CNVfilteR: Remove false positives of CNV calling tools by using SNV calls
30 April 2024
Package
CNVfilteR 1.18.0
Contents
1 Introduction
Many tools for germline copy number variant (CNV) detection from NGS data have been developed. Usually, these tools were designed for different input data like WGS, WES or panel data, and their performance may depend on the CNV size. Available benchmarks show that all these tools produce false positives, sometimes reaching a very high number of them.
With the aim of reducing the number of false positives, CNVfilteR identifies those germline CNVs that can be discarded. This task is performed by using the germline single nucleotide variant (SNV) calls that are usually obtained in common NGS pipelines. As VCF field interpretation is key when working with these files, CNVfilteR specifically supports VCFs produced by VarScan2, Strelka/Strelka2, freeBayes, HaplotypeCaller (GATK), UnifiedGenotyper (GATK) and Torrent Variant Caller. Additionally, results can be plotted using the functions provided by the R/Bioconductor packages karyoploteR and CopyNumberPlots.
2 Installation
CNVfilteR is a Bioconductor package and to install it we have to use BiocManager.
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("CNVfilteR")We can also install the package from github to get the latest devel version.
BiocManager::install("jpuntomarcos/CNVfilteR")3 Quick Start
Below we show a full example that covers the usual steps: CNVs data loading, SNVs loading, identifying false postives and plotting the results.
First, we can load some CNV tool results:
library(CNVfilteR)
cnvs.file <- system.file("extdata", "DECoN.CNVcalls.csv", package = "CNVfilteR", mustWork = TRUE)
cnvs.gr <- loadCNVcalls(cnvs.file = cnvs.file, chr.column = "Chromosome", start.column = "Start", end.column = "End", cnv.column = "CNV.type", sample.column = "Sample", genome = "hg19")Then, we load the SNVs stored in a couple of VCF files.
vcf.files <- c(system.file("extdata", "variants.sample1.vcf.gz", package = "CNVfilteR", mustWork = TRUE),
system.file("extdata", "variants.sample2.vcf.gz", package = "CNVfilteR", mustWork = TRUE))
vcfs <- loadVCFs(vcf.files, cnvs.gr = cnvs.gr, genome = "hg19")## Scanning file /tmp/RtmpujR3WU/Rinst2a526773aa5740/CNVfilteR/extdata/variants.sample1.vcf.gz...## VarScan2 was found as source in the VCF metadata, RD will be used as ref allele depth field, AD will be used as alt allele depth field.## Scanning file /tmp/Rtmp0Ctg0Z/variants.sample2.vcf.gz...## VarScan2 was found as source in the VCF metadata, RD will be used as ref allele depth field, AD will be used as alt allele depth field.We observe that the function recognized VarScan2 as the source, so fields
were selected and allele frequency consequently. Now we can call
filterCNVs() to identify those CNVs that can be discarded.
results <- filterCNVs(cnvs.gr, vcfs)
names(results)## [1] "cnvs" "variantsForEachCNV" "filterParameters"And we can check those CNVs that can be filtered out:
results$cnvs[results$cnvs$filter == TRUE]## GRanges object with 3 ranges and 10 metadata columns:
## seqnames ranges strand | cnv sample cnv.id
## <Rle> <IRanges> <Rle> | <character> <character> <character>
## 3 chr2 48025751-48028294 * | duplication sample1 3
## 16 chr17 41243453-41247939 * | duplication sample1 16
## 19 chr13 32900637-32929425 * | deletion sample2 19
## filter n.total.variants n.hm.variants n.ht.discard.CNV
## <character> <character> <character> <character>
## 3 TRUE 5 0 3
## 16 TRUE 2 0 2
## 19 TRUE 10 4 6
## n.ht.confirm.CNV ht.pct score
## <character> <character> <character>
## 3 2 2.539241834223
## 16 0 1.99927691434002
## 19 60
## -------
## seqinfo: 7 sequences from an unspecified genome; no seqlengthsAs an example, we can observe that the CNV with cnv.id=3 contains 4 variants:
2 in favor of discarding it, two against discarding it. If we want
to know more about the variants falling in a certain CNV we can do:
results$variantsForEachCNV[["3"]]## seqnames start end width strand ref alt ref.support alt.support
## 1 chr2 48026019 48026019 1 * G C 516 521
## 2 chr2 48027019 48027019 1 * G C 1528 964
## 3 chr2 48027182 48027182 1 * G A 1506 971
## 4 chr2 48027434 48027434 1 * A T 1593 1462
## 5 chr2 48027763 48027763 1 * G A 900 863
## alt.freq total.depth indel type score
## 1 50.2411 1037 FALSE ht 0.9999596
## 2 38.6838 2492 FALSE ht -0.3169985
## 3 39.2006 2477 FALSE ht -0.1417051
## 4 47.8560 3055 FALSE ht 0.9981889
## 5 48.9507 1763 FALSE ht 0.9997969Two variants are close to the default expected heterozygous frequency,
0.5, so they obtain a positive score. The other two variants are not
so clearly close to the default expected duplication value, 0.33, so
they obtain a low negative score. All these default values and others can
be modified in the filterCNVs() function.
