This script loads in the differentially expressed genes in cis of adipose 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/Fat-100kb.Rdata')
head(df)## cpg gene_ens gene
## 1 cg02282833 ENSG00000113389 NPR3
## 2 cg02282833 ENSG00000181495 AC026703.1
## 3 cg03689324 ENSG00000129009 ISLR
## 4 cg03689324 ENSG00000137868 STRA6
## 5 cg03689324 ENSG00000140464 PML
## 6 cg03689324 ENSG00000140481 CCDC33Load results of DEG analysis
load('../GOTO_Data/DEGs/Fat-DEG.Rdata')Merge
deg$gene_ens <- rownames(deg)
deg <- deg %>%
dplyr::select(gene_ens,
logFC, deg_p = pvalue,
deg_padj = padj)
df <- right_join(df, deg, by='gene_ens')
head(df)## cpg gene_ens gene logFC deg_p deg_padj
## 1 cg02282833 ENSG00000113389 NPR3 -0.25520983 3.532219e-03 2.201476e-02
## 2 cg03689324 ENSG00000140464 PML -0.07787649 3.678145e-03 2.240324e-02
## 3 cg25662390 ENSG00000109103 UNC119 -0.10813294 5.989413e-05 1.415687e-03
## 4 cg25662390 ENSG00000109107 ALDOC -0.32809022 1.188488e-06 6.370293e-05
## 5 cg19575372 ENSG00000163064 EN1 -0.18533940 4.198761e-04 4.635194e-03
## 6 cg02528008 ENSG00000171444 MCC 0.14204840 4.298163e-06 1.919846e-04Load top CpG results and methData
load('../GOTO_Data/GOTO_methData-filtered.Rdata')Save only adipose samples
methData <- methData[ , methData$tissue == 'fat']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] 178 72load("../GOTO_Data/GOTO_targets-filtered.Rdata")Filter adipose
targets <- targets %>%
filter(tissue == 'fat')Save covariates
targets <- targets %>%
dplyr::select(IOP2_ID, timepoint, age, sex, smoke,
plate, array_row, Basename) %>%
mutate(ID = paste0(IOP2_ID, '_', timepoint))Create ID list
ID_list <- targets %>% mutate(
ID = paste0(IOP2_ID, '_', timepoint)
) %>% dplyr::select(ID)Load RNAseq data
load('../GOTO_Data/DEGs/expData_adipose.RData')
goto_exp <- expData %>%
dplyr::select(IOP2_ID, timepoint, final_plate,
starts_with('ENS')) %>%
mutate(
ID = str_c(IOP2_ID, '_', timepoint))Merge
exp_df <- left_join(ID_list, goto_exp, by = 'ID')
dim(exp_df)## [1] 178 41655Fetch gene symbols
ens2gene <- cinaR::grch37Check data for all genes
check <- df$gene_ens %in% colnames(goto_exp)
xtabs(~check)## check
## TRUE
## 99Save genes we will check
av_genes <- df$gene_ens
head(av_genes)## [1] "ENSG00000113389" "ENSG00000140464" "ENSG00000109103" "ENSG00000109107"
## [5] "ENSG00000163064" "ENSG00000171444"Filter covariates and genes
goto_exp <- goto_exp %>%
dplyr::select(IOP2_ID,
timepoint,
final_plate,
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] 150 62Ensure 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 178 samples"Ensure expression data for all samples
targets <- targets %>% filter(Basename %in% exp_df$Basename)
print(paste0('We have complete data for everything for ',
nrow(targets), ' samples'))## [1] "We have complete data for everything for 150 samples"Order uuid alphabetically
targets <- targets[order(targets$Basename),]
rownames(targets) <- targets$Basename
dim(targets)## [1] 150 9Order DNAm data
betas <- betas[rownames(betas) %in% targets$Basename, ]
betas <- betas[order(rownames(betas)), ]
