Analyzing cell-free DNA methylation data with cfTools (original) (raw)
Introduction
Given the methylation sequencing data of a cell-free DNA (cfDNA) sample, for each cancer marker or tissue marker, we deconvolve the tumor-derived or tissue-specific reads from all reads falling in the marker region. Our read-based deconvolution algorithm exploits the pervasiveness of DNA methylation for signal enhancement, therefore can sensitively identify a trace amount of tumor-specific or tissue-specific cfDNA in plasma.
Specifically, cfTools can deconvolve different sources of cfDNA fragments (or reads) in two contexts:
- Cancer detection [1]: separate cfDNA fragments into tumor-derived fragments and background normal fragments (2 classes), and estimate the tumor-derived cfDNA fraction \(\theta\) (\(0\leq \theta < 1\)).
- Tissue deconvolution [2,3]: separate cfDNA fragments from different tissues (> 2 classes), and estimate the cfDNA fraction \(\theta_t\)(\(0\leq \theta_t < 1\)) of different tissue types (including an unknown type)\(t\) for a plasma cfDNA sample.
These functions can serve as foundations for more advanced cfDNA-based studies, including cancer diagnosis and disease monitoring.
Installation
cfTools is an R package available via theBioconductor repository for packages. You can install the release version by using the following commands in your R session:
if (!requireNamespace("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
BiocManager::install("cfTools")Alternatively, you can install the development version fromGitHub :
BiocManager::install("jasminezhoulab/cfTools")Input data preparation
The two main input files for CancerDetector() and cfDeconvolve() are
- Input 1: fragment-level methylation states (methState), which are represented by a sequence of binary values (0 represents unmethylated CpG and 1 represents methylated CpG on the same fragment);
- Input 2: methylation pattern (paired shape parameters of beta distributions) of markers.
cfSort() mainly takes Input 1 as the only input file.
Section 3.1, 3.2, 3.3 provide an example for generating Input 1. We require users to provide pre-processed paired-end bisulfite sequencing reads (i.e., aligned to the reference genome). For each cfDNA sample, users need to prepare (1) the standard (sorted) BED file of the aligned reads and (2) the methylation information that bismark extracts from the aligned reads as input data files. Specifically,
- Section 3.1
MergePEReads()- Input: a standard (sorted) BED file of paired-end sequencing reads
- Output: fragment-level information of cfDNA reads
- Section 3.2
MergeCpGs()- Input: a
CpG_OT*file and aCpG_OB*file generated by_bismark methylation extractor_ - Output: methylation information of all CpGs on the same fragment
- Input: a
- Section 3.3
GenerateFragMeth()- Input: the output lists of
MergePEReads()andMergeCpGs() - Output: fragment-level information about methylation states
- Input: the output lists of
Section 3.4 provides an example for generating Input 2. Specifically,
- Section 3.4
GenerateMarkerParam()- Input: a list of methylation levels (e.g., beta values) for markers, a vector of sample types (e.g., tumor or normal, tissue types) corresponding to the rows of the list, a vector of marker names corresponding to the columns of the list
- Output: a list containing the paired shape parameters of beta distributions for markers
Example input data files are included within the package:
library(cfTools)
library(utils)
demo.dir <- system.file("data", package="cfTools")Merge paired-end sequencing reads to fragment-level
Function MergePEReads() generates fragment-level information for paired-end sequencing reads. The main input file is a standard (sorted) BED file (e.g. output of bedtools bamtobed) of paired-end sequencing reads containing columns of chromosome name, chromosome start, chromosome end, sequence ID, mapping quality score, and strand.
PEReads <- file.path(demo.dir, "demo.sorted.bed.txt.gz")
head(read.table(PEReads, header=FALSE), 2)
#> V1 V2 V3
#> 1 chr21 9417768 9417888
#> 2 chr21 9418101 9418223
#> V4
#> 1 GWNJ-0965:805:GW2108234048th:1:2211:31223:60642_1:N:0:ATTCCT:R1:CAAGTCCC:R2:ACATTATG:F1:TGACT:F2:AGACT/2
#> 2 GWNJ-0965:805:GW2108234048th:1:2211:31223:60642_1:N:0:ATTCCT:R1:CAAGTCCC:R2:ACATTATG:F1:TGACT:F2:AGACT/1
#> V5 V6
#> 1 0 +
#> 2 0 -The output is a list in BED file format and/or written to an output BED file, where each line contains the information of a cfDNA fragment.