Finally, we may be interested in plotting the results. For example, we can
plot easily the duplication CNV with cnv.id=3 and all the variants falling in
it.
plotVariantsForCNV(results, "3")
We can do the same to plot the deletion CNV with cnv.id=19, where all
variants discard the CNV except one homozygous variant that does not give us any
information for supporting or discarding the CNV:
plotVariantsForCNV(results, "19")
On the opposite side, we can observe those CNVs that cannot be discarded:
results$cnvs[results$cnvs$filter != TRUE]## GRanges object with 17 ranges and 10 metadata columns:
## seqnames ranges strand | cnv sample cnv.id
## <Rle> <IRanges> <Rle> | <character> <character> <character>
## 1 chr2 47641409-47641557 * | duplication sample1 1
## 2 chr2 47698105-47698201 * | duplication sample1 2
## 4 chr3 10091059-10091189 * | duplication sample1 4
## 5 chr3 14219967-14220068 * | duplication sample1 5
## 6 chr3 37042447-37042544 * | duplication sample1 6
## .. ... ... ... . ... ... ...
## 14 chr13 32953455-32969070 * | duplication sample1 14
## 15 chr17 41209070-41215390 * | duplication sample1 15
## 17 chr17 41251793-41256973 * | duplication sample1 17
## 18 chr17 41267744-41276113 * | duplication sample1 18
## 20 chr17 59870959-59938900 * | deletion sample2 20
## filter n.total.variants n.hm.variants n.ht.discard.CNV
## <character> <character> <character> <character>
## 1 0 0
## 2 0 0
## 4 0 0
## 5 0 0
## 6 1 0 1
## .. ... ... ... ...
## 14 0 0
## 15 0 0
## 17 0 0
## 18 0 0
## 20 0 0
## n.ht.confirm.CNV ht.pct score
## <character> <character> <character>
## 1
## 2
## 4
## 5
## 6 0 0.372384685846936
## .. ... ... ...
## 14
## 15
## 17
## 18
## 20
## -------
## seqinfo: 7 sequences from an unspecified genome; no seqlengthsFor example, the CNV with cnv.id=14 contains one variant. If we get
the variant info, we see that the variant has an allele frequency very close to
the default expected duplication value, 0.66.
results$variantsForEachCNV[["14"]]## NULLSo, no evidence was found for discarding the CNV. We can also plot the CNV and the variant:
plotVariantsForCNV(results, "3")
4 Loading Copy-Number Data
CNVfilteR functions expect germline CNVs calls to be a
GRanges object with a few specificic metadata columns:
- cnv for storing the CNV type: duplication or deletion.
- sample for storing the sample name of the CNV.
You can create this object yourself, but
CNVfilter provides the proper function to do this,
loadCNVcalls(). This function can interpret any tsv o csv file by indicating
which columns store the information. For example, in the following code, the
chr.column column is stored in the “Chromosome” column of the cnvs.file.
cnvs.file <- system.file("extdata", "DECoN.CNVcalls.csv", package = "CNVfilteR", mustWork = TRUE)
cnvs.gr <- loadCNVcalls(cnvs.file = cnvs.file, chr.column = "Chromosome", start.column = "Start", end.column = "End", cnv.column = "CNV.type", sample.column = "Sample", genome = "hg19")loadCNVcalls() can interpret different types of CNVs. Among other options,
separator can be selected using the sep parameter (defaults to \t),
and first lines can be skipped using the skip parameter (defaults to 0). Also,
the value used in cnv.column to store the CNV type can be modified
using the deletion and duplication parameters (defaults to “deletion” and
“duplication”, respectively). If, for example, our cnv.column uses “CN1” and
“CN3” for deletion and duplication (respectively), we should indicate
the function to use these values:
cnvs.gr.2 <- loadCNVcalls(cnvs.file = cnvs.file.2, deletion = "CN1", duplication = "CN3", chr.column = "Chromosome", start.column = "Start", end.column = "End", cnv.column = "CNV.type", sample.column = "Sample")Some CNV tools generate results where the CNV location is stored in a single
column with the format chr:start-end (i.e. 1:538001-540000). In this
case, we can call loadCNVcalls() using the coord.column instead of the
chr.column, start.column and end.column columns.