dim(betas)## [1] 150 72Order expression data frame
exp_df <- exp_df[exp_df$Basename %in% targets$Basename, ]
exp_df <- exp_df[order(exp_df$Basename), ]
dim(exp_df)## [1] 150 62Save
save(betas, exp_df, targets, df,
file='../GOTO_Data/eQTM/all_eQTMobjects-fat.Rdata')Bind data frames
lm_df <- cbind(targets,
betas,
exp_df)Run models
for(i in 1:nrow(df)){
cpg <- df$cpg[i]
gene <- df$gene[i]
gene_ens <- df$gene_ens[i]
fit <- lmer(substitute(cpg ~ gene_ens + age + sex + smoke +
plate + array_row + final_plate +
(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 cg02649849 DMRT3 ENSG00000064218 0.01993341 0.001846892 8.215416e-20
## 2 cg03148184 PITX2 ENSG00000164093 0.05923460 0.005773905 2.438325e-18
## 3 cg26708319 PITX2 ENSG00000164093 0.05641919 0.005733204 1.570197e-17
## 4 cg19370653 PITX2 ENSG00000164093 0.06210197 0.006477727 6.770121e-17
## 5 cg07790170 PITX2 ENSG00000164093 0.06962431 0.007406964 4.288950e-16
## 6 cg03943773 PITX2 ENSG00000164093 0.05071661 0.005577768 1.144074e-15
## padj
## 1 8.133262e-18
## 2 1.206971e-16
## 3 5.181649e-16
## 4 1.675605e-15
## 5 8.492120e-15
## 6 1.887722e-14eqtm <- 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 cg02649849 DMRT3 1.993341e-02 1.846892e-03 8.215416e-20 8.133262e-18
## 2 cg03148184 PITX2 5.923460e-02 5.773905e-03 2.438325e-18 1.206971e-16
## 3 cg26708319 PITX2 5.641919e-02 5.733204e-03 1.570197e-17 5.181649e-16
## 4 cg19370653 PITX2 6.210197e-02 6.477727e-03 6.770121e-17 1.675605e-15
## 5 cg07790170 PITX2 6.962431e-02 7.406964e-03 4.288950e-16 8.492120e-15
## 6 cg03943773 PITX2 5.071661e-02 5.577768e-03 1.144074e-15 1.887722e-14
## 7 cg23646776 PITX2 3.645295e-02 4.044028e-03 2.595608e-15 3.670931e-14
## 8 cg24005685 PITX2 5.815366e-02 6.540647e-03 3.590040e-15 4.442674e-14
## 9 cg10895452 EN1 -1.455366e-02 1.683984e-03 1.492518e-14 1.641770e-13
## 10 cg24925400 PITX2 5.023503e-02 5.948266e-03 6.912554e-14 6.843429e-13
## 11 cg01951086 PITX2 6.542884e-02 7.944734e-03 2.485847e-13 2.237262e-12
## 12 cg23806894 PITX2 4.849409e-02 6.307044e-03 2.953658e-12 2.436768e-11
## 13 cg17242937 PITX2 5.104065e-02 6.738782e-03 5.674023e-12 4.320986e-11
## 14 cg21299542 PITX2 3.088919e-02 4.085099e-03 6.658791e-12 4.708716e-11
## 15 cg01733176 PITX2 4.280687e-02 6.015772e-03 6.074817e-11 4.009379e-10
## 16 cg26023087 DMRT3 1.565377e-02 2.226390e-03 9.535956e-11 5.900373e-10
## 17 cg19849728 PITX2 3.800746e-02 5.914208e-03 2.110573e-09 1.229098e-08
## 18 cg14434922 DMRT3 1.340297e-02 2.226531e-03 1.580542e-08 8.692981e-08
## 19 cg05581451 PITX2 3.407584e-02 5.843043e-03 3.950206e-08 2.058265e-07
## 20 cg18792984 TREM1 1.703057e-02 2.939256e-03 4.715314e-08 2.334081e-07
## 21 cg21029045 PITX2 3.718030e-02 6.895944e-03 3.571081e-07 1.683510e-06
## 22 cg19575372 EN1 1.350841e-02 2.527994e-03 4.001800e-07 1.800810e-06
## 23 cg04290158 HOXB8 8.352539e-03 1.639615e-03 1.316360e-06 5.666071e-06
## 24 cg12269436 IFNAR1 1.488650e-03 3.275179e-04 1.321181e-05 5.449872e-05
## 25 cg11284842 NR2F1 6.383606e-03 1.536807e-03 6.076296e-05 2.406213e-04