fragInfo <- MergePEReads(PEReads)
#> + /home/biocbuild/.cache/R/basilisk/1.20.0/0/bin/conda create --yes --prefix /home/biocbuild/.cache/R/basilisk/1.20.0/cfTools/1.8.0/new_env 'python=3.8' --quiet -c conda-forge --override-channels
#> + /home/biocbuild/.cache/R/basilisk/1.20.0/0/bin/conda install --yes --prefix /home/biocbuild/.cache/R/basilisk/1.20.0/cfTools/1.8.0/new_env 'python=3.8' -c conda-forge --override-channels
#> + /home/biocbuild/.cache/R/basilisk/1.20.0/0/bin/conda install --yes --prefix /home/biocbuild/.cache/R/basilisk/1.20.0/cfTools/1.8.0/new_env -c conda-forge 'python=3.8' 'python=3.8' 'numpy=1.23.4' 'scipy=1.8.0' 'tensorflow=2.10.0' --override-channels
head(fragInfo, 2)
#> chr start end fragmentLength strand
#> 1 chr21 9439599 9439741 142 -
#> 2 chr21 9483455 9483622 167 -
#> qname
#> 1 GWNJ-0965:805:GW2108234048th:1:1101:11972:44486_1:N:0:ATTCCT:R1:TTACGGCG:R2:CAGTGGAG:F1:TGACT:F2:TGACT
#> 2 GWNJ-0965:805:GW2108234048th:1:1101:20933:67955_1:N:0:ATTCCT:R1:GGGACTTT:R2:CGCGAGTA:F1:TGACT:F2:TGACTMerge methylation states of CpGs on two strands to fragment-level
Function MergeCpGs() generates fragment-level methylation states of CpGs. The main inputs of it are two output files of bismark methylation extractor, which is a program performing methylation calling on bisulfite treated sequencing reads. The CpG_OT* file contains methylation information for CpGs on the original top strand (OT); the CpG_OB* file contains methylation information for CpGs on the original bottom strand (OB). Both files contain columns of sequence ID, methylation state, chromosome name, chromosome start, methylation call.
CpG_OT <- file.path(demo.dir, "CpG_OT_demo.txt.gz")
CpG_OB <- file.path(demo.dir, "CpG_OB_demo.txt.gz")
head(read.table(CpG_OT, header=FALSE), 2)
#> V1
#> 1 GWNJ-0965:805:GW2108234048th:2:2108:26250:45136_1:N:0:ATTCCT:R1:CTTTCCAA:R2:GCTTCGAG:F1:TGACT:F2:AGACG
#> 2 GWNJ-0965:805:GW2108234048th:2:2108:26250:45136_1:N:0:ATTCCT:R1:CTTTCCAA:R2:GCTTCGAG:F1:TGACT:F2:AGACG
#> V2 V3 V4 V5
#> 1 + chr21 9438974 Z
#> 2 - chr21 9438990 z
head(read.table(CpG_OB, header=FALSE), 2)
#> V1
#> 1 GWNJ-0965:805:GW2108234048th:2:1114:27600:18502_1:N:0:ATTCCT:R1:GAAAATGA:R2:AAGATGCC:F1:TGACT:F2:TGACT
#> 2 GWNJ-0965:805:GW2108234048th:2:1114:27600:18502_1:N:0:ATTCCT:R1:GAAAATGA:R2:AAGATGCC:F1:TGACT:F2:TGACT
#> V2 V3 V4 V5
#> 1 + chr21 9498035 Z
#> 2 + chr21 9497961 ZThe output is a list in BED file format and/or written to an output BED file, where each line contains methylation states of all CpGs on the same fragment. Column methState is a sequence of binary values indicating the methylation states of all CpGs on the same fragment (0 represents unmethylated CpG and 1 represents methylated CpG).
methInfo <- MergeCpGs(CpG_OT, CpG_OB)
head(methInfo, 2)
#> chr cpgStart cpgEnd strand cpgNumber
#> 1 chr21 9439600 9439741 - 7
#> 2 chr21 9483487 9483622 - 16
#> cpgPosition
#> 1 9439600,9439636,9439678,9439685,9439717,9439725,9439741
#> 2 9483487,9483489,9483516,9483519,9483522,9483524,9483536,9483539,9483541,9483574,9483577,9483580,9483582,9483600,9483603,9483622
#> methState
#> 1 0111010
#> 2 0111011010011010
#> qname
#> 1 GWNJ-0965:805:GW2108234048th:1:1101:11972:44486_1:N:0:ATTCCT:R1:TTACGGCG:R2:CAGTGGAG:F1:TGACT:F2:TGACT
#> 2 GWNJ-0965:805:GW2108234048th:1:1101:20933:67955_1:N:0:ATTCCT:R1:GGGACTTT:R2:CGCGAGTA:F1:TGACT:F2:TGACTGenerate fragment-level information about methylation states
Function GenerateFragMeth() combines the output lists ofMergePEReads() and MergeCpGs() into one list, which contains both the fragment information and the methylation states of all CpGs on each fragment.
