extraChIPs: Range-Based operations
29 October 2024
Package
extraChIPs 1.10.0
Contents
1 Introduction
This package is designed to enable the Gene Regulatory Analysis using Variable
IP (GRAVI) workflow, as a method for
detecting differential binding in ChIP-Seq datasets.
As a workflow focussed on data integration, most functions provided by the
package extraChIPs are designed to enable comparison across datasets.
This vignette looks primarily at functions which work with GenomicRanges
objects.
2 Installation
In order to use the package extraChIPs and follow this vignette, we recommend
using the package BiocManager hosted on CRAN.
Once this is installed, the additional packages required for this vignette
(tidyverse, plyranges and Gviz) can also be installed.
if (!"BiocManager" %in% rownames(installed.packages()))
install.packages("BiocManager")
BiocManager::install(c("tidyverse", "plyranges", "Gviz"))
BiocManager::install("extraChIPs")3 Coercion
The advent of the tidyverse has led to tibble objects becoming a common
alternative to data.frame or DataFrame objects.
Simple functions within extraChIP enable coercion from GRanges,
GInteractions and DataFrame objects to tibble objects, with list columns
correctly handled.
By default these coercion functions will coerce GRanges elements to a
character vector.
Similarly, GRanges represented as a character column can be coerced to the
ranges element of a GRanges object.
First let’s coerce from a tibble (or S3 data.frame) to a GRanges
library(tidyverse)
library(extraChIPs)
set.seed(73)
df <- tibble(
range = c("chr1:1-10:+", "chr1:5-10:+", "chr1:5-6:+"),
gene_id = "gene1",
tx_id = paste0("transcript", 1:3),
score = runif(3)
)
df## # A tibble: 3 × 4
## range gene_id tx_id score
## <chr> <chr> <chr> <dbl>
## 1 chr1:1-10:+ gene1 transcript1 0.442
## 2 chr1:5-10:+ gene1 transcript2 0.0831
## 3 chr1:5-6:+ gene1 transcript3 0.615gr <- colToRanges(df, "range")
gr## GRanges object with 3 ranges and 3 metadata columns:
## seqnames ranges strand | gene_id tx_id score
## <Rle> <IRanges> <Rle> | <character> <character> <numeric>
## [1] chr1 1-10 + | gene1 transcript1 0.4423369
## [2] chr1 5-10 + | gene1 transcript2 0.0831099
## [3] chr1 5-6 + | gene1 transcript3 0.6146112
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsCoercion back to a tibble will place the ranges as a character column by
default.
However, this can be turned off and the conventional coercion from
as.data.frame will instead be applied, internally wrapped in as_tibble()
as_tibble(gr)## # A tibble: 3 × 4
## range gene_id tx_id score
## <chr> <chr> <chr> <dbl>
## 1 chr1:1-10:+ gene1 transcript1 0.442
## 2 chr1:5-10:+ gene1 transcript2 0.0831
## 3 chr1:5-6:+ gene1 transcript3 0.615as_tibble(gr, rangeAsChar = FALSE)## # A tibble: 3 × 8
## seqnames start end width strand gene_id tx_id score
## <fct> <int> <int> <int> <fct> <chr> <chr> <dbl>
## 1 chr1 1 10 10 + gene1 transcript1 0.442
## 2 chr1 5 10 6 + gene1 transcript2 0.0831
## 3 chr1 5 6 2 + gene1 transcript3 0.615A simple feature which may be useful for printing gene names using rmarkdown
is contained in collapseGenes().
Here a character vector of gene names is collapsed into a glue object of
length `, with gene names rendered in italics by default.
gn <- c("Gene1", "Gene2", "Gene3")
collapseGenes(gn)Gene1, Gene2 and Gene3
4 Formation of Consensus Peaks
The formation of consensus peaks incorporating ranges from multiple replicates
is a key part of many ChIP-Seq analyses.
A common format returned by tools such as mcas2 callpeak is the narrowPeak
(or broadPeak) format.
Sets of these across multiple replicates can imported using the function
importPeaks(), which returns a GRangesList()
fl <- system.file(
c("extdata/ER_1.narrowPeak", "extdata/ER_2.narrowPeak"),
package = "extraChIPs"
)
peaks <- importPeaks(fl)
names(peaks) <- c("ER_1", "ER_2")In the above we have loaded the peaks from two replicates from the Estrogen
Receptor (ER) in T-47D cells.
