## Reading data created previously
data.path <- "../../code_validation/Meta_analysis_code/DATASETS/ROSMAP"
load("../../../coMethDMR_metaAnalysis/DNAm_RNA/data/matched_data.rda")
dim(matched.dnam)## [1] 431803 529dim(matched.exp)## [1] 55889 529matched.exp <- matched.exp[rowSums(matched.exp) > 0,]
#matched.phenotype
# gghistogram(
# matched.phenotype$braaksc,
# bins = 7,
# fill = "black",
# color = "white",
# alpha = 1
# )We will used residuals data for which confounding effects (age at death, sex, cell-type proportions, batch) have been removed.
load("../../../coMethDMR_metaAnalysis/DNAm_RNA/data/residuals.rda")The function getDNAm.target will extend the regions \(+-250Kbp\) and return the overlapping genes.
regions.gr <- rownames(resid_met) %>%
as.data.frame %>%
tidyr::separate(col = ".",into = c("chr","start","end")) %>%
makeGRangesFromDataFrame()
names(regions.gr) <- rownames(resid_met)
regions.gr## GRanges object with 118 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## chr19:49220102-49220235 chr19 49220102-49220235 *
## chr7:27153580-27153847 chr7 27153580-27153847 *
## chr7:27146237-27146445 chr7 27146237-27146445 *
## chr7:27154720-27155548 chr7 27154720-27155548 *
## chr7:27179268-27179432 chr7 27179268-27179432 *
## ... ... ... ...
## chr6:25042495-25042548 chr6 25042495-25042548 *
## chr6:32906460-32906734 chr6 32906460-32906734 *
## chr12:120835663-120835778 chr12 120835663-120835778 *
## chr22:37608611-37608819 chr22 37608611-37608819 *
## chr16:87886871-87886933 chr16 87886871-87886933 *
## -------
## seqinfo: 22 sequences from an unspecified genome; no seqlengths# function available in methTF package https://gitlab.com/tiagochst/methtf
regions.genes <- get_region_target_gene(
regions.gr = regions.gr,
genome = "hg19",
method = "window",
window.size = 500 * 10 ^ 3
) # 500 kb## Removing regions overlapping promoter regions## o Get promoter regions for hg19## o Remove promoter regions## Mapping regions to genes within a window of size: 5e+05 bpregions.genes <- regions.genes %>%
dplyr::filter(regions.genes$target %in% rownames(resid_exp))
dim(regions.genes)## [1] 978 3head(regions.genes)# http://www.r-tutor.com/elementary-statistics/simple-linear-regression/residual-plot
doParallel::registerDoParallel(detectCores()/2)
tab <- plyr::adply(
regions.genes,
.margins = 1,
.fun = function(row) {
tryCatch({
rna.target <- resid_exp[rownames(resid_exp) == row$target, , drop = FALSE]
met.residual <- resid_met[rownames(resid_met) == as.character(row$regionID), ]
df <- data.frame(
rna.residual = rna.target %>% as.numeric,
met.residual = met.residual %>% as.numeric,
Braak_stage = matched.phenotype$braaksc %>% as.numeric
)
# fit linear model:
results.all <- lm(
rna.residual ~ met.residual + Braak_stage, data = df
)
results.all.pval <- summary(results.all)$coefficients[
2, "Pr(>|t|)", drop = F] %>%
t %>% as.data.frame()
results.all.estimate <- summary(results.all)$coefficients[
2, "Estimate", drop = F] %>%
t %>% as.data.frame()
colnames(results.all.pval) <- paste0(