fragMeth <- GenerateFragMeth(fragInfo, methInfo)
head(fragMeth, 2)
#> chr start end
#> 1 chr21 9417768 9418223
#> 2 chr21 9431278 9431614
#> qname
#> 1 GWNJ-0965:805:GW2108234048th:1:2211:31223:60642_1:N:0:ATTCCT:R1:CAAGTCCC:R2:ACATTATG:F1:TGACT:F2:AGACT
#> 2 GWNJ-0965:805:GW2108234048th:1:1106:4411:3542_1:N:0:ATTCCT:R1:CATGTGCC:R2:AGCACTAA:F1:TGACT:F2:ACACT
#> fragmentLength strand cpgNumber cpgPosition
#> 1 455 - 2 9418124,9418171
#> 2 336 + 5 9431279,9431386,9431537,9431554,9431596
#> methState
#> 1 01
#> 2 00111Generate the methylation pattern of markers
Function GenerateMarkerParam() calculates paired shape parameters of beta distributions for each marker. There are three main inputs to this function: (1) a list of methylation levels (e.g., beta values), where each row is a sample and each column is a marker; (2) a vector of sample types (e.g., tumor or normal, tissue types) corresponding to the rows of the list; (3) a vector of marker names corresponding to the columns of the list.
methLevel <- read.table(file.path(demo.dir, "beta_matrix.txt.gz"),
row.names=1, header = TRUE)
sampleTypes <- read.table(file.path(demo.dir, "sample_type.txt.gz"),
row.names=1, header = TRUE)$sampleType
markerNames <- read.table(file.path(demo.dir, "marker_index.txt.gz"),
row.names=1, header = TRUE)$markerIndex
print(methLevel)
#> marker1 marker2 marker3
#> sample1 0.8967302 0.9444779 0.9274943
#> sample2 0.6807593 0.9391826 0.8329514
#> sample3 0.8407675 0.9312582 0.8824475
#> sample4 0.8614165 0.9571972 0.8170904
#> sample5 0.8685513 0.9138511 0.8993120
#> sample6 0.5926250 0.9626752 0.8629679
#> sample7 0.8382709 0.9145921 0.2179631
#> sample8 0.8554025 0.9510139 0.8368095
#> sample9 0.8286787 0.9376723 0.8830746
#> sample10 0.8362881 0.9286860 0.8326099
#> sample11 0.8782031 0.9801491 0.9095433
#> sample12 0.6815395 0.9093327 0.8749625
#> sample13 0.8751985 0.9527680 0.8362279
#> sample14 0.7974884 0.9184259 0.9163573
#> sample15 0.8785107 0.8995657 0.8645149
#> sample16 0.8159900 0.9719771 0.8779748
#> sample17 0.4364249 0.8534525 0.3489145
#> sample18 0.8701227 0.9525218 0.8704187
#> sample19 0.8737151 0.9037688 0.8957254
#> sample20 0.8572883 0.9467923 0.8380768
print(sampleTypes)
#> [1] "normal" "tumor" "tumor" "normal" "normal" "tumor" "tumor" "normal"
#> [9] "normal" "tumor" "normal" "tumor" "normal" "tumor" "tumor" "normal"
#> [17] "tumor" "normal" "tumor" "normal"
print(markerNames)
#> [1] 1 2 3The output is a list containing the paired shape parameters of beta distributions for markers, which are delimited by :. Users can save this list into a file with column names, no row names, and columns are delimited by TAB for later use.
markerParam <- GenerateMarkerParam(methLevel, sampleTypes, markerNames)
print(markerParam)
#> markerName tumor normal
#> 1 1 5.955:2.032 183.74:29.723
#> 2 2 83.653:7.662 134.584:6.958
#> 3 3 1.476:0.484 72.949:10.939Note that parameter value NA:NA or 0:0 may cause errors in the following analyses. Remove these values before using this file.
