This script annotates adipose CpGs, saves data for TFBS enrichment analysis, locates nearest genes, and reports any DMRs.
Load packages
library(ggrepel)
library(GenomicRanges)
library(ggpubr)
library(DNAmArray)
library(tidyverse)Load data
load("../GOTO_Data/GOTO_results-top-fat.Rdata")
head(top_cpgs)## cpg beta SE p padj_fdr
## cg12544951 cg12544951 0.07383968 0.011865977 3.655413e-09 0.001242957
## cg03475429 cg03475429 0.02358276 0.003853748 6.578430e-09 0.001242957
## cg01733176 cg01733176 -0.03964828 0.006410592 5.500018e-09 0.001242957
## cg06524692 cg06524692 0.02568704 0.004139242 4.030494e-09 0.001242957
## cg08156066 cg08156066 0.06355290 0.010499712 9.559126e-09 0.001444913
## cg05844247 cg05844247 0.06323091 0.010632635 1.718341e-08 0.001642733
## t N
## cg12544951 5.899077 178
## cg03475429 5.801347 178
## cg01733176 -5.831291 178
## cg06524692 5.882937 178
## cg08156066 5.738371 178
## cg05844247 5.638203 178dim(top_cpgs)## [1] 230 7manifest_hg19 (fetched in 2023 from https://zwdzwd.github.io/InfiniumAnnotation)
probeID as cpg - CpG IDCpG_chrm as cpg_chr_hg19 - chromosome (hg19)CpG_beg as cpg_start_hg19 - CpG start position (hg19)CpG_end as cpg_end_hg19 - CpG end position (hg19)probe_strand as cpg_strand - strandgene_HGNCmanifest_hg19 <- read_tsv(
"/exports/molepi/users/ljsinke/LLS/Shared_Data/Manifests/EPIC.hg19.manifest.tsv.gz")## Rows: 865918 Columns: 57
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (21): CpG_chrm, probe_strand, probeID, channel, designType, nextBase, ne...
## dbl (24): CpG_beg, CpG_end, address_A, address_B, probeCpGcnt, context35, pr...
## lgl (12): posMatch, MASK_mapping, MASK_typeINextBaseSwitch, MASK_rmsk15, MAS...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.anno <- manifest_hg19 %>%
dplyr::select(
cpg = probeID,
cpg_chr = CpG_chrm,
cpg_start = CpG_beg,
cpg_end = CpG_end,
cpg_strand = probe_strand,
gene_HGNC
) %>%
mutate(
cpg_chr = substr(cpg_chr,4,5)
)
anno <- anno %>%
dplyr::filter(cpg %in% top_cpgs$cpg)manifest_chrom <- read_tsv(
"/exports/molepi/users/ljsinke/LLS/Shared_Data/Manifests/EPIC.hg19.REMC.chromHMM.tsv.gz"
)## Rows: 865918 Columns: 131
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (129): CpG_chrm, probeID, E001, E002, E003, E004, E005, E006, E007, E008...