## 26 cg00354743 PLEKHA1 1.399044e-03 3.522586e-04 1.178478e-04 4.487282e-04
## 27 cg10583043 NR2F1 3.521072e-03 9.036168e-04 1.589722e-04 5.828979e-04
## 28 cg18290233 NR2F1 4.657664e-03 1.199683e-03 1.671354e-04 5.909429e-04
## 29 cg04917446 HOXB3 1.309957e-03 3.420408e-04 1.986658e-04 6.782038e-04
## 30 cg02329038 HOXB8 8.681407e-03 2.433597e-03 5.009987e-04 1.653296e-03
## 31 cg02329038 HOXB3 1.450310e-03 4.429384e-04 1.348738e-03 4.307261e-03
## 32 cg04290158 HOXB3 1.008914e-03 3.177272e-04 1.868043e-03 5.779258e-03
## 33 cg17573415 KAZN 1.225731e-03 4.065612e-04 3.076135e-03 9.228405e-03
## 34 cg16413687 ALX1 2.566748e-02 9.002151e-03 5.074862e-03 1.477680e-02
## 35 cg19429051 NR2F1 3.706883e-03 1.307050e-03 5.362512e-03 1.516825e-02
## 36 cg04917446 HOXB8 5.179747e-03 1.934210e-03 8.370248e-03 2.180670e-02
## 37 cg19375044 APOC1 -1.540643e-04 5.728035e-05 8.072561e-03 2.180670e-02
## 38 cg15117739 HOXB8 3.375876e-03 1.258818e-03 8.250705e-03 2.180670e-02
## 39 cg02329038 HOXB4 -4.652321e-03 1.744879e-03 8.629034e-03 2.190447e-02
## 40 cg12194701 STK3 -1.855826e-03 7.080399e-04 9.827415e-03 2.432285e-02
## 41 cg19375044 APOE -4.740934e-06 1.830334e-06 1.066527e-02 2.575274e-02
## 42 cg04290158 HOXB4 -3.209333e-03 1.263860e-03 1.226092e-02 2.890074e-02
## 43 cg12374581 ARMC6 1.894700e-03 7.534472e-04 1.318020e-02 3.034510e-02
## 44 cg15117739 HOXB3 5.715023e-04 2.286350e-04 1.363973e-02 3.068938e-02
## 45 cg13603764 TGS1 -2.533563e-03 1.020099e-03 1.432342e-02 3.151153e-02
## 46 cg14674171 SAP30L 4.033845e-03 1.634278e-03 1.489749e-02 3.206199e-02
## 47 cg04917446 HOXB2 2.139051e-03 8.846284e-04 1.699767e-02 3.580361e-02
## 48 cg23660235 MAP2K6 -1.003285e-02 4.198830e-03 1.826678e-02 3.767523e-02
## 49 cg13181327 TRDJ1 5.038842e-02 2.163913e-02 2.158030e-02 4.360102e-02
## 50 cg11631547 ZW10 -4.285119e-03 1.855625e-03 2.252713e-02 4.460372e-02
## 51 cg00054771 GPR155 2.257172e-03 9.907244e-04 2.462484e-02 4.780116e-02
## gene_ens logFC deg_p deg_padj
## 1 ENSG00000064218 -0.33746492 7.907869e-05 1.630238e-03
## 2 ENSG00000164093 -0.21343330 1.677649e-03 1.232473e-02
## 3 ENSG00000164093 -0.21343330 1.677649e-03 1.232473e-02
## 4 ENSG00000164093 -0.21343330 1.677649e-03 1.232473e-02
## 5 ENSG00000164093 -0.21343330 1.677649e-03 1.232473e-02
## 6 ENSG00000164093 -0.21343330 1.677649e-03 1.232473e-02
## 7 ENSG00000164093 -0.21343330 1.677649e-03 1.232473e-02
## 8 ENSG00000164093 -0.21343330 1.677649e-03 1.232473e-02
## 9 ENSG00000163064 -0.18533940 4.198761e-04 4.635194e-03
## 10 ENSG00000164093 -0.21343330 1.677649e-03 1.232473e-02
## 11 ENSG00000164093 -0.21343330 1.677649e-03 1.232473e-02
## 12 ENSG00000164093 -0.21343330 1.677649e-03 1.232473e-02
## 13 ENSG00000164093 -0.21343330 1.677649e-03 1.232473e-02
## 14 ENSG00000164093 -0.21343330 1.677649e-03 1.232473e-02
## 15 ENSG00000164093 -0.21343330 1.677649e-03 1.232473e-02
## 16 ENSG00000064218 -0.33746492 7.907869e-05 1.630238e-03
## 17 ENSG00000164093 -0.21343330 1.677649e-03 1.232473e-02
## 18 ENSG00000064218 -0.33746492 7.907869e-05 