Fragments intersecting with marker regions
To make the computation more efficient, users may only keep the fragments that overlap with the genomic regions of markers. Here, we provide an example of using R package GenomicRanges to perform the intersection.
First, transform the two BED files into GRanges classes.
library(GenomicRanges)
# a BED file of fragment-level methylation information
frag_bed <- read.csv(file.path(demo.dir, "demo.fragment_level.meth.bed.txt.gz"),
header=TRUE, sep="\t")
frag_meth.gr <- GRanges(seqnames=frag_bed$chr,
ranges=IRanges(frag_bed$start, frag_bed$end),
strand=frag_bed$strand,
methState=as.character(frag_bed$methState))
# a BED file of genomic regions of markers
markers_bed <- read.csv(file.path(demo.dir, "markers.bed.txt.gz"),
header=TRUE, sep="\t")
markers.gr <- GRanges(seqnames=markers_bed$chr,
ranges=IRanges(markers_bed$start, markers_bed$end),
markerName=markers_bed$markerName)
head(frag_meth.gr, 2)
#> GRanges object with 2 ranges and 1 metadata column:
#> seqnames ranges strand | methState
#> <Rle> <IRanges> <Rle> | <character>
#> [1] chr21 9417768-9418223 - | 1
#> [2] chr21 9431278-9431614 + | 111
#> -------
#> seqinfo: 1 sequence from an unspecified genome; no seqlengths
head(markers.gr, 2)
#> GRanges object with 2 ranges and 1 metadata column:
#> seqnames ranges strand | markerName
#> <Rle> <IRanges> <Rle> | <integer>
#> [1] chr21 9418124-9418171 * | 1
#> [2] chr21 9437462-9437533 * | 2
#> -------
#> seqinfo: 1 sequence from an unspecified genome; no seqlengthsThen, overlap two GRanges classes and get the fragment-level methylation states intersecting with the markers.
ranges <- subsetByOverlaps(frag_meth.gr, markers.gr, ignore.strand=TRUE)
hits <- findOverlaps(frag_meth.gr, markers.gr,ignore.strand=TRUE)
idx <- subjectHits(hits)
values <- DataFrame(markerName=markers.gr$markerName[idx])
mcols(ranges) <- c(mcols(ranges), values)
marker.methState <- as.data.frame(cbind(ranges$markerName,
ranges$methState))
colnames(marker.methState) <- c("markerName", "methState")
head(marker.methState, 4)
#> markerName methState
#> 1 1 1
#> 2 2 10111101110
#> 3 2 101100011010
#> 4 2 1011000111001Cancer detection with CancerDetector()
Function CancerDetector() separates cfDNA into tumor-derived fragments and background normal fragments and estimates the tumor burden. The main inputs are two files: (1) the fragment-level methylation states of reads (column methState) that mapped to the cancer-specific markers; (2) paired shape parameters of beta distributions for cancer-specific markers. All columns are delimited by TAB, and the first line is the column names. In addition, users can tune the parameter lambda to adjust the relative level of tumor burden.
fragMethFile <- file.path(demo.dir, "CancerDetector.reads.txt.gz")
markerParamFile <- file.path(demo.dir, "CancerDetector.markers.txt.gz")
head(read.csv(fragMethFile, sep = "\t", colClasses = "character"), 4)
#> markerName methState
#> 1 2 000
#> 2 2 000
#> 3 2 000
#> 4 2 111111
head(read.csv(markerParamFile, sep = "\t"), 4)
#> markerName tumor normalPlasma
#> 1 2 5:3 97:12
#> 2 3 3:2 57:9
#> 3 4 4:4 24:3
#> 4 5 3:3 32:5The output is the estimated tumor burden \(\theta\) and the normal cfDNA fraction \(1-\theta\).
CancerDetector(fragMethFile, markerParamFile, lambda=0.5, id="test")
#> cfDNA_tumor_burden normal_cfDNA_fraction
#> 1 0.055 0.945Tissue deconvolution with cfDeconvolve()
Function cfDeconvolve() estimates fractions of cfDNA fragments from different tissues (> 2 classes). The main two input files are similar to function CancerDetector(): (1) the fragment-level methylation states of reads (column methState) that mapped to the tissue-specific markers; (2) paired shape parameters of beta distributions for tissue-specific markers. All columns are delimited by TAB, and there is a header line of the column names.