To form consensus peaks we can place a restriction on the number of replicates
an overlapping peak needs to appear in.
By default, the function makeConsensus() sets the proportion to be zero
(p = 0) so all peaks are retained.
Note that a logical vector/column is returned for each replicate, along with
the number of replicates the consensus peak is derived from.
makeConsensus(peaks)## GRanges object with 14 ranges and 3 metadata columns:
## seqnames ranges strand | ER_1 ER_2 n
## <Rle> <IRanges> <Rle> | <logical> <logical> <numeric>
## [1] chr1 856458-856640 * | TRUE FALSE 1
## [2] chr1 868541-868839 * | FALSE TRUE 1
## [3] chr1 1008550-1010075 * | TRUE TRUE 2
## [4] chr1 1014770-1016015 * | TRUE TRUE 2
## [5] chr1 1051307-1051918 * | TRUE TRUE 2
## ... ... ... ... . ... ... ...
## [10] chr1 1608098-1608346 * | TRUE TRUE 2
## [11] chr1 1690460-1690641 * | FALSE TRUE 1
## [12] chr1 1790733-1790975 * | TRUE FALSE 1
## [13] chr1 1878927-1879257 * | TRUE TRUE 2
## [14] chr1 1900588-1900902 * | TRUE FALSE 1
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsHowever, we may wish for peaks to appear in all replicates, so we can set the
argument p = 1 (or any other value \(0 \leq p \leq 1\)).
makeConsensus(peaks, p = 1)## GRanges object with 6 ranges and 3 metadata columns:
## seqnames ranges strand | ER_1 ER_2 n
## <Rle> <IRanges> <Rle> | <logical> <logical> <numeric>
## [1] chr1 1008550-1010075 * | TRUE TRUE 2
## [2] chr1 1014770-1016015 * | TRUE TRUE 2
## [3] chr1 1051307-1051918 * | TRUE TRUE 2
## [4] chr1 1368372-1369009 * | TRUE TRUE 2
## [5] chr1 1608098-1608346 * | TRUE TRUE 2
## [6] chr1 1878927-1879257 * | TRUE TRUE 2
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsIn addition, we may wish to keep the values from the mcols as part of our
consensus peaks, such as the qValue.
This can be specified using the var argument, and will return a
CompressedList as returned by reduceMC(), seen below.
makeConsensus(peaks, p = 1, var = "qValue")## GRanges object with 6 ranges and 4 metadata columns:
## seqnames ranges strand | qValue ER_1 ER_2
## <Rle> <IRanges> <Rle> | <NumericList> <logical> <logical>
## [1] chr1 1008550-1010075 * | 635.748,605.040 TRUE TRUE
## [2] chr1 1014770-1016015 * | 95.1223,78.3953 TRUE TRUE
## [3] chr1 1051307-1051918 * | 65.5538,95.9677 TRUE TRUE
## [4] chr1 1368372-1369009 * | 22.5483,29.1231 TRUE TRUE
## [5] chr1 1608098-1608346 * | 72.3235,64.8439 TRUE TRUE
## [6] chr1 1878927-1879257 * | 33.8900,14.4909 TRUE TRUE
## n
## <numeric>
## [1] 2
## [2] 2
## [3] 2
## [4] 2
## [5] 2
## [6] 2
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsWe could even identify the peak centre and pass these to the set of consensus peaks for downstream peak re-centreing.
library(plyranges)
peaks %>%
endoapply(mutate, centre = start + peak) %>%
makeConsensus(p = 1, var = "centre")## GRanges object with 6 ranges and 4 metadata columns:
## seqnames ranges strand | centre ER_1 ER_2
## <Rle> <IRanges> <Rle> | <NumericList> <logical> <logical>
## [1] chr1 1008550-1010075 * | 1009212,1009212 TRUE TRUE
## [2] chr1 1014770-1016015 * | 1014981,1014949 TRUE TRUE
## [3] chr1 1051307-1051918 * | 1051565,1051634 TRUE TRUE
## [4] chr1 1368372-1369009 * | 1368613,1368767 TRUE TRUE
## [5] chr1 1608098-1608346 * | 1608247,1608247 TRUE TRUE
## [6] chr1 1878927-1879257 * | 1879087,1879094 TRUE TRUE
## n
## <numeric>
## [1] 2
## [2] 2
## [3] 2
## [4] 2
## [5] 2
## [6] 2
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsWe could then find the mean of the peak centres for an averaged centre.