1.630238e-03
## 19 ENSG00000164093 -0.21343330 1.677649e-03 1.232473e-02
## 20 ENSG00000124731 -0.31439658 1.806825e-03 1.274287e-02
## 21 ENSG00000164093 -0.21343330 1.677649e-03 1.232473e-02
## 22 ENSG00000163064 -0.18533940 4.198761e-04 4.635194e-03
## 23 ENSG00000120068 -0.18480176 9.267916e-05 1.774144e-03
## 24 ENSG00000142166 -0.06464044 3.305086e-03 2.201476e-02
## 25 ENSG00000175745 0.23809135 6.338898e-05 1.415687e-03
## 26 ENSG00000107679 -0.10311515 1.021780e-05 3.911959e-04
## 27 ENSG00000175745 0.23809135 6.338898e-05 1.415687e-03
## 28 ENSG00000175745 0.23809135 6.338898e-05 1.415687e-03
## 29 ENSG00000120093 -0.08176916 3.416656e-03 2.201476e-02
## 30 ENSG00000120068 -0.18480176 9.267916e-05 1.774144e-03
## 31 ENSG00000120093 -0.08176916 3.416656e-03 2.201476e-02
## 32 ENSG00000120093 -0.08176916 3.416656e-03 2.201476e-02
## 33 ENSG00000189337 -0.18651196 7.980329e-08 7.129094e-06
## 34 ENSG00000180318 -0.18709802 1.627306e-03 1.232473e-02
## 35 ENSG00000175745 0.23809135 6.338898e-05 1.415687e-03
## 36 ENSG00000120068 -0.18480176 9.267916e-05 1.774144e-03
## 37 ENSG00000130208 0.51938046 2.371871e-07 1.589154e-05
## 38 ENSG00000120068 -0.18480176 9.267916e-05 1.774144e-03
## 39 ENSG00000182742 0.09745399 3.917924e-03 2.281747e-02
## 40 ENSG00000104375 0.08687633 1.932048e-04 2.829100e-03
## 41 ENSG00000130203 0.46933715 1.952693e-09 5.233216e-07
## 42 ENSG00000182742 0.09745399 3.917924e-03 2.281747e-02
## 43 ENSG00000105676 0.07580619 4.764042e-03 2.659923e-02
## 44 ENSG00000120093 -0.08176916 3.416656e-03 2.201476e-02
## 45 ENSG00000137574 0.06189770 3.505665e-03 2.201476e-02
## 46 ENSG00000164576 -0.09294551 2.680243e-03 1.841808e-02
## 47 ENSG00000173917 -0.08726220 9.565744e-03 4.661126e-02
## 48 ENSG00000108984 0.21091827 2.606564e-05 7.761767e-04
## 49 ENSG00000211825 0.14730800 4.001570e-03 2.281747e-02
## 50 ENSG00000086827 0.07914324 3.989099e-03 2.281747e-02
## 51 ENSG00000163328 -0.10413344 1.190769e-04 2.127507e-03Save
save(out, eqtm, file='../GOTO_Data/eQTM/GOTO-fat_eQTM.Rdata')
write_csv(eqtm, file='../GOTO_Data/Tables/ST14.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] grid parallel stats4 stats graphics grDevices utils
## [8] datasets methods base
##
## other attached packages:
## [1] circlize_0.4.15
## [2] ComplexHeatmap_2.14.0
## [3] RColorBrewer_1.1-3
## [4] pheatmap_1.0.12
## [5] clusterProfiler_4.2.2
## [6] AnnotationHub_3.2.2
## [7] BiocFileCache_2.2.1
## [8] dbplyr_2.2.1
## [9] cinaR_0.2.3
## [10] edgeR_3.40.2
## [11] ggpubr_0.4.0
## [12] GEOquery_2.62.2
## [13] MuSiC_0.2.0
## [14] nnls_1.4
## [15] gplots_3.1.3
## [16] plotly_4.10.1
## [17] SeuratObject_4.1.3
## [18] Seurat_4.3.0
## [19] gridExtra_2.3
## [20] lattice_0.21-8
## [21] bacon_1.22.0
## [22] ellipse_0.4.5
## [23] methylGSA_1.12.0
## [24] sva_3.42.0
## [25] genefilter_1.76.0
## [26] mgcv_1.8-42
## [27] nlme_3.1-162
## [28] limma_3.54.2
## [29] lmerTest_3.1-3
## [30] lme4_1.1-30
## [31] IlluminaHumanMethylationEPICanno.ilm10b4.hg19_0.6.0
## [32] snpStats_1.44.0
## [33] survival_3.5-5
