Script 5: Checking LLS genotypes
Compiled: June 25, 2024
This script compares measured LLS genotypes to those predicted from DNAm data to check for sample mixups.
Setup
Load packages
library(tidyverse)
library(snpStats)Read in the LLS genotyping files
lls_plink <- read.plink(
bed = '../GOTO_Data/Processing/Genotypes/LLS_Offspring_Partners_Final_36_130_Overlap.bed',
bim = '../GOTO_Data/Processing/Genotypes/LLS_Offspring_Partners_Final_36_130_Overlap.bim',
fam = '../GOTO_Data/Processing/Genotypes/LLS_Offspring_Partners_Final_36_130_Overlap.fam')OmicsPrint
Read in the results from omicsPrint (genotyping from DNA methylation values in each of the 3 tissues)
Blood
load('../GOTO_Data/Processing/omicsPrint/GOTO_omicsPrint-blood.Rdata')
dnam_blood <- as.data.frame(genotype)
rm(list = c('mismatches', 'out', 'relations', 'genotype'))
print(paste0("We have data on ",
nrow(dnam_blood), " SNPs from ",
ncol(dnam_blood), " fasted blood samples"))## [1] "We have data on 1230 SNPs from 200 fasted blood samples"Muscle
load('../GOTO_Data/Processing/omicsPrint/GOTO_omicsPrint-muscle.Rdata')
dnam_muscle <- as.data.frame(genotype)
rm(list = c('mismatches', 'out', 'relations', 'genotype'))
print(paste0("We have data on ",
nrow(dnam_muscle), " SNPs from ",
ncol(dnam_muscle), " skeletal muscle samples"))## [1] "We have data on 1010 SNPs from 164 skeletal muscle samples"Fat
load('../GOTO_Data/Processing/omicsPrint/GOTO_omicsPrint-fat.Rdata')
dnam_fat <- as.data.frame(genotype)
rm(list = c('mismatches', 'out', 'relations', 'genotype'))
print(paste0("We have data on ",
nrow(dnam_fat), " SNPs from ",
ncol(dnam_fat), " adipose tissue samples"))## [1] "We have data on 1059 SNPs from 184 adipose tissue samples"Key file
Read in key file sent from M.B.
key_id_gwas <- read_csv('../GOTO_Data/Processing/Genotypes/GOTO_GWASid-IOP2id_20210211.csv')## Rows: 164 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): GWASnr
## dbl (2): GOT, IOP2_ID
##
## 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.GWASnr needs formatting as in the genotype data there is no LLS prefix and only a single underscore
key_id_gwas$GWASnr <- sub('LLS', '', sub('__', '_', key_id_gwas$GWASnr))Read in the targets file of the methylation data to link sample IDs to other IDs
load('../GOTO_Data/Processing/GOTO_targets-unfiltered.Rdata')
key_id_dnam <- targets
rm('targets')Save a sample ID key for each set of samples:
200 blood samples
key_blood <- key_id_dnam %>% dplyr::select(sample_ID = Basename,
IOP2_ID,
HMU_ID,
DNA_labnr) %>%
filter(sample_ID %in% colnames(dnam_blood)) %>%
mutate(IOP2_ID = as.numeric(as.character(IOP2_ID)))
key_blood <- left_join(key_blood, key_id_gwas, by = 'IOP2_ID')164 muscle samples
key_muscle <- key_id_dnam %>% dplyr::select(sample_ID = Basename,
IOP2_ID,
HMU_ID,
DNA_labnr) %>%
filter(sample_ID %in% colnames(dnam_muscle)) %>%
mutate(IOP2_ID = as.numeric(as.character(IOP2_ID)))