5 Loading Variants Data
Common NGS pipelines produce germline variant calling (SNVs or INDELs)
in a VCF file. However, VCF interpretation is
challenging due to the flexibility provided by the VCF format definition.
It is not straightforward to interpret correctly the fields in the VCF file
and compute the allele frequency. loadVCFs() interprets automatically
VCFs produced by VarScan2, Strelka/Strelka2, freeBayes, HaplotypeCaller (GATK),
UnifiedGenotyper (GATK) and Torrent Variant Caller.
In the following example the function recognizes VarScan2 as the source.
vcf.files <- c(system.file("extdata", "variants.sample1.vcf.gz", package = "CNVfilteR", mustWork = TRUE),
system.file("extdata", "variants.sample2.vcf.gz", package = "CNVfilteR", mustWork = TRUE))
vcfs <- loadVCFs(vcf.files, cnvs.gr = cnvs.gr)## Scanning file /tmp/RtmpujR3WU/Rinst2a526773aa5740/CNVfilteR/extdata/variants.sample1.vcf.gz...## VarScan2 was found as source in the VCF metadata, RD will be used as ref allele depth field, AD will be used as alt allele depth field.## Scanning file /tmp/Rtmp0Ctg0Z/variants.sample2.vcf.gz...## VarScan2 was found as source in the VCF metadata, RD will be used as ref allele depth field, AD will be used as alt allele depth field.We can also load the VCF file spicifying how to interpret it, which can be useful if the VCF was generated by a caller not supported by CNVfilteR. For example we can specify the ref/alt fields:
vcfs <- loadVCFs(vcf.files, cnvs.gr = cnvs.gr, vcf.source = "MyCaller", ref.support.field = "RD", alt.support.field = "AD")## Scanning file /tmp/RtmpujR3WU/Rinst2a526773aa5740/CNVfilteR/extdata/variants.sample1.vcf.gz...## VCF source MyCaller is not supported, but ref.support.field/alt.support.field were provided.## Scanning file /tmp/Rtmp0Ctg0Z/variants.sample2.vcf.gz...## VCF source MyCaller is not supported, but ref.support.field/alt.support.field were provided.Alternatively, we can set the list.support.field parameter so that field
will be loaded assuming that it is a list in this order: reference allele,
alternative allele. As an example:
vcf.file3 <- c(system.file("extdata", "variants.sample3.vcf", package = "CNVfilteR", mustWork = TRUE))
vcfs3 <- loadVCFs(vcf.file3, cnvs.gr = cnvs.gr, vcf.source = "MyCaller", list.support.field = "AD")## Scanning file /tmp/Rtmp0Ctg0Z/variants.sample3.vcf.gz...## VCF source MyCaller is not supported, but list.support.field was provided.5.1 VCF free of artifacts
CNVfilteR uses SNVs to identify false-positive CNV calls. Therefore, its performance depends on the SNV calls quality. We recommend using VCF files free of false-positive SNVs (as possible) to improve CNVfilteR accuracy. Some considerations can be followed in order to provide reliable SNVs to CNVfilteR.
5.1.1 Minimun total depth
Use the min.total.depth parameter to discard SNVs with low depth coverage in
the loadVCFs function. The default
value is 10, which may be appropriate in many WGS samples, but
this value should be adapted to your experiment conditions. For
example, we used a min.total.depth
of 30 when using CNVfilteR on panel (targeted-enrinched) samples with high
coverage and VarScan2 as SNV caller.
5.1.2 Regions to exclude
Low complexity and repetitive regions are genome areas where SNV callers
(also CNV callers) perform poorly. If possible, ignore these regions when using
CNVfilteR. We can exclude
those complex regions that have already known alignement artifacts
with the parameter regions.to.exclude.
