This vignette shows a complete workflow of the TCGAbiolinks package. The code is divided in 4 case study:
library(SummarizedExperiment)
library(TCGAbiolinks)
<- GDCquery(
query.exp project = "TCGA-BRCA",
data.category = "Transcriptome Profiling",
data.type = "Gene Expression Quantification",
workflow.type = "STAR - Counts",
sample.type = c("Primary Tumor","Solid Tissue Normal")
)GDCdownload(
query = query.exp,
files.per.chunk = 100
)
<- GDCprepare(
brca.exp query = query.exp,
save = TRUE,
save.filename = "brcaExp.rda"
)
# get subtype information
<- TCGAquery_subtype(tumor = "BRCA")
infomation.subtype
# get clinical data
<- GDCquery_clinic(project = "TCGA-BRCA",type = "clinical")
information.clinical
# Which samples are Primary Tumor
<- brca.exp$barcode[brca.exp$shortLetterCode == "TP"]
samples.primary.tumour
# which samples are solid tissue normal
<- brca.exp$barcode[brca.exp$shortLetterCode == "NT"] samples.solid.tissue.normal
Using TCGAnalyze_DEA
, we identified 4,815 differentially
expression genes (DEG) (log fold change >=1 and FDR < 1%) between
113 normal and 1106 BRCA samples. In order to understand the underlying
biological process from DEGs we performed an enrichment analysis using
TCGAnalyze_EA_complete
function.
<- TCGAanalyze_Preprocessing(
dataPrep object = brca.exp,
cor.cut = 0.6
)
<- TCGAanalyze_Normalization(
dataNorm tabDF = dataPrep,
geneInfo = geneInfoHT,
method = "gcContent"
)
<- TCGAanalyze_Filtering(
dataFilt tabDF = dataNorm,
method = "quantile",
qnt.cut = 0.25
)
<- TCGAanalyze_DEA(
dataDEGs mat1 = dataFilt[,samples.solid.tissue.normal],
mat2 = dataFilt[,samples.primary.tumour],
Cond1type = "Normal",
Cond2type = "Tumor",
fdr.cut = 0.01 ,
logFC.cut = 2,
method = "glmLRT",
pipeline = "edgeR"
)
TCGAbiolinks outputs bar chart with the number of genes for the main categories of three ontologies (GO:biological process, GO:cellular component, and GO:molecular function, respectively).
<- TCGAanalyze_EAcomplete(
ansEA TFname = "DEA genes Normal Vs Tumor",
RegulonList = dataDEGs$gene_name
)
TCGAvisualize_EAbarplot(
tf = rownames(ansEA$ResBP),
GOBPTab = ansEA$ResBP,
GOCCTab = ansEA$ResCC,
GOMFTab = ansEA$ResMF,
PathTab = ansEA$ResPat,
nRGTab = dataDEGs$gene_name,
nBar = 10
)
The figure resulted from the code above is shown below.
The Kaplan-Meier analysis was used to compute survival univariate
curves, and
log-Ratio test was computed to assess the statistical significance by
using TCGAanalyze_SurvivalKM function; starting with 3,390 DEGs genes we
found 555 significantly genes with p.value <0.05.
<- TCGAquery_SampleTypes(colnames(dataFilt), typesample = c("NT"))
group1 <- TCGAquery_SampleTypes(colnames(dataFilt), typesample = c("TP"))
group2
<- TCGAanalyze_SurvivalKM(
dataSurv clinical_patient = dataClin,
dataGE = dataFilt,
Genelist = rownames(dataDEGs),
Survresult = FALSE,
ThreshTop = 0.67,
ThreshDown = 0.33,
p.cut = 0.05,
group1 = group1,
group2 = group2
)
Cox-regression analysis was used to compute survival multivariate curves, and cox p-value was computed to assess the statistical significance by using TCGAnalyze_SurvivalCoxNET function. Survival multivariate analysis found 160 significantly genes according to the cox p-value FDR 5.00e-02. From DEGs that we found to correlate significantly with survival by both univariate and multivariate analyses we analyzed the following network.