peaks %>%
endoapply(mutate, centre = start + peak) %>%
makeConsensus(p = 1, var = "centre") %>%
mutate(centre = vapply(centre, mean, numeric(1)))## GRanges object with 6 ranges and 4 metadata columns:
## seqnames ranges strand | centre ER_1 ER_2 n
## <Rle> <IRanges> <Rle> | <numeric> <logical> <logical> <numeric>
## [1] chr1 1008550-1010075 * | 1009212 TRUE TRUE 2
## [2] chr1 1014770-1016015 * | 1014965 TRUE TRUE 2
## [3] chr1 1051307-1051918 * | 1051600 TRUE TRUE 2
## [4] chr1 1368372-1369009 * | 1368690 TRUE TRUE 2
## [5] chr1 1608098-1608346 * | 1608247 TRUE TRUE 2
## [6] chr1 1878927-1879257 * | 1879090 TRUE TRUE 2
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths5 Simple Operations Retaining mcols()
The standard set operations implemented in the package GenomicRanges will
always drop the mcols element by default.
The extraChIPs functions reduceMC(), setdiffMC(), intersectMC() and
unionMC() all produce the same output as their similarly-named functions,
however, the mcols() elements in the query object are also returned.
Where required, columns are coerced into CompressedList columns.
This can particularly useful when needed to propagate the information contained
in the initial ranges through to subsequent analytic steps
5.1 Simplifying single GRanges objects
The classical approach to defining TSS regions for a set of transcripts would be
to use the function resize()`, setting the width as 1.
tss <- resize(gr, width = 1)
tss## GRanges object with 3 ranges and 3 metadata columns:
## seqnames ranges strand | gene_id tx_id score
## <Rle> <IRanges> <Rle> | <character> <character> <numeric>
## [1] chr1 1 + | gene1 transcript1 0.4423369
## [2] chr1 5 + | gene1 transcript2 0.0831099
## [3] chr1 5 + | gene1 transcript3 0.6146112
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsAs we can see, two transcripts start at position 5, so we may choose to reduce
this, which would lose the mcols element.
The alternative reduceMC() will retain all mcols.
GenomicRanges::reduce(tss)## GRanges object with 2 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] chr1 1 +
## [2] chr1 5 +
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsreduceMC(tss)## GRanges object with 2 ranges and 3 metadata columns:
## seqnames ranges strand | gene_id tx_id
## <Rle> <IRanges> <Rle> | <character> <CharacterList>
## [1] chr1 1 + | gene1 transcript1
## [2] chr1 5 + | gene1 transcript2,transcript3
## score
## <NumericList>
## [1] 0.442337
## [2] 0.0831099,0.6146112
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsBy default, this function will attempt to coerce mcols to a new mcol of the
appropriate type, however, when multiple values are inevitable such as for the
tx_id column above, these will be coerced to a CompressedList.
The simplification of the multiple values seen in the gene_id can also be
turned off if desired should repeated values be important for downstream
analysis.
reduceMC(tss, simplify = FALSE) ## GRanges object with 2 ranges and 3 metadata columns:
## seqnames ranges strand | gene_id tx_id
## <Rle> <IRanges> <Rle> | <CharacterList> <CharacterList>
## [1] chr1 1 + | gene1 transcript1
## [2] chr1 5 + | gene1,gene1 transcript2,transcript3
## score
## <NumericList>
## [1] 0.442337
## [2] 0.0831099,0.6146112
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsThis allows for simple integration with tidyverse nesting strategies.
reduceMC(tss, simplify = FALSE) %>%
as_tibble() %>%
unnest(everything())## # A tibble: 3 × 4
## range gene_id tx_id score
## <chr> <chr> <chr> <dbl>
## 1 chr1:1:+ gene1 transcript1 0.442
## 2 chr1:5:+ gene1 transcript2 0.0831
## 3 chr1:5:+ gene1 transcript3 0.615Whilst reduceMC relies on the range-reduction as implemented in
GenomicRanges::reduce(), some alternative approaches are included, such as
chopMC(), which finds identical ranges and nests the mcols element as
CompressedList objects.
chopMC(tss)## GRanges object with 2 ranges and 3 metadata columns:
## seqnames ranges strand | gene_id tx_id
## <Rle> <IRanges> <Rle> | <character> <CharacterList>
## [1] chr1 1 + | gene1 transcript1
## [2] chr1 5 + | gene1 transcript2,transcript3
## score
## <NumericList>
## [1] 0.442337
## [2] 0.0831099,0.6146112
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsIn the case of the object tss, this output is identical to reduceMC(),
however, given that there are no identical ranges in gr the two functions
would behave very differently on that object.
