylationEPICmanifest_0.3.0
## [43] minfi_1.40.0
## [44] bumphunter_1.36.0
## [45] locfit_1.5-9.8
## [46] iterators_1.0.14
## [47] foreach_1.5.2
## [48] Biostrings_2.62.0
## [49] XVector_0.34.0
## [50] SummarizedExperiment_1.24.0
## [51] Biobase_2.58.0
## [52] MatrixGenerics_1.10.0
## [53] matrixStats_1.0.0
## [54] GenomicRanges_1.46.1
## [55] GenomeInfoDb_1.34.9
## [56] IRanges_2.32.0
## [57] S4Vectors_0.36.2
## [58] BiocGenerics_0.44.0
## [59] BiocParallel_1.32.6
## [60] MethylAid_1.28.0
## [61] forcats_0.5.2
## [62] stringr_1.5.0
## [63] dplyr_1.1.3
## [64] purrr_0.3.4
## [65] readr_2.1.2
## [66] tidyr_1.2.1
## [67] tibble_3.2.1
## [68] ggplot2_3.4.3
## [69] tidyverse_1.3.2
## [70] rmarkdown_2.16
##
## loaded via a namespace (and not attached):
## [1] ica_1.0-3
## [2] Rsamtools_2.10.0
## [3] lmtest_0.9-40
## [4] crayon_1.5.2
## [5] rhdf5filters_1.10.1
## [6] backports_1.4.1
## [7] reprex_2.0.2
## [8] GOSemSim_2.20.0
## [9] rlang_1.1.1
## [10] ROCR_1.0-11
## [11] readxl_1.4.1
## [12] SparseM_1.81
## [13] nloptr_2.0.3
## [14] filelock_1.0.2
## [15] rjson_0.2.21
## [16] bit64_4.0.5
## [17] glue_1.6.2
## [18] sctransform_0.3.5
## [19] rngtools_1.5.2
## [20] spatstat.sparse_3.0-1
## [21] mcmc_0.9-7
## [22] spatstat.geom_3.2-1
## [23] DOSE_3.20.1
## [24] haven_2.5.1
## [25] tidyselect_1.2.0
## [26] fitdistrplus_1.1-11
## [27] XML_3.99-0.14
## [28] zoo_1.8-12
## [29] GenomicAlignments_1.30.0
## [30] MatrixModels_0.5-1
## [31] xtable_1.8-4
## [32] magrittr_2.0.3
## [33] evaluate_0.21
## [34] cli_3.6.1
## [35] zlibbioc_1.44.0
## [36] miniUI_0.1.1.1
## [37] rstudioapi_0.14
## [38] doRNG_1.8.6
## [39] sp_1.6-1
## [40] MultiAssayExperiment_1.20.0
## [41] bslib_0.5.0
## [42] fastmatch_1.1-3
## [43] treeio_1.18.1
## [44] shiny_1.7.2
## [45] xfun_0.39
## [46] askpass_1.1
## [47] multtest_2.50.0
## [48] cluster_2.1.4
## [49] caTools_1.18.2
## [50] tidygraph_1.2.2
## [51] KEGGREST_1.34.0
## [52] interactiveDisplayBase_1.32.0
## [53] quantreg_5.94
## [54] base64_2.0.1
## [55] ape_5.7-1
## [56] scrime_1.3.5
## [57] listenv_0.9.0
## [58] png_0.1-8
## [59] reshape_0.8.9
## [60] future_1.32.0
## [61] withr_2.5.0
## [62] bitops_1.0-7
## [63] ggforce_0.3.4
## [64] plyr_1.8.8
## [65] cellranger_1.1.0
## [66] coda_0.19-4
## [67] pillar_1.9.0
## [68] cachem_1.0.8
## [69] fs_1.6.2
## [70] clusterProfiler_4.2.2
## [71] DelayedMatrixStats_1.16.0
## [72] vctrs_0.6.3
## [73] ellipsis_0.3.2
## [74] generics_0.1.3
## [75] tools_4.2.2
## [76] munsell_0.5.0
## [77] tweenr_2.0.2
## [78] fgsea_1.20.0
## [79] DelayedArray_0.24.0
## [80] abind_1.4-5
## [81] fastmap_1.1.1
## [82] compiler_4.2.2
## [83] httpuv_1.6.11
## [84] rtracklayer_1.54.0
## [85] beanplot_1.3.1
## [86] MCMCpack_1.6-3
## [87] GenomeInfoDbData_1.2.9
## [88] deldir_1.0-9
## [89] utf8_1.2.3
## [90] later_1.3.1
## [91] RobustRankAggreg_1.2.1
## [92] jsonlite_1.8.5
## [93] scales_1.2.1
## [94] carData_3.0-5
## [95] pbapply_1.7-0
## [96] tidytree_0.4.0
## [97] sparseMatrixStats_1.10.0