In this example, we exclude PMS2, PRSS1, and
FANCD2 genes because they are pseudogenes with alignments artifacts:
regions.to.exclude <- GRanges(seqnames = c("chr3","chr7", "chr7"), ranges = IRanges(c(10068098, 6012870, 142457319), c(10143614, 6048756, 142460923)))
vcfs4 <- loadVCFs(vcf.files, cnvs.gr = cnvs.gr, regions.to.exclude = regions.to.exclude)## Scanning file /tmp/RtmpujR3WU/Rinst2a526773aa5740/CNVfilteR/extdata/variants.sample1.vcf.gz...## VarScan2 was found as source in the VCF metadata, RD will be used as ref allele depth field, AD will be used as alt allele depth field.## Scanning file /tmp/Rtmp0Ctg0Z/variants.sample2.vcf.gz...## VarScan2 was found as source in the VCF metadata, RD will be used as ref allele depth field, AD will be used as alt allele depth field.5.1.3 INDELs excluded by default
Also, the parameter exclude.indels indicates whether to exclude INDELs when
loading the variants. TRUE is the default and recommended value given
that INDELs allele frequency varies differently than SNVs. Including
INDELs may allow the algorithm to identify more CNVs to discard with a greater
risk of identifying them wrongly. Additionally, any SNV overlapping an INDEL
will be ignored because the SNV allele frequency may be affected in that region.
5.2 Other settings
The function loadVCFs() also adapts to different needs. If sample.names parameter is
not provided, the sample names included in the VCF itself will be used.
Both single-sample and multi-sample VCFs are accepted, but when
multi-sample VCFs are used, sample.names parameter must be NULL.
If VCF is not compressed with bgzip, the function compresses it and generates the .gz file. If .tbi file does not exist for a given VCF file, the function also generates it. All files are generated in a temporary folder.
See loadVCFs() documentation to see other parameters info.
5.3 Limitations
Currlently CNVfilteR does not support mutiallelic sites in VCF files,
such as chr3 193372598 .;. TTA T,TTT. As an easy work around,
mutiallelic sites can be split by using
bcftools:
bcftools norm -N -m -both yourSample.vcf > splitSample.vcf
6 Identifying false positives
The task of identifying false positives is performed by the filterCNVs()
function. It checks all the variants (SNVs and optionally INDELs) falling in
each CNV present in cnvs.gr to identify those CNVs that can be filtered out. It
returns an S3 object with 3 elements: cnvs, variantsForEachCNV and
filterParameters:
results <- filterCNVs(cnvs.gr, vcfs)## 3 out of 20 (15%) CNVs can be filtered## 3 out of 5 (60%) CNVs with overlapping SNVs can be filteredtail(results$cnvs)## GRanges object with 6 ranges and 10 metadata columns:
## seqnames ranges strand | cnv sample cnv.id
## <Rle> <IRanges> <Rle> | <character> <character> <character>
## 15 chr17 41209070-41215390 * | duplication sample1 15
## 16 chr17 41243453-41247939 * | duplication sample1 16
## 17 chr17 41251793-41256973 * | duplication sample1 17
## 18 chr17 41267744-41276113 * | duplication sample1 18
## 19 chr13 32900637-32929425 * | deletion sample2 19
## 20 chr17 59870959-59938900 * | deletion sample2 20
## filter n.total.variants n.hm.variants n.ht.discard.CNV
## <character> <character> <character> <character>
## 15 0 0
## 16 TRUE 2 0 2
## 17 0 0
## 18 0 0
## 19 TRUE 10 4 6
## 20 0 0
## n.ht.confirm.CNV ht.pct score
## <character> <character> <character>
## 15
## 16 0 1.99927691434002
## 17
## 18
## 19 60
## 20
## -------
## seqinfo: 7 sequences from an unspecified genome; no seqlengthsObserve that those CNVs that can be filtered out have the value TRUE in the
column filter. CNVfilteR employs two
different strategies for identifying those CNVs:
- A deletion CNV can be filtered out if there is at least
ht.deletions.threshold% of heterozygous variants in the CNV. Defaultht.deletions.thresholdvalue is 30, so 30% is required. - A duplication CNV can be filtered out if the total
scoreis >=dup.threshold.scoreafter computing all heterozygous variants falling in that CNV. Defaultdup.threshold.scorevalue is 0.5. How the score is computed for each variant is explained in the next section.