A final operation for simplifying GRanges objects would be distinctMC()
which is a wrapper to dplyr]::distinct incorporating both the range and
mcols.
The columns to search can be called using <data-masking> approaches as detailed
in the manual.
distinctMC(tss)## GRanges object with 2 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] chr1 1 +
## [2] chr1 5 +
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsdistinctMC(tss, gene_id)## GRanges object with 2 ranges and 1 metadata column:
## seqnames ranges strand | gene_id
## <Rle> <IRanges> <Rle> | <character>
## [1] chr1 1 + | gene1
## [2] chr1 5 + | gene1
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths5.2 Set Operations with Two GRanges Objects
Whilst reduce/reduceMC is applied to a single GRanges object, the set
operation functions intersect, setdiff and union are valuable approaches
for comparing ranges.
Using the *MC() functions will retain mcols elements from the query range.
peaks <- GRanges(c("chr1:1-5", "chr1:9-12:*"))
peaks$peak_id <- c("peak1", "peak2")
GenomicRanges::intersect(gr, peaks, ignore.strand = TRUE)## GRanges object with 2 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] chr1 1-5 *
## [2] chr1 9-10 *
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsintersectMC(gr, peaks, ignore.strand = TRUE)## GRanges object with 2 ranges and 3 metadata columns:
## seqnames ranges strand | gene_id
## <Rle> <IRanges> <Rle> | <character>
## [1] chr1 1-5 * | gene1
## [2] chr1 9-10 * | gene1
## tx_id score
## <CharacterList> <NumericList>
## [1] transcript1,transcript2,transcript3 0.4423369,0.0831099,0.6146112
## [2] transcript1,transcript2 0.4423369,0.0831099
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthssetdiffMC(gr, peaks, ignore.strand = TRUE)## GRanges object with 1 range and 3 metadata columns:
## seqnames ranges strand | gene_id
## <Rle> <IRanges> <Rle> | <character>
## [1] chr1 6-8 * | gene1
## tx_id score
## <CharacterList> <NumericList>
## [1] transcript1,transcript2,transcript3 0.4423369,0.0831099,0.6146112
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsunionMC(gr, peaks, ignore.strand = TRUE)## GRanges object with 1 range and 3 metadata columns:
## seqnames ranges strand | gene_id
## <Rle> <IRanges> <Rle> | <character>
## [1] chr1 1-12 * | gene1
## tx_id score
## <CharacterList> <NumericList>
## [1] transcript1,transcript2,transcript3 0.4423369,0.0831099,0.6146112
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsThere is a performance overhead to preparation of mcols as CompressedList
objects and all mcols in the query object will be returned.
If only wishing to retain a subset of mcols, these should be selected prior
to passing to these functions.
gr %>%
select(tx_id) %>%
intersectMC(peaks, ignore.strand = TRUE)## GRanges object with 2 ranges and 1 metadata column:
## seqnames ranges strand | tx_id
## <Rle> <IRanges> <Rle> | <CharacterList>
## [1] chr1 1-5 * | transcript1,transcript2,transcript3
## [2] chr1 9-10 * | transcript1,transcript2
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths5.3 Overlapping proportions
Whilst the functions findOverlaps() and overlapsAny() are extremely
useful, the addition of propOverlap() returns a numeric vector with
the proportion of each query range (x) which overlaps any range in the
subject range (y)
propOverlap(gr, peaks)## [1] 0.7 0.5 0.5This is also extended to enable comparison across multiple features and to classify each peak by which features that it overlaps the most strongly.
bestOverlap(gr, peaks, var = "peak_id")## [1] "peak1" "peak2" "peak1"6 More Complex Operations
6.1 Range Partitioning
In addition to standard set operations, one set of ranges can be used to
partition another set of ranges, returning mcols from both ranges.
Ranges from the query range (x) are returned after being partitioned by the
ranges in the subject range (y).
Subject ranges used for partitioning must be non-overlapping, and if
overlapping ranges are provided, these will be reduced prior to partitioning.