The interactome network graph was generated using STRING.,org.Hs.string version 10 (Human functional protein association network). The network graph was resized by dnet package considering only multivariate survival genes, with strong interaction (threshold = 700) we obtained a subgraphsub graph of 24 nodes and 31 edges.
require(dnet) # to change
<- dRDataLoader(RData = "org.Hs.string")
org.Hs.string
<- TCGAvisualize_SurvivalCoxNET(
TabCoxNet
dataClin,
dataFilt, Genelist = rownames(dataSurv),
scoreConfidence = 700,
org.Hs.string = org.Hs.string,
titlePlot = "Case Study n.1 dnet"
)
The figure resulted from the code above is shown below.
We focused on the analysis of LGG samples. In particular, we used TCGAbiolinks to download 293 samples with molecular subtypes. Link the complete complete code. .
library(TCGAbiolinks)
library(SummarizedExperiment)
<- GDCquery(
query.exp project = "TCGA-LGG",
data.category = "Transcriptome Profiling",
data.type = "Gene Expression Quantification",
workflow.type = "STAR - Counts",
sample.type = c("Primary Tumor")
)
GDCdownload(query.exp)
<- GDCprepare(
lgg.exp query = query.exp,
save = FALSE
)
First, we searched for possible outliers using the
TCGAanalyze_Preprocessing
function, which performs an Array
Array Intensity correlation AAIC. We used all samples in expression data
which contain molecular subtypes, filtering out samples without
molecular information, and using only IDHmut-codel (n=85),
IDHmut-non-codel (n=141) and IDHwt (n=56), NA (11), to define a square
symmetric matrix of pearson correlation among all samples (n=293).
According to this matrix we found no samples with low correlation
(cor.cut = 0.6) that can be identified as possible outliers, so we
continued our analysis with 70 samples.
Second, using the TCGAanalyze_Normalization
function we
normalized mRNA transcripts and miRNA, using EDASeq package. This
function does use Within-lane normalization procedures to adjust for
GC-content effect (or other gene-level effects) on read counts: loess
robust local regression, global-scaling, and full-quantile normalization
(Risso et al. 2011) and between-lane
normalization procedures to adjust for distributional differences
between lanes (e.g., sequencing depth): global-scaling and full-quantile
normalization (Bullard et al. 2010).
Third, using the TCGAanalyze_Filtering
function we
applied 3 filters removing features / mRNAs with low signal across
samples obtaining 4578, 4284, 1187 mRNAs respectively.
Then we applied two Hierarchical cluster analysis on 1187 mRNAs after the three filters described above, the first cluster using as method ward.D2, and the second with ConsensusClusterPlus.
After the two clustering analysis, with cut.tree = 4 we obtained n= 4 expression clusters (EC).
library(dplyr)
<- TCGAanalyze_Preprocessing(
dataPrep object = lgg.exp,
cor.cut = 0.6
)<- TCGAanalyze_Normalization(
dataNorm tabDF = dataPrep,
geneInfo = geneInfoHT,
method = "gcContent"
)
<- dataNorm %>%
datFilt TCGAanalyze_Filtering(method = "varFilter") %>%
TCGAanalyze_Filtering(method = "filter1") %>%
TCGAanalyze_Filtering(method = "filter2",foldChange = 1)
<- TCGAanalyze_Clustering(
data_Hc2 tabDF = datFilt,
method = "consensus",
methodHC = "ward.D2"
) # Add cluster information to Summarized Experiment
colData(lgg.exp)$groupsHC <- paste0("EC",data_Hc2[[4]]$consensusClass)
The next steps will be to visualize the data. First, we created the survival plot.
TCGAanalyze_survival(
data = colData(lgg.exp),
clusterCol = "groupsHC",
main = "TCGA kaplan meier survival plot from consensus cluster",
legend = "RNA Group",
height = 10,
risk.table = T,
conf.int = F,
color = c("black","red","blue","green3"),
filename = "survival_lgg_expression_subtypes.png"
)
The result is showed below:
We will also, create a heatmap of the expression.