## dbl (2): CpG_beg, CpG_end
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.manifest_chrom <- manifest_chrom %>%
dplyr::select(
cpg = probeID,
E063)
anno <- left_join(
anno, manifest_chrom,
by="cpg"
)
top_cpgs <- left_join(top_cpgs, anno, by="cpg")
head(top_cpgs)## cpg beta SE p padj_fdr t N
## 1 cg12544951 0.07383968 0.011865977 3.655413e-09 0.001242957 5.899077 178
## 2 cg03475429 0.02358276 0.003853748 6.578430e-09 0.001242957 5.801347 178
## 3 cg01733176 -0.03964828 0.006410592 5.500018e-09 0.001242957 -5.831291 178
## 4 cg06524692 0.02568704 0.004139242 4.030494e-09 0.001242957 5.882937 178
## 5 cg08156066 0.06355290 0.010499712 9.559126e-09 0.001444913 5.738371 178
## 6 cg05844247 0.06323091 0.010632635 1.718341e-08 0.001642733 5.638203 178
## cpg_chr cpg_start cpg_end cpg_strand gene_HGNC E063
## 1 20 21695342 21695344 - PAX1 13_ReprPC
## 2 10 94982392 94982394 - <NA> 15_Quies
## 3 4 111561069 111561071 + PITX2 13_ReprPC
## 4 1 28864470 28864472 - RCC1 5_TxWk
## 5 20 21695333 21695335 - PAX1 13_ReprPC
## 6 20 21694427 21694429 + PAX1 13_ReprPCCpGs for HOMER
homer <- top_cpgs %>%
mutate(
cpg_chr = paste0('chr', cpg_chr)) %>%
dplyr::select(
cpg,
chr = cpg_chr,
start = cpg_start,
end = cpg_end,
strand = cpg_strand
)
write_tsv(homer, file="../GOTO_Data/Homer/GOTO_Homer-Fat.tsv")Save significant CpGs
print(paste0("There are ",
nrow(top_cpgs),
" CpGs significant at 5% FDR level"))## [1] "There are 230 CpGs significant at 5% FDR level"cpg_top <- top_cpgs$cpgSave gene locations (saved previously)
txdb <- makeTxDbFromEnsembl(organism = "Homo sapiens",
release = 75,
circ_seqs = NULL,
server = "ensembldb.ensembl.org",
username = "anonymous", password = NULL, port = 0L,
tx_attrib = NULL)
gene_range <- unlist(cdsBy(txdb, by = "gene"))
gene_range <- unique(gene_range)
save(gene_range, file='/exports/molepi/users/ljsinke/LLS/Shared_Data/allGeneRanges.Rdata)Load gene_range object
load('/exports/molepi/users/ljsinke/LLS/Shared_Data/allGeneRanges.Rdata')
gene_range## GRanges object with 302587 ranges and 2 metadata columns:
## seqnames ranges strand | cds_id cds_name
## <Rle> <IRanges> <Rle> | <integer> <character>
## ENSG00000000003 X 99885795-99885863 - | 717267 ENSP00000362111
## ENSG00000000003 X 99887482-99887565 - | 717268 ENSP00000362111
## ENSG00000000003 X 99888402-99888536 - | 717269 ENSP00000362111
## ENSG00000000003 X 99888928-99889026 - | 717270 ENSP00000362111
## ENSG00000000003 X 99890175-99890249 - | 717271 ENSP00000362111
## ... ... ... ... . ... ...
## LRG_97 LRG_97 17272-17334 + | 799400 LRG_97p1
## LRG_97 LRG_97 17876-18035 + | 799401 LRG_97p1
## LRG_97 LRG_97 22463-22593 + | 799402 LRG_97p1
## LRG_98 LRG_98 10293-13424 + | 799403 LRG_98p1
## LRG_99 LRG_99 9069-10652 + | 799404 LRG_99p1
## -------
## seqinfo: 722 sequences (1 circular) from an unspecified genomeMake a GenomicRanges object for the top CpGs
cpg_range <- GRanges(seqnames = top_cpgs$cpg_chr,
IRanges(
start = top_cpgs$cpg_start,
end = top_cpgs$cpg_end,
names = top_cpgs$cpg
))
cpg_range## GRanges object with 230 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## cg12544951 20 21695342-21695344 *