key_muscle <- left_join(key_muscle, key_id_gwas, by = 'IOP2_ID')184 fat samples
key_fat <- key_id_dnam %>% dplyr::select(sample_ID = Basename,
IOP2_ID,
HMU_ID,
DNA_labnr) %>%
filter(sample_ID %in% colnames(dnam_fat)) %>%
mutate(IOP2_ID = as.numeric(as.character(IOP2_ID)))
key_fat <- left_join(key_fat, key_id_gwas, by = 'IOP2_ID')Save
save(key_blood, key_muscle, key_fat, file = '../GOTO_Data/Processing/Genotypes/GOTO_data-keys.Rdata')
rm(list = c('key_id_gwas', 'key_id_dnam'))Subsetting
Annotating CpGs to SNPs
Load in mapping file from Zhou
cpg_snp <- read_tsv(
"/exports/molepi/users/ljsinke/LLS/Shared_Data/Manifests/EPIC.hg38.commonsnp.tsv")
dim(cpg_snp)## [1] 667132 111Save only CpGs that map to a single SNP
cpg_snp <- cpg_snp %>% dplyr::select(cpg=probeID, snpID)
cpg_snp <- cpg_snp %>% group_by(cpg) %>%
mutate(n = n()) %>% ungroup() %>% filter(n == 1) %>% dplyr::select(-n)Merge with the beta2genotype data
595 CpGs in blood data map to only 1 SNP
dnam_blood <- dnam_blood %>% rownames_to_column(var = 'cpg')
dim(dnam_blood)## [1] 1230 201dnam_blood <- inner_join(cpg_snp, dnam_blood, by = 'cpg')
dim(dnam_blood)## [1] 595 202488 CpGs in muscle data map to only 1 SNP
dnam_muscle <- dnam_muscle %>% rownames_to_column(var = 'cpg')
dim(dnam_muscle)## [1] 1010 165dnam_muscle <- inner_join(cpg_snp, dnam_muscle, by = 'cpg')
dim(dnam_muscle)## [1] 488 166508 CpGs in fat data map to only 1 SNP
dnam_fat <- dnam_fat %>% rownames_to_column(var = 'cpg')
dim(dnam_fat)## [1] 1059 185dnam_fat <- inner_join(cpg_snp, dnam_fat, by = 'cpg')
dim(dnam_fat)## [1] 508 186Keep only samples with genotype data
48 individuals were not part of LLS but participated in GOTO. These individuals have genotypes in a different dataset. We remove their samples from the beta2genotype data, but keep them in the next script.
The rownames of the genotype data have a . where the GWASnr has an underscore.
rownames(lls_plink$genotypes) <- gsub('.', '_',
rownames(lls_plink$genotypes), fixed=TRUE)First we subset the keys
Blood
key_blood <- key_blood %>% filter(GWASnr %in% rownames(lls_plink$genotypes))Muscle
key_muscle <- key_muscle %>% filter(GWASnr %in% rownames(lls_plink$genotypes))Fat
key_fat <- key_fat %>% filter(GWASnr %in% rownames(lls_plink$genotypes))Now we can subset the beta2genotype data using these keys
Final blood data of 595 CpGs from 156 samples
dnam_blood <- dnam_blood %>% dplyr::select(cpg, snpID, key_blood$sample_ID)
dim(dnam_blood)## [1] 595 156Final muscle data of 488 CpGs from 136 samples
dnam_muscle <- dnam_muscle %>% dplyr::select(cpg, snpID, key_muscle$sample_ID)
dim(dnam_muscle)## [1] 488 136Final fat data of 508 CpGs from 142 samples
dnam_fat <- dnam_fat %>% dplyr::select(cpg, snpID, key_fat$sample_ID)
dim(dnam_fat)## [1] 508 142Subsetting the genotype data
Now we can use the finalized beta2genotype data to subset the genotype data, which contains data on 302806 SNPs from 2489 individuals.