TCGAvisualize_Heatmap(
data = t(datFilt),
col.metadata = colData(lgg.exp)[,
c("barcode",
"groupsHC",
"paper_Histology",
"paper_IDH.codel.subtype")
],col.colors = list(
groupsHC = c(
"EC1"="black",
"EC2"="red",
"EC3"="blue",
"EC4"="green3")
),sortCol = "groupsHC",
type = "expression", # sets default color
scale = "row", # use z-scores for better visualization. Center gene expression level around 0.
title = "Heatmap from concensus cluster",
filename = "case2_Heatmap.png",
extremes = seq(-2,2,1),
color.levels = colorRampPalette(c("green", "black", "red"))(n = 5),
cluster_rows = TRUE,
cluster_columns = FALSE,
width = 1000,
height = 500
)
The result is shown below:
Finally, we will take a look in the mutation genes. We will first
download the MAF file with
GDCquery
,GDCdownload
and
GDCprepare
. In this example we will investigate the gene
“ATR”.
library(maftools)
library(dplyr)
<- GDCquery(
query project = "TCGA-LGG",
data.category = "Simple Nucleotide Variation",
access = "open",
data.type = "Masked Somatic Mutation",
workflow.type = "Aliquot Ensemble Somatic Variant Merging and Masking"
)GDCdownload(query)
<- GDCprepare(query)
LGGmut # Selecting gene
<- LGGmut %>% dplyr::filter(Hugo_Symbol == "ATR")
LGGmut.atr
<- LGGmut.atr[!duplicated(LGGmut.atr$Tumor_Sample_Barcode),]
dataMut $Tumor_Sample_Barcode <- substr(dataMut$Tumor_Sample_Barcode,1,12)
dataMut
# Adding the Expression Cluster classification found before
<- merge(dataMut, cluster, by.y = "patient", by.x = "Tumor_Sample_Barcode") dataMut
In recent years, it has been described the relationship between DNA methylation and gene expression and the study of this relationship is often difficult to accomplish.
This case study will show the steps to investigate the relationship between the two types of data.
First, we downloaded ACC DNA methylation data for HumanMethylation450k platforms, and ACC RNA expression data for Illumina HiSeq platform.
TCGAbiolinks adds by default the subtypes classification already published by researchers.
We will use this classification to do our examples. So, selected the groups CIMP-low and CIMP-high to do RNA expression and DNA methylation comparison.
library(TCGAbiolinks)
library(SummarizedExperiment)
#-----------------------------------
# STEP 1: Search, download, prepare |
#-----------------------------------
# 1.1 - DNA methylation
# ----------------------------------
<- GDCquery(
query.met project = "TCGA-ACC",
data.category = "DNA Methylation",
data.type = "Methylation Beta Value",
platform = "Illumina Human Methylation 450"
)
GDCdownload(
query = query.met,
files.per.chunk = 20,
directory = "case3/GDCdata"
)
<- GDCprepare(
acc.met query = query.met,
save = FALSE,
directory = "case3/GDCdata"
)
#-----------------------------------
# 1.2 - RNA expression
# ----------------------------------
<- GDCquery(
query.exp project = "TCGA-ACC",
data.category = "Transcriptome Profiling",
data.type = "Gene Expression Quantification",
workflow.type = "STAR - Counts"
)
GDCdownload(
query = query.exp,
files.per.chunk = 20,
directory = "case3/GDCdata"
)
<- GDCprepare(
acc.exp query = query.exp,
save = FALSE,
directory = "case3/GDCdata"
)
For DNA methylation, we perform a DMC (different methylated CpGs) analysis, which will give the difference of DNA methylation for the probes of the groups and their significance value. The output can be seen in a volcano plot. Note: Depending on the number of samples this function can be very slow due to the wilcoxon test, taking from hours to days.
# na.omit
<- acc.met[rowSums(is.na(assay(acc.met))) == 0,]
acc.met
# Volcano plot
<- TCGAanalyze_DMC(
acc.met data = acc.met,
groupCol = "subtype_MethyLevel",
group1 = "CIMP-high",
group2="CIMP-low",
p.cut = 10^-5,
diffmean.cut = 0.25,
legend = "State",
plot.filename = "case3/CIMP-highvsCIMP-low_metvolcano.png"
)
The figure resulted from the code above is shown below:
For the expression analysis, we do a DEA (differential expression analysis) which will give the fold change of gene expression and their significance value.