## [98] lazyeval_0.2.2
## [99] promises_1.2.0.1
## [100] car_3.1-0
## [101] goftest_1.2-3
## [102] spatstat.utils_3.0-3
## [103] reticulate_1.30
## [104] htm2txt_2.2.2
## [105] nor1mix_1.3-0
## [106] cowplot_1.1.1
## [107] statmod_1.5.0
## [108] siggenes_1.68.0
## [109] Rtsne_0.16
## [110] downloader_0.4
## [111] uwot_0.1.14
## [112] igraph_1.4.3
## [113] HDF5Array_1.22.1
## [114] numDeriv_2016.8-1.1
## [115] yaml_2.3.7
## [116] htmltools_0.5.5
## [117] memoise_2.0.1
## [118] BiocIO_1.8.0
## [119] graphlayouts_0.8.1
## [120] quadprog_1.5-8
## [121] viridisLite_0.4.2
## [122] digest_0.6.31
## [123] assertthat_0.2.1
## [124] mime_0.12
## [125] rappdirs_0.3.3
## [126] RSQLite_2.2.17
## [127] yulab.utils_0.0.6
## [128] future.apply_1.11.0
## [129] data.table_1.14.8
## [130] blob_1.2.4
## [131] preprocessCore_1.60.2
## [132] splines_4.2.2
## [133] labeling_0.4.2
## [134] Rhdf5lib_1.20.0
## [135] illuminaio_0.40.0
## [136] googledrive_2.0.0
## [137] RaggedExperiment_1.18.0
## [138] RCurl_1.98-1.12
## [139] broom_1.0.1
## [140] hms_1.1.2
## [141] modelr_0.1.9
## [142] rhdf5_2.42.1
## [143] colorspace_2.1-0
## [144] BiocManager_1.30.21
## [145] aplot_0.1.7
## [146] sass_0.4.6
## [147] Rcpp_1.0.10
## [148] mclust_6.0.0
## [149] RANN_2.6.1
## [150] enrichplot_1.14.2
## [151] fansi_1.0.4
## [152] tzdb_0.4.0
## [153] parallelly_1.36.0
## [154] R6_2.5.1
## [155] grid_4.2.2
## [156] ggridges_0.5.4
## [157] lifecycle_1.0.3
## [158] ggsignif_0.6.3
## [159] curl_5.0.1
## [160] googlesheets4_1.0.1
## [161] minqa_1.2.5
## [162] leiden_0.4.3
## [163] jquerylib_0.1.4
## [164] DO.db_2.9
## [165] qvalue_2.26.0
## [166] RcppAnnoy_0.0.20
## [167] RColorBrewer_1.1-3
## [168] spatstat.explore_3.1-0
## [169] htmlwidgets_1.5.4
## [170] polyclip_1.10-4
## [171] biomaRt_2.50.3
## [172] missMethyl_1.28.0
## [173] shadowtext_0.1.2
## [174] timechange_0.2.0
## [175] gridGraphics_0.5-1
## [176] reactome.db_1.77.0
## [177] rvest_1.0.3
## [178] globals_0.16.2
## [179] openssl_2.0.6
## [180] spatstat.random_3.1-5
## [181] patchwork_1.1.2
## [182] progressr_0.13.0
## [183] codetools_0.2-19
## [184] IlluminaHumanMethylation450kanno.ilmn12.hg19_0.6.0
## [185] lubridate_1.9.2
## [186] GO.db_3.14.0
## [187] gtools_3.9.4
## [188] prettyunits_1.1.1
## [189] gridBase_0.4-7
## [190] gtable_0.3.3
## [191] DBI_1.1.3
## [192] tensor_1.5
## [193] ggfun_0.0.7
## [194] httr_1.4.6
## [195] highr_0.10
## [196] KernSmooth_2.23-21
## [197] stringi_1.7.12
## [198] vroom_1.5.7
## [199] progress_1.2.2
## [200] reshape2_1.4.4
## [201] farver_2.1.1
## [202] annotate_1.72.0
## [203] viridis_0.6.2
## [204] hexbin_1.28.3
## [205] ggtree_3.2.1
## [206] xml2_1.3.4
## [207] boot_1.3-28.1
## [208] restfulr_0.0.15
## [209] scattermore_0.8
## [210] ggplotify_0.1.0
## [211] BiocVersion_3.16.0
## [212] bit_4.0.5
## [213] spatstat.data_3.0-1
## [214] scatterpie_0.1.8
## [215] ggraph_2.0.6
## [216] pkgconfig_2.0.3
## [217] gargle_1.5.0
## [218] rstatix_0.7.0
## [219] knitr_1.43Clear
rm(list=ls())