lls_plink <- as.data.frame(t(lls_plink$genotypes))
dim(lls_plink)## [1] 302806 248956 / 595 SNPs for blood found in the genotype data from 78 people
genotype_blood <- lls_plink %>% dplyr::select(key_blood$GWASnr) %>%
rownames_to_column(var = 'snpID') %>%
filter(snpID %in% dnam_blood$snpID) %>% arrange(snpID)
dim(genotype_blood)## [1] 56 7841 / 488 SNPs for muscle found in the genotype data from 68 people
genotype_muscle <- lls_plink %>% dplyr::select(key_muscle$GWASnr) %>%
rownames_to_column(var = 'snpID') %>%
filter(snpID %in% dnam_muscle$snpID) %>% arrange(snpID)
dim(genotype_muscle)## [1] 41 6850 / 508 SNPs for fat found in the genotype data from 71 people
genotype_fat <- lls_plink %>% dplyr::select(key_fat$GWASnr) %>%
rownames_to_column(var = 'snpID') %>%
filter(snpID %in% dnam_fat$snpID) %>% arrange(snpID)
dim(genotype_fat)## [1] 50 71Subsetting beta2genotype for SNPs
Finally we keep only the SNPs that overlap with genotype data
56 single SNP CpGs from 156 blood samples
dnam_blood <- dnam_blood %>% filter(snpID %in% genotype_blood$snpID) %>% arrange(snpID)
dim(dnam_blood)## [1] 56 15641 single SNP CpGs from 136 muscle samples
dnam_muscle <- dnam_muscle %>% filter(snpID %in% genotype_muscle$snpID) %>% arrange(snpID)
dim(dnam_muscle)## [1] 41 13650 single SNP CpGs from 142 fat samples
dnam_fat <- dnam_fat %>% filter(snpID %in% genotype_fat$snpID) %>% arrange(snpID)
dim(dnam_fat)## [1] 50 142Preprocessing
The genotype data needs to be converted to numeric
genotype_blood[, c(2:ncol(genotype_blood))] <- sapply(genotype_blood[, c(2:ncol(genotype_blood))], as.numeric)
genotype_muscle[, c(2:ncol(genotype_muscle))] <- sapply(genotype_muscle[, c(2:ncol(genotype_muscle))], as.numeric)
genotype_fat[, c(2:ncol(genotype_fat))] <- sapply(genotype_fat[, c(2:ncol(genotype_fat))], as.numeric)Analysis
Blood
Create a data frame of assumed and actual matches for each sample
dnam <- dnam_blood
gwas <- genotype_blood
key <- key_blood
out_blood <- data.frame()
for(j in 3:ncol(dnam)){
ind <- dnam[,j]
for(i in 2:ncol(gwas)){
out_blood <- rbind(out_blood,
data.frame(sample_ID = (colnames(dnam)[j]),
actual_match = colnames(gwas)[i],
assumed_match = key[key$sample_ID == colnames(dnam)[j],6],
n_snps = data.frame(xtabs(~gwas[,i] == ind))[2,2]))
}
}
out <- out_blood %>%
group_by(sample_ID) %>%
mutate(max = max(n_snps)) %>% ungroup()We visualize the results. For every sample the assumed match also has the most matching SNPs, suggesting no sample mixups for blood.
out %>% ggplot(aes(x = sample_ID, y = n_snps)) +
geom_point(fill = ifelse(out$actual_match == out$assumed_match,
'yellow', 'grey60'),
color = ifelse(out$n_snps == out$max,
'red', 'grey60'),
shape = 21, size = 2) +
theme_bw() +
ylab('Number of matching SNPs \n') + xlab('Sample ID') +
ggtitle('Comparison of genotypes for all sample-person pairs') +
theme(axis.text.x = element_text(angle = 90),
panel.background = element_rect(fill = 'white'),
axis.text.x.bottom = element_blank(),
panel.grid = element_blank(),
axis.title = element_text(size = 16),
title = element_text(size = 14),
axis.text.y = element_text(size = 14)) +
scale_x_discrete(expand = c(-1.01,-1.01))
The smallest overlap is 41 SNPs and as shown above, all samples have the highest match with the assumed match based on genotype.
out %>% filter(n_snps == max) %>% arrange(max)## # A tibble: 154 × 5
## sample_ID actual_match assumed_match n_snps max
## <chr> <chr> <chr> <int> <int>
## 1 203548970103_R01C01 43_1_1c4 43_1_1c4 41 41
## 2 203548970103_R02C01 43_1_1c4 43_1_1c4 43 43
## 3 203548970004_R01C01 218_1_1c2 218_1_1c2 44 44
## 4 203548970004_R02C01 218_1_1c2 218_1_1c2 44 44
## 5 203548970040_R01C01 397_1_2c6s 397_1_2c6s 45 45
## 6 203548970040_R02C01 397_1_2c6s 397_1_2c6s 45 45
## 7 203548870093_R03C01 15_1_2c7 15_1_2c7 45 45
## 8 203548870093_R04C01 15_1_2c7 15_1_2c7 45 45
## 9 203548980012_R01C01 200_1_5c3 200_1_5c3 45 45
## 10 203548980012_R02C01 200_1_5c3 200_1_5c3 45 45
## # ℹ 144 more rowsout %>% filter(n_snps == max) %>% filter(actual_match != assumed_match)## # A tibble: 0 × 5
## # ℹ 5 variables: sample_ID <chr>, actual_match <chr>, assumed_match <chr>,
## # n_snps <int>, max <int>Save
save(out, key_blood, file='../GOTO_Data/Processing/Genotypes/GOTO_genotypeCheck-LLS-blood.Rdata')Muscle
Create a data frame of assumed and actual matches for each sample
dnam <- dnam_muscle
gwas <- genotype_muscle
key <- key_muscle
out_muscle <- data.frame()
for(j in 3:ncol(dnam)){
ind <- dnam[,j]
for(i in 2:ncol(gwas)){
out_muscle <- rbind(out_muscle,
data.frame(sample_ID = (colnames(dnam)[j]),
actual_match = colnames(gwas)[i],
assumed_match = key[key$sample_ID == colnames(dnam)[j],6],
n_snps = data.frame(xtabs(~gwas[,i] == ind))[2,2]))
}
}
out <- out_muscle %>% group_by(sample_ID) %>% mutate(max = max(n_snps)) %>% ungroup()We visualize the results.