## [34] ggrepel_0.9.1
## [35] ggfortify_0.4.14
## [36] irlba_2.3.5.1
## [37] Matrix_1.5-4.1
## [38] omicsPrint_1.14.0
## [39] MASS_7.3-60
## [40] DNAmArray_2.0.0
## [41] pls_2.8-2
## [42] FDb.InfiniumMethylation.hg19_2.2.0
## [43] org.Hs.eg.db_3.14.0
## [44] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
## [45] GenomicFeatures_1.46.5
## [46] AnnotationDbi_1.56.2
## [47] IlluminaHumanMethylationEPICmanifest_0.3.0
## [48] minfi_1.40.0
## [49] bumphunter_1.36.0
## [50] locfit_1.5-9.8
## [51] iterators_1.0.14
## [52] foreach_1.5.2
## [53] Biostrings_2.62.0
## [54] XVector_0.34.0
## [55] SummarizedExperiment_1.24.0
## [56] Biobase_2.58.0
## [57] MatrixGenerics_1.10.0
## [58] matrixStats_1.0.0
## [59] GenomicRanges_1.46.1
## [60] GenomeInfoDb_1.34.9
## [61] IRanges_2.32.0
## [62] S4Vectors_0.36.2
## [63] BiocGenerics_0.44.0
## [64] BiocParallel_1.32.6
## [65] MethylAid_1.28.0
## [66] forcats_0.5.2
## [67] stringr_1.5.0
## [68] dplyr_1.1.3
## [69] purrr_0.3.4
## [70] readr_2.1.2
## [71] tidyr_1.2.1
## [72] tibble_3.2.1
## [73] ggplot2_3.4.3
## [74] tidyverse_1.3.2
## [75] rmarkdown_2.16
##
## loaded via a namespace (and not attached):
## [1] graphlayouts_0.8.1
## [2] pbapply_1.7-0
## [3] haven_2.5.1
## [4] vctrs_0.6.3
## [5] beanplot_1.3.1
## [6] blob_1.2.4
## [7] spatstat.data_3.0-1
## [8] later_1.3.1
## [9] nloptr_2.0.3
## [10] DBI_1.1.3
## [11] rappdirs_0.3.3
## [12] uwot_0.1.14
## [13] zlibbioc_1.44.0
## [14] MatrixModels_0.5-1
## [15] GlobalOptions_0.1.2
## [16] htmlwidgets_1.5.4
## [17] future_1.32.0
## [18] leiden_0.4.3
## [19] illuminaio_0.40.0
## [20] tidygraph_1.2.2
## [21] Rcpp_1.0.10
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## [23] promises_1.2.0.1
## [24] DelayedArray_0.24.0
## [25] magick_2.7.4
## [26] fs_1.6.2
## [27] fastmatch_1.1-3
## [28] digest_0.6.31
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## [30] nor1mix_1.3-0
## [31] sctransform_0.3.5
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## [38] gridBase_0.4-7
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## [40] DelayedMatrixStats_1.16.0
## [41] minqa_1.2.5
## [42] reticulate_1.30
## [43] GetoptLong_1.0.5
## [44] xfun_0.39
## [45] bslib_0.5.0
## [46] zoo_1.8-12
## [47] tidyselect_1.2.0
## [48] reshape2_1.4.4
## [49] ica_1.0-3
## [50] viridisLite_0.4.2
## [51] rtracklayer_1.54.0
## [52] rlang_1.1.1
## [53] hexbin_1.28.3
## [54] jquerylib_0.1.4
## [55] glue_1.6.2
## [56] modelr_0.1.9
## [57] ggsignif_0.6.3
## [58] labeling_0.4.2
## [59] SparseM_1.81
## [60] httpuv_1.6.11
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## [62] reactome.db_1.77.0
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## [65] jsonlite_1.8.5
## [66] bit_4.0.5
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## [68] Rsamtools_2.10.0
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## [71] scattermore_0.8
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## [73] yulab.utils_0.0.6
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## [75] bitops_1.0-7
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## [78] RSQLite_2.2.17