"all_pval_", colnames(results.all.pval))
colnames(results.all.estimate) <- paste0(
"all_estimate_", colnames(results.all.estimate))
return(
data.frame(
cbind(results.all.pval, results.all.estimate),
row.names = NULL,
stringsAsFactors = FALSE
)
)
}, error = function(e) {
print(row)
return()
})
},
.id = NULL,
.progress = "time",
.parallel = TRUE,
.inform = TRUE
)## Progress disabled when using parallel plyrreadr::write_csv(
tab,
path = "./NatComm_revision/DATASETS/gene_expression_results/results_regions_lm_250kb_window.csv"
)tab <- readr::read_csv(
file = "./NatComm_revision/DATASETS/gene_expression_results/results_regions_lm_250kb_window.csv",
col_types = readr::cols()
)
meta.analysis.folder <- "../../code_validation/Meta_analysis_code/meta_analysis_region_results/"
region.analysis <- readr::read_csv(
file.path(meta.analysis.folder,"step4_dmr_vs_cpgs/meta_analysis_sig_no_crossHyb_smoking_ov_comb_p_with_sig_single_cpgs.csv"),
col_types = readr::cols()
)
tab$all_fdr <- p.adjust(tab$all_pval_met.residual,method = "fdr")
output <- tab[,c("regionID","target_gene_name",
"all_estimate_met.residual",
"all_pval_met.residual","all_fdr")]
colnames(output) <- c(
"coMethDMR",
"geneSymbol",
"estimate.all",
"pval.all",
"fdr.all"
)
cols <- c(
grep("ROSMAP_coMethRegion",colnames(region.analysis)),
grep("Relation",colnames(region.analysis)):grep("^smoke_bi$",colnames(region.analysis))
)
output2 <- merge(
output,
region.analysis[,cols],
by.x = "coMethDMR",
by.y = "ROSMAP_coMethRegion",
all.x = TRUE
)
output2 <- output2 %>% arrange(pval.all)write.csv(
output2,
file = "./NatComm_revision/DATASETS/gene_expression_results/results_regions_lm_250kb_window_renamed.csv",
row.names = FALSE
)Load DNAm data that removed confounding effects.
The function get_region_target_gene will extend the regions \(+-250Kbp\) and return the overlapping genes.
probes.info <- sesameData::sesameDataGet("HM450.hg19.manifest")
dmr.gr <- probes.info[row.names(resid_met_cpg),]
regions.genes <- get_region_target_gene(
regions.gr = dmr.gr,
genome = "hg19",
method = "window",
window.size = 500 * 10 ^ 3
) # 500 kb## Removing regions overlapping promoter regions## o Get promoter regions for hg19## o Remove promoter regions## Mapping regions to genes within a window of size: 5e+05 bpregions.genes <- regions.genes %>%
dplyr::filter(regions.genes$target %in% rownames(resid_exp))
regions.genes$cpg <- names(dmr.gr)[
match(
regions.genes$regionID,
paste0(
as.data.frame(dmr.gr)$seqnames, ":",
as.data.frame(dmr.gr)$start,"-",
as.data.frame(dmr.gr)$end
)
)
]
dim(regions.genes)## [1] 16358 4head(regions.genes)# http://www.r-tutor.com/elementary-statistics/simple-linear-regression/residual-plot
doParallel::registerDoParallel(detectCores()/2)
tab.cpg <- plyr::adply(
regions.genes,
.margins = 1,
.fun = function(row) {
tryCatch({
rna.target <-
resid_exp[rownames(resid_exp) == row$target, , drop = FALSE]
met.residual <-
resid_met_cpg[rownames(resid_met_cpg) == as.character(row$cpg), ]
df <- data.frame(
rna.residual = rna.target %>% as.numeric,