6.1 Scoring model for duplication CNVs
The scoring model for determining whether a certain duplication CNV can be discarded is based on the allele frequency for each heterozygous variant falling in that CNV:
- In common conditions with no presence of a duplication CNV, the allele
frequency of a heterozygous variant is expected to be close to 50%
(
expected.ht.mean). So, a variant with an allele frequency close to 50% gives us evidence of the non-existence of a duplication CNV, so the CNV could be discarded. - On the opposite side, if the variant occurs in the same region of a
certain duplication CNV, the allele frequency is expected to be close to 33.3%
(
expected.dup.ht.mean1) when the variant is not in the same allele than the duplication CNV, and 66.6% (expected.dup.ht.mean2) when the variant is in the same allele than the duplication CNV call. So, a variant with an allele frequency close to 33.3% or 66.6% gives us evidence of the existence of duplication CNV.
Although we can expect that most of the variants are close to the expected means
(33.3%, 50%, and 66.6%), many of them can be far from any expected mean. The
scoring model implemented in the filterCNVs() function measures
the evidence - for discarding a certain CNV - using
a scoring model. The scoring model is based on the fuzzy logic, where elements
can have any value between 1 (True) and 0 (False). Following this idea,
each variant will be scored with a value between 0 and 1 depending on
how close is the allele frequency to the nearest expected mean.
- For those variants with an allele frequency close to the expected mean when no duplication CNV occurs (defaults 50%), the score will be positive in the interval [0, 1].
- For those variants with an allele frequency close to the expected mean when a duplication CNV occurs (defaults 33.3%, 66.6%), the score will be negative in the interval [-1, 0].
The total score is computed among all the variants falling in the CNV. If the
score is greater than the dup.threshold.score, the CNV can be discarded.
A common way of applying the fuzzy logic is using the sigmoid function. CNVfilteR uses the sigmoid function implemented in the pracma package, which is defined as \[ \begin{aligned} y = 1/(1 + e^{-c1(x−c2)}) \end{aligned} \]
The scoring model is built on 6 sigmoids defined on 6 different intervals. The
c1 parameter is 2 by default (sigmoid.c1), and the c2 parameter
is defined for the 6 sigmoids (sigmoid.c2.vector).
- First sigmoid: interval [20,
expected.dup.ht.mean1], c2=28 - Second sigmoid: interval [
expected.dup.ht.mean1,sigmoid.int1], c2=38.3 - Third sigmoid: interval [
sigmoid.int1,expected.ht.mean], c2=44.7 - Fourth sigmoid: interval [
expected.ht.mean,sigmoid.int2], c2=55.3 - Fifth sigmoid: interval [
sigmoid.int2,expected.dup.ht.mean2], c`=61.3 - Sixth sigmoid: interval [
expected.dup.ht.mean2, 80], c2=71.3
Where sigmoid.int1 is the mean between expected.dup.ht.mean1 and
expected.ht.mean, and sigmoid.int2 is the mean between
expected.dup.ht.mean2 and expected.ht.mean.
The scoring model can be plotted using the plotScoringModel() function.
p <- results$filterParameters
plotScoringModel(expected.ht.mean = p$expected.ht.mean,
expected.dup.ht.mean1 = p$expected.dup.ht.mean1,
expected.dup.ht.mean2 = p$expected.dup.ht.mean2,
sigmoid.c1 = p$sigmoid.c1,
sigmoid.c2.vector = p$sigmoid.c2.vector)
And the scoring model can be modified when calling the filterCNVs() function.
Let’s see how the model changes when we modify the parameter sigmoid.c1
(1 instead of 2):
plotScoringModel(expected.ht.mean = p$expected.ht.mean,
expected.dup.ht.mean1 = p$expected.dup.ht.mean1,
expected.dup.ht.mean2 = p$expected.dup.ht.mean2,
sigmoid.c1 = 1,
sigmoid.c2.vector = p$sigmoid.c2.vector)
We can also modify the sigmoid.c2.vector parameter for each sigmoid function. For example, to
make the central sigmoids narrower:
plotScoringModel(expected.ht.mean = p$expected.ht.mean,
expected.dup.ht.mean1 = p$expected.dup.ht.mean1,
expected.dup.ht.mean2 = p$expected.dup.ht.mean2,
sigmoid.c1 = p$sigmoid.c1,
sigmoid.c2.vector = c(28, 38.3, 46.7, 53.3, 61.3, 71.3))
6.2 The margin.pct parameter
Many CNV callers produce inaccurate CNV calls. These inaccurate CNV calls are
more likely to be true (to overlap the real CNV) in the middle of the CNV than
in the extremes. So, the margin.pct parameter defines the percentage of CNV
(from each CNV limit) where SNVs will be ignored. By default, only 10% of SNVs
from each CNV extreme will be ignored. This margin.pct parameter can be
modified to better adapt it to your CNV caller. For example, we observed that
DECoN produced very accurate CNV calls in our genes panel dataset, so
margin.pct value was updated to 0 in this context.