#-------------------------------------------------
# 2.3 - DEA - Expression analysis - volcano plot
# ------------------------------------------------
<- subset(
acc.exp.aux
acc.exp, select = colData(acc.exp)$subtype_MethyLevel %in% c("CIMP-high","CIMP-low")
)
<- colData(acc.exp.aux)$subtype_MethyLevel %in% c("CIMP-high")
idx <- colData(acc.exp.aux)$subtype_MethyLevel %in% c("CIMP-low")
idx2
<- TCGAanalyze_Preprocessing(
dataPrep object = acc.exp.aux,
cor.cut = 0.6
)
<- TCGAanalyze_Normalization(
dataNorm tabDF = dataPrep,
geneInfo = geneInfoHT,
method = "gcContent"
)
<- TCGAanalyze_Filtering(
dataFilt tabDF = dataNorm,
qnt.cut = 0.25,
method = 'quantile'
)
<- TCGAanalyze_DEA(
dataDEGs mat1 = dataFilt[,idx],
mat2 = dataFilt[,idx2],
Cond1type = "CIMP-high",
Cond2type = "CIMP-low",
method = "glmLRT"
)
TCGAVisualize_volcano(
x = dataDEGs$logFC,
y = dataDEGs$FDR,
filename = "case3/Case3_volcanoexp.png",
x.cut = 3,
y.cut = 10^-5,
names = rownames(dataDEGs),
color = c("black","red","darkgreen"),
names.size = 2,
xlab = " Gene expression fold change (Log2)",
legend = "State",
title = "Volcano plot (CIMP-high vs CIMP-low)",
width = 10
)
The figure resulted from the code above is shown below:
Finally, using both previous analysis we do a starburst plot to select the genes that are Candidate Biologically Significant.
Observation: over the time, the number of samples has increased and the clinical data updated. We used only the samples that had a classification in the examples.
#------------------------------------------
# 2.4 - Starburst plot
# -----------------------------------------
# If true the argument names of the genes in circle
# (biologically significant genes, has a change in gene
# expression and DNA methylation and respects all the thresholds)
# will be shown
# these genes are returned by the function see starburst object after the function is executed
<- TCGAvisualize_starburst(
starburst met = acc.met,
exp = dataDEGs,
genome = "hg19"
group1 = "CIMP-high",
group2 = "CIMP-low",
filename = "case3/starburst.png",
met.platform = "450K",
met.p.cut = 10^-5,
exp.p.cut = 10^-5,
diffmean.cut = 0.25,
logFC.cut = 3,
names = FALSE,
height = 10,
width = 15,
dpi = 300
)
The figure resulted from the code above is shown below:
An example of package to perform DNA methylation and RNA expression analysis is ELMER Lijing Yao, Berman, and Farnham (2015). ELMER, which is designed to combine DNA methylation and gene expression data from human tissues to infer multi-level cis-regulatory networks. ELMER uses DNA methylation to identify distal probes, and correlates them with the expression of nearby genes to identify one or more transcriptional targets. Transcription factor (TF) binding site analysis of those anti-correlated distal probes is coupled with expression analysis of all TFs to infer upstream regulators. This package can be easily applied to TCGA public available cancer data sets and custom DNA methylation and gene expression data sets.
ELMER analyses have the following steps:
We will present this the study KIRC by TCGAbiolinks and ELMER integration.
For more information, please consult the ELMER package:
And the following articles:
For the DNA methylation data we will search the platform HumanMethylation450. After, we will download the data and prepared into a SummarizedExperiment object.
library(TCGAbiolinks)
library(SummarizedExperiment)
library(ELMER)
library(parallel)
dir.create("case4")
setwd("case4")
#-----------------------------------
# STEP 1: Search, download, prepare |
#-----------------------------------
# 1.1 - DNA methylation
# ----------------------------------
<- GDCquery(
query.met project = "TCGA-KIRC",
data.category = "DNA Methylation",
data.type = "Methylation Beta Value",
platform = "Illumina Human Methylation 450"
)GDCdownload(query.met)
<- GDCprepare(
kirc.met query = query.met,
save = TRUE,
save.filename = "kircDNAmet.rda",
summarizedExperiment = TRUE
)
For gene expression we will use Gene Expression Quantification.
# Step 1.2 download expression data
#-----------------------------------
# 1.2 - RNA expression
# ----------------------------------
<- GDCquery(
query.exp project = "TCGA-KIRC",
data.category = "Transcriptome Profiling",
data.type = "Gene Expression Quantification",
workflow.type = "STAR - Counts"
)GDCdownload(query.exp,files.per.chunk = 20)
<- GDCprepare(
kirc.exp query = query.exp,
save = TRUE,
save.filename = "kircExp.rda"
)
A MultiAssayExperiment object from the r BiocStyle::Biocpkg(“MultiAssayExperiment”) package is the input for multiple main functions of r BiocStyle::Biocpkg(“ELMER”).