## [79] data.table_1.14.8
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## [81] rstudioapi_0.14
## [82] GenomicAlignments_1.30.0
## [83] qvalue_2.26.0
## [84] listenv_0.9.0
## [85] miniUI_0.1.1.1
## [86] gridGraphics_0.5-1
## [87] readxl_1.4.1
## [88] lifecycle_1.0.3
## [89] htm2txt_2.2.2
## [90] munsell_0.5.0
## [91] cellranger_1.1.0
## [92] caTools_1.18.2
## [93] codetools_0.2-19
## [94] coda_0.19-4
## [95] MultiAssayExperiment_1.20.0
## [96] lmtest_0.9-40
## [97] missMethyl_1.28.0
## [98] xtable_1.8-4
## [99] ROCR_1.0-11
## [100] googlesheets4_1.0.1
## [101] BiocManager_1.30.21
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## [117] prettyunits_1.1.1
## [118] lubridate_1.9.2
## [119] ggridges_0.5.4
## [120] googledrive_2.0.0
## [121] reprex_2.0.2
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## [126] gargle_1.5.0
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## [128] htmltools_0.5.5
## [129] yaml_2.3.7
## [130] utf8_1.2.3
## [131] MCMCpack_1.6-3
## [132] interactiveDisplayBase_1.32.0
## [133] XML_3.99-0.14
## [134] withr_2.5.0
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## [137] rngtools_1.5.2
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## [139] progressr_0.13.0
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## [150] rjson_0.2.21
## [151] rstatix_0.7.0
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## [155] magrittr_2.0.3
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## [158] ape_5.7-1
## [159] ggplotify_0.1.0
## [160] xml2_1.3.4
## [161] httr_1.4.6
## [162] assertthat_0.2.1
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## [166] Rhdf5lib_1.20.0
## [167] progress_1.2.2
## [168] KEGGREST_1.34.0
## [169] treeio_1.18.1
## [170] shape_1.4.6
## [171] gtools_3.9.4
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## [177] carData_3.0-5
## [178] ggfun_0.0.7
## [179] colorspace_2.1-0
## [180] generics_0.1.3
## [181] RobustRankAggreg_1.2.1
## [182] pillar_1.9.0
## [183] tweenr_2.0.2
## [184] sp_1.6-1
## [185] GenomeInfoDbData_1.2.9
## [186] plyr_1.8.8
## [187] gtable_0.3.3
## [188] rvest_1.0.3
## [189] restfulr_0.0.15
## [190] knitr_1.43
## [191] shadowtext_0.1.2
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## [193] fastmap_1.1.1
## [194] Cairo_1.6-0
## [195] doParallel_1.0.17
## [196] quantreg_5.94
## [197] broom_1.0.1
## [198] openssl_2.0.6
## [199] scales_1.2.1
## [200] filelock_1.0.2
## [201] backports_1.4.1
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## [203] base64_2.0.1
## [204] vroom_1.5.7
## [205] enrichplot_1.14.2
## [206] mcmc_0.9-7
## [207] hms_1.1.2
## [208] ggforce_0.3.4
## [209] scrime_1.3.5
## [210] Rtsne_0.16
## [211] shiny_1.7.2
## [212] IlluminaHumanMethylation450kanno.ilmn12.hg19_0.6.0
## [213] polyclip_1.10-4
## [214] numDeriv_2016.8-1.1
## [215] siggenes_1.68.0
## [216] lazyeval_0.2.2
## [217] crayon_1.5.2
## [218] downloader_0.4
## [219] sparseMatrixStats_1.10.0
## [220] viridis_0.6.2
## [221] reshape_0.8.9
## [222] compiler_4.2.2
## [223] spatstat.geom_3.2-1Clear
rm(list=ls())