## cg03475429 10 94982392-94982394 *
## cg01733176 4 111561069-111561071 *
## cg06524692 1 28864470-28864472 *
## cg08156066 20 21695333-21695335 *
## ... ... ... ...
## cg19575372 2 119612786-119612788 *
## cg02848074 13 31306177-31306179 *
## cg02468230 15 79405155-79405157 *
## cg03689324 15 74426924-74426926 *
## cg02282833 5 32726188-32726190 *
## -------
## seqinfo: 22 sequences from an unspecified genome; no seqlengthsInitialize a distance matrix
distance.matrix <- matrix(
NaN,
nrow = length(gene_range),
ncol = length(cpg_range),
dimnames = list(names(gene_range),
names(cpg_range)))Loop through CpGs
for(i in 1:nrow(top_cpgs)){
cpg <- cpg_range[i]
calc.distance <- distance(
cpg,
gene_range,
ignore.strand = T)
distance.matrix[,i] <- calc.distance
}
dim(distance.matrix)## [1] 302587 230distance.matrix <- as.data.frame(distance.matrix)
colnames(distance.matrix) <- names(cpg_range)
distance.matrix$gene <- names(gene_range)Make a list of nearest genes
gene_list <- lapply(names(cpg_range), function(x){
df <- (distance.matrix %>% dplyr::select(gene, all_of(x)))
df <- df[rowSums(is.na(df)) == 0,]
return(unique(df[df[,2] == min(df[,2]), ]))
})
gene_list[[1]]## gene cg12544951
## ENSG00000125813.6 ENSG00000125813 0Save gene symbols
ens2gene <- cinaR::grch37Bind
df <- data.frame()
for(i in 1:length(gene_list)){
df <- rbind(df,
data.frame(gene_nearest_ens = gene_list[[i]]$gene,
cpg = colnames(gene_list[[i]])[2],
gene_nearest_dist = gene_list[[i]][1,2])
)
}
sym <- ens2gene$symbol[match(df$gene_nearest_ens,
ens2gene$ensgene)]
df$gene_nearest_name <- sym
df <- df[match(cpg_top, df$cpg),]
top_cpgs <- top_cpgs[match(cpg_top, top_cpgs$cpg),]
top_cpgs <- left_join(top_cpgs, df, by = "cpg")
head(top_cpgs)## cpg beta SE p padj_fdr t N
## 1 cg12544951 0.07383968 0.011865977 3.655413e-09 0.001242957 5.899077 178
## 2 cg03475429 0.02358276 0.003853748 6.578430e-09 0.001242957 5.801347 178
## 3 cg01733176 -0.03964828 0.006410592 5.500018e-09 0.001242957 -5.831291 178
## 4 cg06524692 0.02568704 0.004139242 4.030494e-09 0.001242957 5.882937 178
## 5 cg08156066 0.06355290 0.010499712 9.559126e-09 0.001444913 5.738371 178
## 6 cg05844247 0.06323091 0.010632635 1.718341e-08 0.001642733 5.638203 178
## cpg_chr cpg_start cpg_end cpg_strand gene_HGNC E063 gene_nearest_ens
## 1 20 21695342 21695344 - PAX1 13_ReprPC ENSG00000125813
## 2 10 94982392 94982394 - <NA> 15_Quies ENSG00000138119
## 3 4 111561069 111561071 + PITX2 13_ReprPC ENSG00000164093
## 4 1 28864470 28864472 - RCC1 5_TxWk ENSG00000180198
## 5 20 21695333 21695335 - PAX1 13_ReprPC ENSG00000125813
## 6 20 21694427 21694429 + PAX1 13_ReprPC ENSG00000125813
## gene_nearest_dist gene_nearest_name
## 1 0 PAX1
## 2 84325 MYOF
## 3 6914 PITX2
## 4 0 RCC1
## 5 0 PAX1
## 6 679 PAX1Make DMR data frame
dmr_cpgs <- top_cpgs %>%
dplyr::select(
chromosome = cpg_chr,
start = cpg_start,
padj = padj_fdr)Create significance indicator
dmr_cpgs <- dmr_cpgs %>% mutate(
crit = ifelse(padj <= 0.05, 1, 0))Arrange by genomic position
dmr_cpgs <- dmr_cpgs %>%
arrange(chromosome, start) %>%
dplyr::select(chromosome, start, crit)Initialize variables
chromosome=1:22
MAXIMUM_REGION_LENGTH = 1000
mismatches = 3Run DMRfinder
for(x in chromosome){
tryCatch({chr1 = dmr_cpgs[dmr_cpgs[,1]==x,]
if(nrow(chr1) >= 1){
chr1 <- chr1 %>% arrange(start)
chr.final = data.frame(
coord = chr1$start,
crit = chr1$crit
)
last_coordinate = length( chr.final$crit )
next_coordinate = 0
for (i in 1:(last_coordinate-1)) {
if ( i>=next_coordinate ) {
if (chr.final$crit[ i ]==1) {
start_location = chr.final$coord[ i ]
last_visited_crit_loc = start_location