This enables the identification of specific ranges from the query range (x)
which overlap ranges from the subject range (y)
Under this approach, mcols from both query and subject ranges will be
returned to enable the clear ranges which are common and distinct within the
two sets of ranges.
partitionRanges(gr, peaks)## GRanges object with 5 ranges and 4 metadata columns:
## seqnames ranges strand | peak_id gene_id
## <Rle> <IRanges> <Rle> | <character> <character>
## [1] chr1 1-5 + | peak1 gene1
## [2] chr1 5 + | peak1 gene1
## [3] chr1 6 + | <NA> gene1
## [4] chr1 6-8 + | <NA> gene1
## [5] chr1 9-10 + | peak2 gene1
## tx_id score
## <CharacterList> <NumericList>
## [1] transcript1 0.442337
## [2] transcript2,transcript3 0.0831099,0.6146112
## [3] transcript3 0.614611
## [4] transcript1,transcript2 0.4423369,0.0831099
## [5] transcript1,transcript2 0.4423369,0.0831099
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsWhilst this shares some similarity with intersectMC() the additional
capabilities provide greater flexibility.
partitionRanges(gr, peaks) %>%
subset(is.na(peak_id))## GRanges object with 2 ranges and 4 metadata columns:
## seqnames ranges strand | peak_id gene_id
## <Rle> <IRanges> <Rle> | <character> <character>
## [1] chr1 6 + | <NA> gene1
## [2] chr1 6-8 + | <NA> gene1
## tx_id score
## <CharacterList> <NumericList>
## [1] transcript3 0.614611
## [2] transcript1,transcript2 0.4423369,0.0831099
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths6.2 Stitching Ranges
Using the function stitchRanges() we are able to join together sets of nearby
ranges, but with the option of placing clear barriers between ranges, across
which ranges cannot be joined.
This may be useful if joining enhancers to form putative super-enhancers, but
explicitly omitting defined promoter regions.
enh <- GRanges(c("chr1:1-10", "chr1:101-110", "chr1:181-200"))
prom <- GRanges("chr1:150:+")
se <- stitchRanges(enh, exclude = prom, maxgap = 100)
se## GRanges object with 2 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] chr1 1-110 *
## [2] chr1 181-200 *
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsAs a visualisation (below) ranges within 100bp were stitched together, however the region defined as a ‘promoter’ acted as a barrier and ranges were not stitched together across this barrier.
7 Session Info
sessionInfo()## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.20-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.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] magrittr_2.0.3 quantro_1.40.0
## [3] here_1.0.1 ggrepel_0.9.6
## [5] glue_1.8.0 scales_1.3.0
## [7] plyranges_1.26.0 extraChIPs_1.10.0
## [9] ggside_0.3.1 patchwork_1.3.0
## [11] edgeR_4.4.0 limma_3.62.0
## [13] rtracklayer_1.66.0 BiocParallel_1.40.0
## [15] csaw_1.40.0 SummarizedExperiment_1.36.0
## [17] Biobase_2.66.0 MatrixGenerics_1.18.0
## [19] matrixStats_1.4.1 Rsamtools_2.22.0
## [21] Biostrings_2.74.0 XVector_0.46.0
## [23] GenomicRanges_1.58.0 GenomeInfoDb_1.42.0
## [25] IRanges_2.40.0 S4Vectors_0.44.0
## [27] BiocGenerics_0.52.0 lubridate_1.9.3
## [29] forcats_1.0.0 stringr_1.5.1
## [31] dplyr_1.1.4 purrr_1.0.2