fragMethFile2 <- file.path(demo.dir, "cfDeconvolve.reads.txt.gz")
markerParamFile2 <- file.path(demo.dir, "cfDeconvolve.markers.txt.gz")
head(read.csv(fragMethFile2, header=TRUE, sep="\t",
colClasses = "character"), 4)
#> markerName methState
#> 1 2 1111100
#> 2 2 0100110
#> 3 2 0111111
#> 4 2 1111111
head(read.csv(markerParamFile2, header=TRUE, sep="\t",
colClasses = "character"), 4)
#> markerName tissue1 tissue2 tissue3 tissue4 tissue5 tissue6
#> 1 2 1.68:1.82 2:18 1.02:0.459 2.08:18 0.061 0.176
#> 2 27 0.605 0.47 0.667 29.1:12.1 0.0528 0.542
#> 3 61 12.8:2.68 9.35:1.75 19.4:4.06 6.28:7.06 8.44:20.4 21.1:3.48
#> 4 63 4.71:2.49 13.5:2.17 55.3:8.53 0.906 31.3:3.8 5.46:3.17
#> tissue7
#> 1 6.65:2.19
#> 2 7.31:3.47
#> 3 6.69:4.01
#> 4 15:3.3Other input parameters are:
- Number of tissue types: a positive integer number k. Reads will be classified into k different tissue types.
- Read-based tissue deconvolution EM algorithm type: options are
em.global.unknown(default),em.global.known,em.local.unknown,em.local.known. - Likelihood ratio threshold: a positive float number. We suggest this number is 2 (default). All reads with likelihood ratio < cutoff (default: -1.0, meanin no reads filtering is used) will not be used. Likelihood ratio is the max(all tissues’ likelihoods)/min(all tissues’ likelihoods).
- EM algorithm maximum iteration number: a positive integer number. Default is 100.
- Random seed: a random seed that initialize the EM algorithm. Default is 0.
- Sample ID: the unique sample ID.
For example:
numTissues <- 7
emAlgorithmType <- "em.global.unknown"
likelihoodRatioThreshold <- 2
emMaxIterations <- 100
randomSeed <- 0
id <- "test"The output is a list containing the cfDNA fractions of different tissue types and an unknown class.
tissueFraction <- cfDeconvolve(fragMethFile2, markerParamFile2, numTissues,
emAlgorithmType, likelihoodRatioThreshold,
emMaxIterations, randomSeed, id)
tissueFraction
#> tissue1 tissue2 tissue3 tissue4 tissue5 tissue6 tissue7
#> 1 1.58246e-13 1.35298e-19 0.0442296 1.45249e-21 0.0157226 4.00821e-18 0.930494
#> unknown
#> 1 0.00955414Tissue deconvolution with cfSort()
Function cfSort() estimates fractions of cfDNA fragments derived from 29 major human tissues. It is the first supervised tissue deconvolution approach with deep learning models. The main input file is similar to functionCancerDetector() and cfDeconvolve(): the fragment-level methylation states of reads (column methState) that mapped to the tissue-specific markers. The first column is the marker name of cfSort markers.
fragMethInfo <- file.path(demo.dir, "cfsort_reads.txt.gz")
head(read.csv(fragMethInfo, header=TRUE, sep="\t",
colClasses = "character"), 4)
#> markerName methState
#> 1 2 1110101
#> 2 2 1111110
#> 3 2 1111101
#> 4 2 1111111The output is a list containing the cfDNA fractions of 29 tissue types.
tissueFraction2 <- cfSort(fragMethInfo, id="demo")
tissueFraction2
#> adipose_tissue adrenal_gland bladder blood_vessel breast cervix_uteri colon
#> 1 0 0 0 0 0 0 0
#> esophagus fallopian_tube heart kidney liver lung muscle nerve ovary pancreas
#> 1 0 0 0 0 0 0 0 0 0 0
#> pituitary prostate salivary_gland skin small_intestine spleen stomach testis
#> 1 0 0 0 0 0 0 0 0
#> thyroid uterus vagina WBC
#> 1 0 0 0 1Visualization with PlotFractionPie()
Function PlotFractionPie() generates a pie chart for a vector of cfDNA fractions. The main input is a numeric vector of class-specific fractions. The title, colors, and font size can be adjusted to enhance the look of the figure.
cancer_normal_df <- CancerDetector(fragMethFile, markerParamFile, lambda=0.5, id="test")
PlotFractionPie(cancer_normal_df, title = "cfDNA Composition", class_colors = c("normal_cfDNA_fraction" = "blue"), font_size = 1.2)Reference
[1] Li W, Li Q, Kang S, Same M, Zhou Y, Sun C, Liu CC, Matsuoka L, Sher L, Wong WH, Alber F, Zhou XJ. CancerDetector: ultrasensitive and non-invasive cancer detection at the resolution of individual reads using cell-free DNA methylation sequencing data. Nucleic Acids Res. 2018 Sep 6;46(15):e89. doi: 10.1093/nar/gky423. PMID: 29897492; PMCID: PMC6125664.