We will first need to get distal probes (2 KB away from TSS).
<- get.feature.probe(genome = "hg38", met.platform = "450K") distal.probes
To create it you can use the createMAE function. This function will keep only samples that have both DNA methylation and gene expression.
library(MultiAssayExperiment)
<- createMAE(
mae exp = kirc.exp,
met = kirc.met,
save = FALSE,
linearize.exp = TRUE,
filter.probes = distal.probes,
save.filename = "mae_kirc.rda",
met.platform = "450K",
genome = "hg38",
TCGA = TRUE
)# Remove FFPE samples
<- mae[,!mae$is_ffpe] mae
We will execute ELMER to identify probes that are hypomethylated in tumor samples compared to the normal samples.
<- "definition"
group.col <- "Primary Tumor"
group1 <- "Solid Tissue Normal"
group2 <- "hypo"
direction <- file.path("kirc",direction)
dir.out dir.create(dir.out, recursive = TRUE)
#--------------------------------------
# STEP 3: Analysis |
#--------------------------------------
# Step 3.1: Get diff methylated probes |
#--------------------------------------
<- get.diff.meth(
sig.diff data = mae,
group.col = group.col,
group1 = group1,
group2 = group2,
minSubgroupFrac = 0.2,
sig.dif = 0.3,
diff.dir = direction, # Search for hypomethylated probes in group 1
cores = 1,
dir.out = dir.out,
pvalue = 0.01
)
#-------------------------------------------------------------
# Step 3.2: Identify significant probe-gene pairs |
#-------------------------------------------------------------
# Collect nearby 20 genes for Sig.probes
<- GetNearGenes(
nearGenes data = mae,
probes = sig.diff$probe,
numFlankingGenes = 20, # 10 upstream and 10 dowstream genes
cores = 1
)
<- get.pair(
pair data = mae,
group.col = group.col,
group1 = group1,
group2 = group2,
nearGenes = nearGenes,
minSubgroupFrac = 0.4, # % of samples to use in to create groups U/M
permu.dir = file.path(dir.out,"permu"),
permu.size = 100, # Please set to 100000 to get significant results
raw.pvalue = 0.05,
Pe = 0.01, # Please set to 0.001 to get significant results
filter.probes = TRUE, # See preAssociationProbeFiltering function
filter.percentage = 0.05,
filter.portion = 0.3,
dir.out = dir.out,
cores = 1,
label = direction
)
# Identify enriched motif for significantly hypomethylated probes which
# have putative target genes.
<- get.enriched.motif(
enriched.motif data = mae,
probes = pair$Probe,
dir.out = dir.out,
label = direction,
min.incidence = 10,
lower.OR = 1.1
)
<- get.TFs(
TF data = mae,
group.col = group.col,
group1 = group1,
group2 = group2,
minSubgroupFrac = 0.4,
enriched.motif = enriched.motif,
dir.out = dir.out,
cores = 1,
label = direction
)
From this analysis it is possible to verify the relationship between nearby 20 gene expression vs DNA methylation at this probe. The result of this is show by the ELMER scatter plot.
scatter.plot(
data = mae,
byProbe = list(probe = sig.diff$probe[1], numFlankingGenes = 20),
category = "definition",
dir.out = "plots",
lm = TRUE, # Draw linear regression curve
save = TRUE
)
The result is shown below:
Each scatter plot showing the average DNA methylation level of sites with the UA6 motif in all KIRC samples plotted against the expression of the transcription factor ZNF677 and PEG3 respectively.
scatter.plot(
data = mae,
byTF = list(
TF = c("RUNX1","RUNX2","RUNX3"),
probe = enriched.motif[[names(enriched.motif)[10]]]
), category = "definition",
dir.out = "plots",
save = TRUE,
lm_line = TRUE
)
The result is shown below:
You cen see the anticorrelated pairs of gene and probes by drawing a heatmap.
heatmapPairs(
data = mae,
group.col = "definition",
group1 = "Primary Tumor",
annotation.col = c("gender"),
group2 = "Solid Tissue Normal",
pairs = pair,
filename = "heatmap.pdf"
)
The result is shown below:
The plot shows the odds ratio (x axis) for the selected motifs with lower boundary of OR above 1.8. The range shows the 95% confidence interval for each Odds Ratio.