Summarizing, variants in the CNV call but close to the ends of the CNV call will
be ignored. margin.pct defines the percentage of CNV length, located at
each CNV limit, where variants will be ignored. For example, for a CNV
chr1:1000-2000 and a margin.pct value of 10,
variants within chr1:1000-1100 and chr1:1900-2000 will be ignored.
7 Plotting results
We can plot easily a certain CNV and the variants in it. For example, the
duplication CNV with cnv.id=17 can be plotted as follows:
plotVariantsForCNV(results, "16")
Some parameters can be customized, like points.cex and points.pch. It is
also possible to plot all CNVs in a global schema where all the chromosomes are
plotted:
cnvs.file <- system.file("extdata", "DECoN.CNVcalls.2.csv",
package = "CNVfilteR", mustWork = TRUE)
cnvs.gr.2 <- loadCNVcalls(cnvs.file = cnvs.file, chr.column = "Chromosome",
start.column = "Start", end.column = "End",
cnv.column = "CNV.type", sample.column = "Sample",
genome = "hg19")
plotAllCNVs(cnvs.gr.2)
Note that if a CNV is too small, it will not be visible when calling
plotAllCNVs().
8 Session Info
sessionInfo()## R version 4.4.0 beta (2024-04-15 r86425)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.19-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
##
## locale:
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## [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
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## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
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## other attached packages:
## [1] BSgenome.Hsapiens.UCSC.hg19.masked_1.3.993
## [2] BSgenome.Hsapiens.UCSC.hg19_1.4.3
## [3] BSgenome_1.72.0
## [4] rtracklayer_1.64.0
## [5] BiocIO_1.14.0
## [6] Biostrings_2.72.0
## [7] XVector_0.44.0
## [8] GenomicRanges_1.56.0
## [9] GenomeInfoDb_1.40.0
## [10] IRanges_2.38.0
## [11] S4Vectors_0.42.0
## [12] BiocGenerics_0.50.0
## [13] CNVfilteR_1.18.0
## [14] knitr_1.46
## [15] BiocStyle_2.32.0
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## [41] RSQLite_2.3.6 fansi_1.0.6
## [43] httr_1.4.7 abind_1.4-5
## [45] compiler_4.4.0 bit64_4.0.5
## [47] htmlTable_2.4.2 backports_1.4.1
## [49] BiocParallel_1.38.0 DBI_1.2.2
## [51] highr_0.10 DelayedArray_0.30.0
## [53] rjson_0.2.21 tools_4.4.0
## [55] foreign_0.8-86 karyoploteR_1.30.0
## [57] nnet_7.3-19 glue_1.7.0
## [59] restfulr_0.0.15 rhdf5filters_1.16.0
## [61] cn.mops_1.50.0 grid_4.4.0
## [63] checkmate_2.3.1 cluster_2.1.6
## [65] generics_0.1.3 gtable_0.3.5
## [67] ensembldb_2.28.0 data.table_1.15.4
## [69] utf8_1.2.4 pillar_1.9.0
## [71] stringr_1.5.1 dplyr_1.1.4
## [73] lattice_0.22-6 bit_4.0.5
## [75] biovizBase_1.52.0 tidyselect_1.2.1
## [77] gridExtra_2.3 bookdown_0.39
## [79] ProtGenerics_1.36.0 SummarizedExperiment_1.34.0
## [81] xfun_0.43 Biobase_2.64.0
## [83] matrixStats_1.3.0 stringi_1.8.3
## [85] UCSC.utils_1.0.0 lazyeval_0.2.2
## [87] yaml_2.3.8 evaluate_0.23
## [89] codetools_0.2-20 tibble_3.2.1
## [91] BiocManager_1.30.22 cli_3.6.2
## [93] rpart_4.1.23 munsell_0.5.1
## [95] jquerylib_0.1.4 dichromat_2.0-0.1
## [97] Rcpp_1.0.12 png_0.1-8
## [99] XML_3.99-0.16.1 parallel_4.4.0
## [101] ggplot2_3.5.1 assertthat_0.2.1
## [103] blob_1.2.4 AnnotationFilter_1.28.0
## [105] bitops_1.0-7 CopyNumberPlots_1.20.0
## [107] VariantAnnotation_1.50.0 scales_1.3.0
## [109] crayon_1.5.2 bamsignals_1.36.0
## [111] rlang_1.1.3 KEGGREST_1.44.0