[2] Del Vecchio G, Li Q, Li W, Thamotharan S, Tosevska A, Morselli M, Sung K, Janzen C, Zhou X, Pellegrini M, Devaskar SU. Cell-free DNA methylation and transcriptomic signature prediction of pregnancies with adverse outcomes.Epigenetics. 2021 Jun;16(6):642-661. doi: 10.1080/15592294.2020.1816774. PMID: 33045922; PMCID: PMC8143248.
[3] Li S, Zeng W, Ni X, Liu Q, Li W, Stackpole ML, Zhou Y, Gower A, Krysan K, Ahuja P, Lu DS, Raman SS, Hsu W, Aberle DR, Magyar CE, French SW, Han SB, Garon EB, Agopian VG, Wong WH, Dubinett SM, Zhou XJ. Comprehensive tissue deconvolution of cell-free DNA by deep learning for disease diagnosis and monitoring. Proc Natl Acad Sci U S A. 2023 Jul 11;120(28):e2305236120. doi: 10.1073/pnas.2305236120. Epub 2023 Jul 3. PMID: 37399400.
Session info
#> R version 4.5.0 RC (2025-04-04 r88126)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.2 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0 LAPACK version 3.12.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_GB LC_COLLATE=C
#> [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
#>
#> time zone: America/New_York
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats4 stats graphics grDevices utils datasets methods
#> [8] base
#>
#> other attached packages:
#> [1] cfToolsData_1.5.0 GenomicRanges_1.60.0 GenomeInfoDb_1.44.0
#> [4] IRanges_2.42.0 S4Vectors_0.46.0 BiocGenerics_0.54.0
#> [7] generics_0.1.3 cfTools_1.8.0 BiocStyle_2.36.0
#>
#> loaded via a namespace (and not attached):
#> [1] KEGGREST_1.48.0 dir.expiry_1.16.0 xfun_0.52
#> [4] bslib_0.9.0 Biobase_2.68.0 lattice_0.22-7
#> [7] vctrs_0.6.5 tools_4.5.0 curl_6.2.2
#> [10] parallel_4.5.0 tibble_3.2.1 AnnotationDbi_1.70.0
#> [13] RSQLite_2.3.9 blob_1.2.4 pkgconfig_2.0.3
#> [16] R.oo_1.27.0 Matrix_1.7-3 dbplyr_2.5.0
#> [19] lifecycle_1.0.4 GenomeInfoDbData_1.2.14 compiler_4.5.0
#> [22] Biostrings_2.76.0 tinytex_0.57 htmltools_0.5.8.1
#> [25] sass_0.4.10 yaml_2.3.10 crayon_1.5.3
#> [28] pillar_1.10.2 jquerylib_0.1.4 R.utils_2.13.0
#> [31] cachem_1.1.0 magick_2.8.6 mime_0.13
#> [34] ExperimentHub_2.16.0 AnnotationHub_3.16.0 basilisk_1.20.0
#> [37] tidyselect_1.2.1 digest_0.6.37 purrr_1.0.4
#> [40] dplyr_1.1.4 bookdown_0.43 BiocVersion_3.21.1
#> [43] fastmap_1.2.0 grid_4.5.0 cli_3.6.4
#> [46] magrittr_2.0.3 withr_3.0.2 filelock_1.0.3
#> [49] UCSC.utils_1.4.0 rappdirs_0.3.3 bit64_4.6.0-1
#> [52] rmarkdown_2.29 XVector_0.48.0 httr_1.4.7
#> [55] bit_4.6.0 reticulate_1.42.0 png_0.1-8
#> [58] R.methodsS3_1.8.2 memoise_2.0.1 evaluate_1.0.3
#> [61] knitr_1.50 basilisk.utils_1.20.0 BiocFileCache_2.16.0
#> [64] rlang_1.1.6 Rcpp_1.0.14 glue_1.8.0
#> [67] DBI_1.2.3 BiocManager_1.30.25 jsonlite_2.0.0
#> [70] R6_2.6.1