sessionInfo()
## R version 4.2.1 Patched (2022-07-09 r82577)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur ... 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
##
## locale:
## [1] C/en_US.UTF-8/en_US.UTF-8/C/en_GB/en_US.UTF-8
##
## attached base packages:
## [1] grid stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] jpeg_0.1-9 png_0.1-7
## [3] DT_0.26 dplyr_1.0.10
## [5] SummarizedExperiment_1.28.0 Biobase_2.58.0
## [7] GenomicRanges_1.50.0 GenomeInfoDb_1.34.0
## [9] IRanges_2.32.0 S4Vectors_0.36.0
## [11] BiocGenerics_0.44.0 MatrixGenerics_1.10.0
## [13] matrixStats_0.62.0 TCGAbiolinks_2.26.0
## [15] testthat_3.1.5
##
## loaded via a namespace (and not attached):
## [1] colorspace_2.0-3 ellipsis_0.3.2
## [3] rprojroot_2.0.3 XVector_0.38.0
## [5] fs_1.5.2 rstudioapi_0.14
## [7] remotes_2.4.2 bit64_4.0.5
## [9] AnnotationDbi_1.60.0 fansi_1.0.3
## [11] xml2_1.3.3 R.methodsS3_1.8.2
## [13] cachem_1.0.6 knitr_1.40
## [15] pkgload_1.3.1 jsonlite_1.8.3
## [17] dbplyr_2.2.1 R.oo_1.25.0
## [19] shiny_1.7.3 readr_2.1.3
## [21] compiler_4.2.1 httr_1.4.4
## [23] assertthat_0.2.1 Matrix_1.5-1
## [25] fastmap_1.1.0 cli_3.4.1
## [27] later_1.3.0 htmltools_0.5.3
## [29] prettyunits_1.1.1 tools_4.2.1
## [31] gtable_0.3.1 glue_1.6.2
## [33] GenomeInfoDbData_1.2.9 rappdirs_0.3.3
## [35] Rcpp_1.0.9 jquerylib_0.1.4
## [37] vctrs_0.5.0 Biostrings_2.66.0
## [39] xfun_0.34 stringr_1.4.1
## [41] brio_1.1.3 ps_1.7.2
## [43] rvest_1.0.3 mime_0.12
## [45] miniUI_0.1.1.1 lifecycle_1.0.3
## [47] devtools_2.4.5 XML_3.99-0.12
## [49] zlibbioc_1.44.0 scales_1.2.1
## [51] hms_1.1.2 promises_1.2.0.1
## [53] yaml_2.3.6 curl_4.3.3
## [55] memoise_2.0.1 ggplot2_3.3.6
## [57] downloader_0.4 sass_0.4.2
## [59] biomaRt_2.54.0 stringi_1.7.8
## [61] RSQLite_2.2.18 highr_0.9
## [63] desc_1.4.2 filelock_1.0.2
## [65] pkgbuild_1.3.1 rlang_1.0.6
## [67] pkgconfig_2.0.3 bitops_1.0-7
## [69] evaluate_0.17 TCGAbiolinksGUI.data_1.17.0
## [71] lattice_0.20-45 purrr_0.3.5
## [73] htmlwidgets_1.5.4 bit_4.0.4
## [75] processx_3.8.0 tidyselect_1.2.0
## [77] plyr_1.8.7 magrittr_2.0.3
## [79] R6_2.5.1 generics_0.1.3
## [81] profvis_0.3.7 DelayedArray_0.24.0
## [83] DBI_1.1.3 pillar_1.8.1
## [85] withr_2.5.0 KEGGREST_1.38.0
## [87] RCurl_1.98-1.9 tibble_3.1.8
## [89] crayon_1.5.2 utf8_1.2.2
## [91] BiocFileCache_2.6.0 tzdb_0.3.0
## [93] rmarkdown_2.17 urlchecker_1.0.1
## [95] progress_1.2.2 usethis_2.1.6
## [97] data.table_1.14.4 blob_1.2.3
## [99] callr_3.7.2 digest_0.6.30
## [101] xtable_1.8-4 tidyr_1.2.1
## [103] httpuv_1.6.6 R.utils_2.12.1
## [105] munsell_0.5.0 bslib_0.4.0
## [107] sessioninfo_1.2.2