out %>% ggplot(aes(x = sample_ID, y = n_snps)) +
geom_point(fill = ifelse(out$actual_match == out$assumed_match,
'yellow', 'grey60'),
color = ifelse(out$n_snps == out$max,
'red', 'grey60'),
shape = 21, size = 2) +
theme_bw() +
ylab('Number of matching SNPs \n') + xlab('Sample ID') +
ggtitle('Comparison of genotypes for all sample-person pairs') +
theme(axis.text.x = element_text(angle = 90),
panel.background = element_rect(fill = 'white'),
axis.text.x.bottom = element_blank(),
panel.grid = element_blank(),
axis.title = element_text(size = 16),
title = element_text(size = 14),
axis.text.y = element_text(size = 14)) +
scale_x_discrete(expand = c(-1.01,-1.01))
The smallest overlap is 29 SNPs and two samples have been switched. This confirms what we saw with omicsPrint.
out %>% filter(n_snps == max) %>% arrange(max)## # A tibble: 134 × 5
## sample_ID actual_match assumed_match n_snps max
## <chr> <chr> <chr> <int> <int>
## 1 203548970101_R08C01 38_1_1c6s 38_1_1c6s 29 29
## 2 203527980015_R01C01 349_1_7c16 349_1_7c16 32 32
## 3 203527980015_R02C01 349_1_7c16 349_1_7c16 32 32
## 4 203527980082_R03C01 15_1_2c7 15_1_2c7 33 33
## 5 203527980082_R04C01 15_1_2c7 15_1_2c7 33 33
## 6 203548870077_R02C01 138_1_3c4 138_1_3c4 33 33
## 7 203548870019_R08C01 246_1_2c6 246_1_2c6 33 33
## 8 203548970101_R07C01 38_1_1c6s 38_1_1c6s 34 34
## 9 203548970103_R05C01 14_1_1c6 14_1_1c6 34 34
## 10 203548970103_R06C01 14_1_1c6 14_1_1c6 34 34
## # ℹ 124 more rowsout %>% filter(n_snps == max) %>% filter(actual_match != assumed_match)## # A tibble: 2 × 5
## sample_ID actual_match assumed_match n_snps max
## <chr> <chr> <chr> <int> <int>
## 1 203548970088_R08C01 58_1_1c5 58_1_1c5s 35 35
## 2 203548980011_R03C01 58_1_1c5s 58_1_1c5 36 36Save
save(out, key_muscle, file='../GOTO_Data/Processing/Genotypes/GOTO_genotypeCheck-LLS-muscle.Rdata')Fat
Create a data frame of assumed and actual matches for each sample
dnam <- dnam_fat
gwas <- genotype_fat
key <- key_fat
out_fat <- data.frame()
for(j in 3:ncol(dnam)){
ind <- dnam[,j]
for(i in 2:ncol(gwas)){
out_fat <- rbind(out_fat,
data.frame(sample_ID = (colnames(dnam)[j]),
actual_match = colnames(gwas)[i],
assumed_match = key[key$sample_ID == colnames(dnam)[j],6],
n_snps = data.frame(xtabs(~gwas[,i] == ind))[2,2]))
}
}
out <- out_fat %>% group_by(sample_ID) %>% mutate(max = max(n_snps)) %>% ungroup()We visualize the results.