met.residual = met.residual %>% as.numeric,
Braak_stage = matched.phenotype$braaksc %>% as.numeric
)
# fit linear model
results.all <- lm(
rna.residual ~ met.residual + Braak_stage, data = df
)
# get pvalues coeficients
results.all.pval <- summary(results.all)$coefficients[
2, 4, drop = F] %>% t %>% as.data.frame()
colnames(results.all.pval) <-
paste0("all_pval_", colnames(results.all.pval))
# get estimate coeficients
results.all.estimate <- summary(results.all)$coefficients[
2, 1, drop = F] %>% t %>% as.data.frame()
colnames(results.all.estimate) <-
paste0("all_estimate_", colnames(results.all.estimate))
return(
data.frame(
cbind(results.all.pval, results.all.estimate),
row.names = NULL,
stringsAsFactors = FALSE
)
)
}, error = function(e) {
print(row)
return()
})
},
.id = NULL,
.progress = "time",
.parallel = TRUE,
.inform = TRUE
)## Progress disabled when using parallel plyrreadr::write_csv(
tab.cpg,
path = "./NatComm_revision/DATASETS/gene_expression_results/results_single_cpg_lm_250kb_window.csv"
)tab.cpg <- readr::read_csv(
"./NatComm_revision/DATASETS/gene_expression_results/results_single_cpg_lm_250kb_window.csv",
col_types = readr::cols()
)
tab.cpg$fdr.all <- p.adjust(tab.cpg$all_pval_met.residual,method = "fdr")
output <- tab.cpg[,c("cpg","target_gene_name",
"all_estimate_met.residual",
"all_pval_met.residual","fdr.all")
]
colnames(output) <- c(
"cpg",
"geneSymbol",
"estimate.all",
"pval.all",
"fdr.all"
)
output <- output %>% arrange(pval.all)write.csv(
output,
file = "./NatComm_revision/DATASETS/gene_expression_results/results_single_cpg_lm_250kb_window_renamed.csv",
row.names = FALSE
)dmr <- read.csv(
"./NatComm_revision/DATASETS/gene_expression_results/results_regions_lm_250kb_window_renamed.csv"
)
pathDropbox <- file.path(dir("~", pattern = "Dropbox", full.names = TRUE))
dmr_meta <- read.csv(
file.path(pathDropbox,
"coMethDMR_metaAnalysis/",
"code_validation/Meta_analysis_code/meta_analysis_region_results",
"/step4_dmr_vs_cpgs/meta_analysis_sig_no_crossHyb_smoking_ov_comb_p_with_all.csv")
)[, c("ROSMAP_coMethRegion",
"GREAT_annotation",
"UCSC_RefGene_Group",
"UCSC_RefGene_Accession",
"UCSC_RefGene_Name",
"state")
]
dmr.annot <- merge(
dmr, dmr_meta,
by.x = "coMethDMR",
by.y = "ROSMAP_coMethRegion",
sort = FALSE
)
dmr.annot <- dmr.annot[
order(dmr.annot$pval.all), c(1:5, 17:20, 6, 21, 7: 14)
]write.csv(
dmr.annot,
"./NatComm_revision/DATASETS/gene_expression_results/results_regions_lm_250kb_window_renamed_with_annot.csv"
)cpg <- read.csv(
"./NatComm_revision/DATASETS/gene_expression_results/results_single_cpg_lm_250kb_window_renamed.csv"
)
cpg_meta <- read.csv(
file.path(pathDropbox,
"coMethDMR_metaAnalysis/",
"code_validation/Meta_analysis_code/meta_analysis_single_cpg_results/",
"/meta_analysis_single_cpg_sig_no_crossHyb_smoking_with_state_greatAnnot_df.csv")
)[, c("cpg",
"GREAT_annotation",
"UCSC_RefGene_Group",
"UCSC_RefGene_Accession",
"UCSC_RefGene_Name",
"Relation_to_Island",
"state",
"estimate",
"se",
"pVal.fixed",
"pVal.random",
"pValQ",
"direction",