sum_of_ones = 1
number_of_items = 1
# start crawling loop
for (j in (i+1):last_coordinate ) {
if (chr.final$coord[ j ] > (last_visited_crit_loc + MAXIMUM_REGION_LENGTH)) { break }
if((number_of_items-sum_of_ones)>mismatches) { break } #Number of mismatches
number_of_items = number_of_items + 1
if (chr.final$crit[j]==1) {
last_visited_crit_loc = chr.final$coord[ j ]
sum_of_ones = sum_of_ones + 1
}
}
# now check if the result is good enough
if (sum_of_ones>=3) {
last_one=i+number_of_items-1
for (k in (i+number_of_items-1):1) {
if ( chr.final$crit[k] == 0 ) {
last_one = last_one - 1
number_of_items = number_of_items - 1
}
else {
break
}
}
cat(x, ';',start_location,";",chr.final$coord[last_one],";",sum_of_ones/number_of_items,"\n")
next_coordinate = last_one + 1
}
}
}
}
}}, error=function(e){})
}top_cpgs <- top_cpgs %>% arrange(desc(padj_fdr))
save(top_cpgs, file='../GOTO_Data/GOTO_results-top-fat.Rdata')
write_csv(top_cpgs, file='../GOTO_Data/Tables/ST11.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
## [22] KernSmooth_2.23-21
## [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
## [29] png_0.1-8
## [30] nor1mix_1.3-0
## [31] sctransform_0.3.5
## [32] scatterpie_0.1.8
## [33] cowplot_1.1.1
## [34] DOSE_3.20.1
## [35] ggraph_2.0.6
## [36] pkgconfig_2.0.3
## [37] GO.db_3.14.0
## [38] gridBase_0.4-7
## [39] spatstat.random_3.1-5
## [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
## [61] preprocessCore_1.60.2
## [62] reactome.db_1.77.0
## [63] DO.db_2.9
## [64] annotate_1.72.0
## [65] jsonlite_1.8.5
## [66] bit_4.0.5
## [67] mime_0.12
## [68] Rsamtools_2.10.0
## [69] stringi_1.7.12
## [70] spatstat.sparse_3.0-1
## [71] scattermore_0.8
## [72] spatstat.explore_3.1-0
## [73] yulab.utils_0.0.6
## [74] quadprog_1.5-8
## [75] bitops_1.0-7
## [76] cli_3.6.1
## [77] rhdf5filters_1.10.1
## [78] RSQLite_2.2.17
## [79] data.table_1.14.8
## [80] timechange_0.2.0
## [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
## [102] abind_1.4-5
## [103] farver_2.1.1
## [104] parallelly_1.36.0
## [105] RANN_2.6.1
## [106] aplot_0.1.7
## [107] askpass_1.1
## [108] ggtree_3.2.1
## [109] BiocIO_1.8.0
## [110] RcppAnnoy_0.0.20
## [111] goftest_1.2-3
## [112] patchwork_1.1.2
## [113] cluster_2.1.4
## [114] future.apply_1.11.0
## [115] tidytree_0.4.0
## [116] ellipsis_0.3.2
## [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
## [122] mclust_6.0.0
## [123] igraph_1.4.3
## [124] multtest_2.50.0
## [125] fgsea_1.20.0
## [126] gargle_1.5.0
## [127] spatstat.utils_3.0-3
## [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
## [135] fitdistrplus_1.1-11
## [136] bit64_4.0.5
## [137] rngtools_1.5.2
## [138] doRNG_1.8.6
## [139] progressr_0.13.0
## [140] GOSemSim_2.20.0
## [141] memoise_2.0.1
## [142] evaluate_0.21
## [143] tzdb_0.4.0
## [144] curl_5.0.1
## [145] fansi_1.0.4
## [146] highr_0.10
## [147] tensor_1.5
## [148] cachem_1.0.8
## [149] deldir_1.0-9
## [150] rjson_0.2.21
## [151] rstatix_0.7.0
## [152] clue_0.3-64
## [153] tools_4.2.2
## [154] sass_0.4.6
## [155] magrittr_2.0.3
## [156] RCurl_1.98-1.12
## [157] car_3.1-0
## [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
## [163] boot_1.3-28.1
## [164] globals_0.16.2
## [165] R6_2.5.1
## [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
## [172] statmod_1.5.0
## [173] BiocVersion_3.16.0
## [174] HDF5Array_1.22.1
## [175] rhdf5_2.42.1
## [176] splines_4.2.2
## [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
## [192] biomaRt_2.50.3
## [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
## [202] RaggedExperiment_1.18.0
## [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())