out %>% ggplot(aes(x = sample_ID, y = n_snps)) +
geom_point(fill = ifelse(out$actual_match == out$assumed_match,
'yellow', 'grey60'),
color = ifelse(out$n_snps == out$max,
'red', 'grey60'),
shape = 21, size = 2) +
theme_bw() +
ylab('Number of matching SNPs \n') + xlab('Sample ID') +
ggtitle('Comparison of genotypes for all sample-person pairs') +
theme(axis.text.x = element_text(angle = 90),
panel.background = element_rect(fill = 'white'),
axis.text.x.bottom = element_blank(),
panel.grid = element_blank(),
axis.title = element_text(size = 16),
title = element_text(size = 14),
axis.text.y = element_text(size = 14)) +
scale_x_discrete(expand = c(-1.01,-1.01))
The smallest overlap is 29 SNPs and one sample is not from the assumed individual. We will have to drop this sample and its pair.
out %>% filter(n_snps == max) %>% arrange(max)## # A tibble: 140 × 5
## sample_ID actual_match assumed_match n_snps max
## <chr> <chr> <chr> <int> <int>
## 1 203548970042_R05C01 349_1_7c11 349_1_7c7s 29 29
## 2 203548970100_R08C01 159_1_3c22s 159_1_3c22s 35 35
## 3 203548970098_R07C01 397_1_2c6s 397_1_2c6s 39 39
## 4 203548970098_R08C01 397_1_2c6s 397_1_2c6s 39 39
## 5 203548970100_R01C01 218_1_1c2 218_1_1c2 40 40
## 6 203548970100_R02C01 218_1_1c2 218_1_1c2 40 40
## 7 203740910037_R05C01 461_1_2c4s 461_1_2c4s 40 40
## 8 203740910037_R06C01 461_1_2c4s 461_1_2c4s 40 40
## 9 203527980080_R03C01 138_1_3c4 138_1_3c4 41 41
## 10 203548980032_R03C01 43_1_1c4 43_1_1c4 41 41
## # ℹ 130 more rowsout %>% filter(n_snps == max) %>% filter(actual_match != assumed_match)## # A tibble: 1 × 5
## sample_ID actual_match assumed_match n_snps max
## <chr> <chr> <chr> <int> <int>
## 1 203548970042_R05C01 349_1_7c11 349_1_7c7s 29 29Save
save(out, key_fat, file='../GOTO_Data/Processing/Genotypes/GOTO_genotypeCheck-LLS-fat.Rdata')Session Info
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] snpStats_1.44.0
## [2] survival_3.5-5
## [3] ggrepel_0.9.1
## [4] ggfortify_0.4.14
## [5] irlba_2.3.5.1
## [6] Matrix_1.5-4.1
## [7] omicsPrint_1.14.0
## [8] MASS_7.3-60
## [9] DNAmArray_2.0.0
## [10] pls_2.8-2
## [11] FDb.InfiniumMethylation.hg19_2.2.0
## [12] org.Hs.eg.db_3.14.0
## [13] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
## [14] GenomicFeatures_1.46.5
## [15] AnnotationDbi_1.56.2
## [16] IlluminaHumanMethylationEPICmanifest_0.3.0
## [17] minfi_1.40.0
## [18] bumphunter_1.36.0
## [19] locfit_1.5-9.8
## [20] iterators_1.0.14
## [21] foreach_1.5.2
## [22] Biostrings_2.62.0
## [23] XVector_0.34.0
## [24] SummarizedExperiment_1.24.0
## [25] Biobase_2.58.0
## [26] MatrixGenerics_1.10.0
## [27] matrixStats_1.0.0
## [28] GenomicRanges_1.46.1
## [29] GenomeInfoDb_1.34.9
## [30] IRanges_2.32.0
## [31] S4Vectors_0.36.2
## [32] BiocGenerics_0.44.0
## [33] BiocParallel_1.32.6