"pVal.final",
"fdr")
]
cpg.annot <- merge(
cpg, cpg_meta,
by = "cpg",
sort = FALSE
)
cpg.annot <- cpg.annot %>% arrange(pval.all)write.csv(
cpg.annot,
"./NatComm_revision/DATASETS/gene_expression_results/results_single_cpg_lm_250kb_window_renamed_with_annot.csv"
)devtools::session_info()## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 4.0.2 (2020-06-22)
## os macOS Catalina 10.15.6
## system x86_64, darwin17.0
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz America/New_York
## date 2020-08-11
##
## ─ Packages ───────────────────────────────────────────────────────────────────
## ! package * version date lib
## abind 1.4-5 2016-07-21 [1]
## annotate 1.66.0 2020-04-28 [1]
## AnnotationDbi 1.50.3 2020-07-25 [1]
## AnnotationHub * 2.20.0 2020-04-27 [1]
## askpass 1.1 2019-01-13 [1]
## assertthat 0.2.1 2019-03-21 [1]
## backports 1.1.8 2020-06-17 [1]
## base64 2.0 2016-05-10 [1]
## base64enc 0.1-3 2015-07-28 [1]
## beanplot 1.2 2014-09-19 [1]
## Biobase * 2.48.0 2020-04-27 [1]
## BiocFileCache * 1.12.0 2020-04-27 [1]
## BiocGenerics * 0.34.0 2020-04-27 [1]
## BiocManager 1.30.10 2019-11-16 [1]
## BiocParallel 1.22.0 2020-04-27 [1]
## BiocVersion 3.11.1 2020-04-07 [1]
## biomaRt 2.44.1 2020-06-17 [1]
## Biostrings 2.56.0 2020-04-27 [1]
## bit 4.0.3 2020-07-30 [1]
## bit64 4.0.2 2020-07-30 [1]
## bitops 1.0-6 2013-08-17 [1]
## blob 1.2.1 2020-01-20 [1]
## boot 1.3-25 2020-04-26 [1]
## broom 0.7.0 2020-07-09 [1]
## bumphunter 1.30.0 2020-04-27 [1]
## callr 3.4.3 2020-03-28 [1]
## car 3.0-8 2020-05-21 [1]
## carData 3.0-4 2020-05-22 [1]
## cellranger 1.1.0 2016-07-27 [1]
## circlize 0.4.10 2020-06-15 [1]
## cli 2.0.2 2020-02-28 [1]
## clue 0.3-57 2019-02-25 [1]
## cluster 2.1.0 2019-06-19 [1]
## codetools 0.2-16 2018-12-24 [1]
## colorspace 1.4-1 2019-03-18 [1]
## coMethDMR * 0.0.0.9001 2020-07-23 [1]
## P coMethTF * 0.1.0 2020-08-06 [?]
## ComplexHeatmap * 2.4.3 2020-07-25 [1]
## crayon 1.3.4 2017-09-16 [1]
## curl 4.3 2019-12-02 [1]
## data.table 1.13.0 2020-07-24 [1]
## DBI 1.1.0 2019-12-15 [1]
## dbplyr * 1.4.4 2020-05-27 [1]
## DelayedArray * 0.14.1 2020-07-14 [1]
## DelayedMatrixStats * 1.10.1 2020-07-03 [1]
## desc 1.2.0 2018-05-01 [1]
## devtools 2.3.1 2020-07-21 [1]
## digest 0.6.25 2020-02-23 [1]
## doParallel * 1.0.15 2019-08-02 [1]
## doRNG 1.8.2 2020-01-27 [1]
## downloader 0.4 2015-07-09 [1]
## dplyr * 1.0.1 2020-07-31 [1]
## ellipsis 0.3.1 2020-05-15 [1]
## evaluate 0.14 2019-05-28 [1]
## ExperimentHub * 1.14.0 2020-04-27 [1]
## fansi 0.4.1 2020-01-08 [1]
## fastmap 1.0.1 2019-10-08 [1]
## forcats 0.5.0 2020-03-01 [1]
## foreach * 1.5.0 2020-03-30 [1]
## foreign 0.8-80 2020-05-24 [1]
## fs 1.5.0 2020-07-31 [1]
## genefilter 1.70.0 2020-04-27 [1]
## generics 0.0.2 2018-11-29 [1]
## GenomeInfoDb * 1.24.2 2020-06-15 [1]
## GenomeInfoDbData 1.2.3 2020-07-23 [1]
## GenomicAlignments 1.24.0 2020-04-27 [1]
## GenomicFeatures 1.40.1 2020-07-14 [1]
## GenomicRanges * 1.40.0 2020-04-27 [1]
## GEOquery 2.56.0 2020-04-27 [1]
## GetoptLong 1.0.2 2020-07-06 [1]
## ggplot2 * 3.3.2 2020-06-19 [1]
## ggpubr * 0.4.0 2020-06-27 [1]
## ggsignif 0.6.0 2019-08-08 [1]
## GlobalOptions 0.1.2 2020-06-10 [1]
## glue 1.4.1 2020-05-13 [1]
## gtable 0.3.0 2019-03-25 [1]
## haven 2.3.1 2020-06-01 [1]
## HDF5Array 1.16.1 2020-06-16 [1]
## hms 0.5.3 2020-01-08 [1]
## htmltools 0.5.0 2020-06-16 [1]
## httpuv 1.5.4 2020-06-06 [1]
## httr 1.4.2 2020-07-20 [1]
## IlluminaHumanMethylation450kanno.ilmn12.hg19 0.6.0 2020-07-23 [1]
## IlluminaHumanMethylationEPICanno.ilm10b2.hg19 0.6.0 2020-07-23 [1]
## illuminaio 0.30.0 2020-04-27 [1]
## interactiveDisplayBase 1.26.3 2020-06-02 [1]
## IRanges * 2.22.2 2020-05-21 [1]
## iterators * 1.0.12 2019-07-26 [1]
## jsonlite 1.7.0 2020-06-25 [1]
## knitr 1.29 2020-06-23 [1]
## later 1.1.0.1 2020-06-05 [1]
## lattice 0.20-41 2020-04-02 [1]
## lifecycle 0.2.0 2020-03-06 [1]
## limma 3.44.3 2020-06-12 [1]
## lme4 1.1-23 2020-04-07 [1]
## lmerTest 3.1-2 2020-04-08 [1]
## locfit 1.5-9.4 2020-03-25 [1]
## magrittr 1.5 2014-11-22 [1]
## MASS 7.3-51.6 2020-04-26 [1]
## Matrix 1.2-18 2019-11-27 [1]
## matrixStats * 0.56.0 2020-03-13 [1]
## mclust 5.4.6 2020-04-11 [1]
## memoise 1.1.0 2017-04-21 [1]
## mime 0.9 2020-02-04 [1]
## minfi 1.34.0 2020-04-27 [1]
## minqa 1.2.4 2014-10-09 [1]
## multtest 2.44.0 2020-04-27 [1]
## munsell 0.5.0 2018-06-12 [1]
## nlme 3.1-148 2020-05-24 [1]
## nloptr 1.2.2.2 2020-07-02 [1]
## nor1mix 1.3-0 2019-06-13 [1]
## numDeriv 2016.8-1.1 2019-06-06 [1]
## openssl 1.4.2 2020-06-27 [1]
## openxlsx 4.1.5 2020-05-06 [1]
## pillar 1.4.6 2020-07-10 [1]
## pkgbuild 1.1.0 2020-07-13 [1]
## pkgconfig 2.0.3 2019-09-22 [1]
## pkgload 1.1.0 2020-05-29 [1]
## plyr 1.8.6 2020-03-03 [1]
## png 0.1-7 2013-12-03 [1]
## preprocessCore 1.50.0 2020-04-27 [1]
## prettyunits 1.1.1 2020-01-24 [1]
## processx 3.4.3 2020-07-05 [1]
## progress 1.2.2 2019-05-16 [1]
## promises 1.1.1 2020-06-09 [1]
## ps 1.3.3 2020-05-08 [1]
## pscl 1.5.5 2020-03-07 [1]
## purrr 0.3.4 2020-04-17 [1]
## quadprog 1.5-8 2019-11-20 [1]
## R.methodsS3 1.8.0 2020-02-14 [1]
## R.oo 1.23.0 2019-11-03 [1]
## R.utils 2.9.2 2019-12-08 [1]
## R6 2.4.1 2019-11-12 [1]
## rappdirs 0.3.1 2016-03-28 [1]
## RColorBrewer 1.1-2 2014-12-07 [1]
## Rcpp 1.0.5 2020-07-06 [1]
## RCurl 1.98-1.2 2020-04-18 [1]
## readr 1.3.1 2018-12-21 [1]
## readxl 1.3.1 2019-03-13 [1]
## remotes 2.2.0 2020-07-21 [1]
## reshape 0.8.8 2018-10-23 [1]
## reshape2 1.4.4 2020-04-09 [1]
## rhdf5 2.32.2 2020-07-03 [1]
## Rhdf5lib 1.10.1 2020-07-09 [1]
## rio 0.5.16 2018-11-26 [1]
## rjson 0.2.20 2018-06-08 [1]
## rlang 0.4.7 2020-07-09 [1]
## rmarkdown 2.3 2020-06-18 [1]
## rngtools 1.5 2020-01-23 [1]
## rprojroot 1.3-2 2018-01-03 [1]
## Rsamtools 2.4.0 2020-04-27 [1]
## RSQLite 2.2.0 2020-01-07 [1]
## rstatix 0.6.0 2020-06-18 [1]
## rstudioapi 0.11 2020-02-07 [1]
## rtracklayer 1.48.0 2020-07-14 [1]
## rvest 0.3.6 2020-07-25 [1]
## S4Vectors * 0.26.1 2020-05-16 [1]
## scales 1.1.1 2020-05-11 [1]
## scrime 1.3.5 2018-12-01 [1]
## sesameData * 1.6.0 2020-05-07 [1]
## sessioninfo 1.1.1 2018-11-05 [1]
## sfsmisc 1.1-7 2020-05-07 [1]
## shape 1.4.4 2018-02-07 [1]
## shiny 1.5.0 2020-06-23 [1]
## siggenes 1.62.0 2020-04-27 [1]
## statmod 1.4.34 2020-02-17 [1]
## stringi 1.4.6 2020-02-17 [1]
## stringr 1.4.0 2019-02-10 [1]
## SummarizedExperiment * 1.18.2 2020-07-14 [1]
## survival 3.2-3 2020-06-13 [1]
## TCGAbiolinks * 2.16.3 2020-07-15 [1]
## testthat * 2.3.2 2020-03-02 [1]
## tibble 3.0.3 2020-07-10 [1]
## tidyr * 1.1.1 2020-07-31 [1]
## tidyselect 1.1.0 2020-05-11 [1]
## usethis 1.6.1 2020-04-29 [1]
## vctrs 0.3.2 2020-07-15 [1]
## withr 2.2.0 2020-04-20 [1]
## xfun 0.16 2020-07-24 [1]
## XML 3.99-0.5 2020-07-23 [1]
## xml2 1.3.2 2020-04-23 [1]
## xtable 1.8-4 2019-04-21 [1]
## XVector 0.28.0 2020-04-27 [1]
## yaml 2.2.1 2020-02-01 [1]
## zip 2.0.4 2019-09-01 [1]
## zlibbioc 1.34.0 2020-04-27 [1]
## source
## CRAN (R 4.0.2)
## Bioconductor
## Bioconductor
## Bioconductor
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## Bioconductor
## Bioconductor
## Bioconductor
## CRAN (R 4.0.2)
## Bioconductor
## Bioconductor
## Bioconductor
## Bioconductor
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## Bioconductor
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
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## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## Bioconductor
## local
## Bioconductor
## CRAN (R 4.0.2)
## CRAN (R 4.0.1)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## Bioconductor
## Bioconductor
## CRAN (R 4.0.2)
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## CRAN (R 4.0.2)
## CRAN (R 4.0.1)
## Bioconductor
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## Bioconductor
## CRAN (R 4.0.2)
## Bioconductor
## Bioconductor
## Bioconductor
## Bioconductor
## Bioconductor
## Bioconductor
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## Bioconductor
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## Bioconductor
## Bioconductor
## Bioconductor
## Bioconductor
## Bioconductor
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## Bioconductor
## CRAN (R 4.0.1)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## Bioconductor
## CRAN (R 4.0.2)
## Bioconductor
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.1)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## Bioconductor
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## Bioconductor
## Bioconductor
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## Bioconductor
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## Bioconductor
## CRAN (R 4.0.2)
## Bioconductor
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## Bioconductor
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## Bioconductor
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## Bioconductor
## CRAN (R 4.0.2)
## Bioconductor
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## Bioconductor
## CRAN (R 4.0.2)
## CRAN (R 4.0.2)
## Bioconductor
##
## [1] /Library/Frameworks/R.framework/Versions/4.0/Resources/library
##
## P ── Loaded and on-disk path mismatch.