## [33] readr_2.1.5 tidyr_1.3.1
## [35] tibble_3.2.1 ggplot2_3.5.1
## [37] tidyverse_2.0.0 BiocStyle_2.34.0
##
## loaded via a namespace (and not attached):
## [1] ProtGenerics_1.38.0 bitops_1.0-9
## [3] httr_1.4.7 RColorBrewer_1.1-3
## [5] doParallel_1.0.17 InteractionSet_1.34.0
## [7] tools_4.4.1 doRNG_1.8.6
## [9] backports_1.5.0 utf8_1.2.4
## [11] R6_2.5.1 HDF5Array_1.34.0
## [13] mgcv_1.9-1 lazyeval_0.2.2
## [15] Gviz_1.50.0 rhdf5filters_1.18.0
## [17] withr_3.0.2 prettyunits_1.2.0
## [19] gridExtra_2.3 base64_2.0.2
## [21] VennDiagram_1.7.3 preprocessCore_1.68.0
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## [31] illuminaio_0.48.0 rentrez_1.2.3
## [33] dichromat_2.0-0.1 scrime_1.3.5
## [35] BSgenome_1.74.0 rstudioapi_0.17.1
## [37] RSQLite_2.3.7 generics_0.1.3
## [39] BiocIO_1.16.0 Matrix_1.7-1
## [41] interp_1.1-6 futile.logger_1.4.3
## [43] fansi_1.0.6 abind_1.4-8
## [45] lifecycle_1.0.4 yaml_2.3.10
## [47] rhdf5_2.50.0 SparseArray_1.6.0
## [49] BiocFileCache_2.14.0 grid_4.4.1
## [51] blob_1.2.4 crayon_1.5.3
## [53] lattice_0.22-6 ComplexUpset_1.3.3
## [55] GenomicFeatures_1.58.0 annotate_1.84.0
## [57] KEGGREST_1.46.0 pillar_1.9.0
## [59] knitr_1.48 beanplot_1.3.1
## [61] metapod_1.14.0 rjson_0.2.23
## [63] codetools_0.2-20 data.table_1.16.2
## [65] vctrs_0.6.5 png_0.1-8
## [67] gtable_0.3.6 cachem_1.1.0
## [69] xfun_0.48 S4Arrays_1.6.0
## [71] survival_3.7-0 iterators_1.0.14
## [73] statmod_1.5.0 GenomicInteractions_1.40.0
## [75] nlme_3.1-166 bit64_4.5.2
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## [83] colorspace_2.1-1 DBI_1.2.3
## [85] Hmisc_5.2-0 nnet_7.3-19
## [87] tidyselect_1.2.1 bit_4.5.0
## [89] compiler_4.4.1 curl_5.2.3
## [91] httr2_1.0.5 htmlTable_2.4.3
## [93] xml2_1.3.6 DelayedArray_0.32.0
## [95] bookdown_0.41 checkmate_2.3.2
## [97] quadprog_1.5-8 rappdirs_0.3.3
## [99] digest_0.6.37 rmarkdown_2.28
## [101] GEOquery_2.74.0 htmltools_0.5.8.1
## [103] pkgconfig_2.0.3 jpeg_0.1-10
## [105] base64enc_0.1-3 sparseMatrixStats_1.18.0
## [107] highr_0.11 dbplyr_2.5.0
## [109] fastmap_1.2.0 ensembldb_2.30.0
## [111] rlang_1.1.4 htmlwidgets_1.6.4
## [113] UCSC.utils_1.2.0 DelayedMatrixStats_1.28.0
## [115] farver_2.1.2 jquerylib_0.1.4
## [117] jsonlite_1.8.9 mclust_6.1.1
## [119] VariantAnnotation_1.52.0 RCurl_1.98-1.16
## [121] Formula_1.2-5 GenomeInfoDbData_1.2.13
## [123] Rhdf5lib_1.28.0 munsell_0.5.1
## [125] Rcpp_1.0.13 stringi_1.8.4
## [127] zlibbioc_1.52.0 MASS_7.3-61
## [129] plyr_1.8.9 bumphunter_1.48.0
## [131] minfi_1.52.0 parallel_4.4.1
## [133] deldir_2.0-4 splines_4.4.1
## [135] multtest_2.62.0 hms_1.1.3
## [137] locfit_1.5-9.10 igraph_2.1.1
## [139] rngtools_1.5.2 biomaRt_2.62.0
## [141] futile.options_1.0.1 XML_3.99-0.17
## [143] evaluate_1.0.1 latticeExtra_0.6-30
## [145] biovizBase_1.54.0 lambda.r_1.2.4
## [147] BiocManager_1.30.25 tzdb_0.4.0
## [149] foreach_1.5.2 tweenr_2.0.3
## [151] openssl_2.2.2 polyclip_1.10-7
## [153] reshape_0.8.9 ggforce_0.4.2
## [155] broom_1.0.7 xtable_1.8-4
## [157] restfulr_0.0.15 AnnotationFilter_1.30.0
## [159] memoise_2.0.1 AnnotationDbi_1.68.0
## [161] GenomicAlignments_1.42.0 cluster_2.1.6
## [163] timechange_0.3.0