## [34] MethylAid_1.28.0
## [35] forcats_0.5.2
## [36] stringr_1.5.0
## [37] dplyr_1.1.3
## [38] purrr_0.3.4
## [39] readr_2.1.2
## [40] tidyr_1.2.1
## [41] tibble_3.2.1
## [42] ggplot2_3.4.3
## [43] tidyverse_1.3.2
## [44] rmarkdown_2.16
##
## loaded via a namespace (and not attached):
## [1] utf8_1.2.3 tidyselect_1.2.0
## [3] RSQLite_2.2.17 grid_4.2.2
## [5] munsell_0.5.0 codetools_0.2-19
## [7] preprocessCore_1.60.2 withr_2.5.0
## [9] colorspace_2.1-0 filelock_1.0.2
## [11] highr_0.10 knitr_1.43
## [13] rstudioapi_0.14 labeling_0.4.2
## [15] GenomeInfoDbData_1.2.9 farver_2.1.1
## [17] bit64_4.0.5 rhdf5_2.42.1
## [19] vctrs_0.6.3 generics_0.1.3
## [21] xfun_0.39 timechange_0.2.0
## [23] BiocFileCache_2.2.1 R6_2.5.1
## [25] illuminaio_0.40.0 bitops_1.0-7
## [27] rhdf5filters_1.10.1 cachem_1.0.8
## [29] reshape_0.8.9 DelayedArray_0.24.0
## [31] assertthat_0.2.1 vroom_1.5.7
## [33] promises_1.2.0.1 BiocIO_1.8.0
## [35] scales_1.2.1 googlesheets4_1.0.1
## [37] gtable_0.3.3 rlang_1.1.1
## [39] genefilter_1.76.0 splines_4.2.2
## [41] rtracklayer_1.54.0 gargle_1.5.0
## [43] GEOquery_2.62.2 htm2txt_2.2.2
## [45] hexbin_1.28.3 broom_1.0.1
## [47] yaml_2.3.7 reshape2_1.4.4
## [49] RaggedExperiment_1.18.0 modelr_0.1.9
## [51] backports_1.4.1 httpuv_1.6.11
## [53] tools_4.2.2 gridBase_0.4-7
## [55] nor1mix_1.3-0 ellipsis_0.3.2
## [57] jquerylib_0.1.4 RColorBrewer_1.1-3
## [59] siggenes_1.68.0 MultiAssayExperiment_1.20.0
## [61] Rcpp_1.0.10 plyr_1.8.8
## [63] sparseMatrixStats_1.10.0 progress_1.2.2
## [65] zlibbioc_1.44.0 RCurl_1.98-1.12
## [67] prettyunits_1.1.1 openssl_2.0.6
## [69] haven_2.5.1 fs_1.6.2
## [71] magrittr_2.0.3 data.table_1.14.8
## [73] reprex_2.0.2 googledrive_2.0.0
## [75] hms_1.1.2 mime_0.12
## [77] evaluate_0.21 xtable_1.8-4
## [79] XML_3.99-0.14 mclust_6.0.0
## [81] readxl_1.4.1 gridExtra_2.3
## [83] compiler_4.2.2 biomaRt_2.50.3
## [85] crayon_1.5.2 htmltools_0.5.5
## [87] later_1.3.1 tzdb_0.4.0
## [89] lubridate_1.9.2 DBI_1.1.3
## [91] dbplyr_2.2.1 rappdirs_0.3.3
## [93] cli_3.6.1 quadprog_1.5-8
## [95] pkgconfig_2.0.3 GenomicAlignments_1.30.0
## [97] xml2_1.3.4 annotate_1.72.0
## [99] bslib_0.5.0 rngtools_1.5.2
## [101] multtest_2.50.0 beanplot_1.3.1
## [103] rvest_1.0.3 doRNG_1.8.6
## [105] scrime_1.3.5 digest_0.6.31
## [107] base64_2.0.1 cellranger_1.1.0
## [109] DelayedMatrixStats_1.16.0 restfulr_0.0.15
## [111] curl_5.0.1 shiny_1.7.2
## [113] Rsamtools_2.10.0 rjson_0.2.21
## [115] lifecycle_1.0.3 nlme_3.1-162
## [117] jsonlite_1.8.5 Rhdf5lib_1.20.0
## [119] askpass_1.1 limma_3.54.2
## [121] fansi_1.0.4 pillar_1.9.0
## [123] lattice_0.21-8 KEGGREST_1.34.0
## [125] fastmap_1.1.1 httr_1.4.6
## [127] glue_1.6.2 png_0.1-8
## [129] bit_4.0.5 stringi_1.7.12
## [131] sass_0.4.6 HDF5Array_1.22.1
## [133] blob_1.2.4 memoise_2.0.1Clear
rm(list=ls())