benchmarks/cumulative_sums.md
In charles-plessy/CAGEr: Analysis of CAGE (Cap Analysis of Gene Expression) sequencing data for precise mapping of transcription start sites and promoterome mining
title: "Cumulative sums"
author: "Charles Plessy"
date: "2023-07-18"
output:
html_document:
keep_md: yes
editor_options:
chunk_output_type: console
Purpose
The function to compute cumulative sums is one of the slowest in CAGEr.
Can we speed it up?
Setup
library("CAGEr") |> suppressPackageStartupMessages()
library("ggplot2") |> suppressPackageStartupMessages()
Example data
(clusters <- tagClustersGR(exampleCAGEexp)[[1]])
## TagClusters object with 532 ranges and 6 metadata columns:
## seqnames ranges strand | score nr_ctss
## <Rle> <IRanges> <Rle> | <Rle> <integer>
## 1 chr17 26050540 + | 22.310089406231 1
## 2 chr17 26391265 + | 22.310089406231 1
## 3 chr17 26446219 + | 22.310089406231 1
## 4 chr17 26453605 + | 44.5738050899834 1
## 5 chr17 26453632-26453708 + | 1023.81202126363 16
## ... ... ... ... . ... ...
## 528 chr17 32706269 + | 22.310089406231 1
## 529 chr17 32706379 + | 66.8200508203776 1
## 530 chr17 32707348-32707353 + | 89.1939839024454 3
## 531 chr17 32708868 + | 22.310089406231 1
## 532 chr17 32708942 + | 22.310089406231 1
## dominant_ctss tpm.dominant_ctss q_0.1 q_0.9
## <integer> <Rle> <Rle> <Rle>
## 1 26050540 22.310089406231 1 1
## 2 26391265 22.310089406231 1 1
## 3 26446219 22.310089406231 1 1
## 4 26453605 44.5738050899834 1 1
## 5 26453667 288.917306039892 30 72
## ... ... ... ... ...
## 528 32706269 22.310089406231 1 1
## 529 32706379 66.8200508203776 1 1
## 530 32707353 44.5738050899834 1 6
## 531 32708868 22.310089406231 1 1
## 532 32708942 22.310089406231 1 1
## -------
## seqinfo: 26 sequences (1 circular) from danRer7 genome
(ctss <- CTSSnormalizedTpmGR(exampleCAGEexp, 1))
## CTSS object with 1697 positions and 6 metadata columns:
## seqnames pos strand | genes annotation exprClass
## <Rle> <integer> <Rle> | <Rle> <Rle> <Rle>
## [1] chr17 26050540 + | grid1a promoter 4_4
## [2] chr17 26391265 + | si:ch73-34j14.2 exon 0_0
## [3] chr17 26446219 + | unknown 4_4
## [4] chr17 26453605 + | promoter 4_3
## [5] chr17 26453632 + | promoter 2_1
## ... ... ... ... . ... ... ...
## [1693] chr17 32707348 + | ywhaqb intron 4_4
## [1694] chr17 32707350 + | ywhaqb intron 4_4
## [1695] chr17 32707353 + | ywhaqb intron 4_4
## [1696] chr17 32708868 + | ywhaqb exon 0_2
## [1697] chr17 32708942 + | ywhaqb exon 4_4
## filteredCTSSidx cluster score
## <Rle> <Rle> <Rle>
## [1] TRUE chr17:26050540:+ 22.310089406231
## [2] TRUE chr17:26391265-26391.. 22.310089406231
## [3] TRUE chr17:26446219:+ 22.310089406231
## [4] TRUE chr17:26453603-26453.. 44.5738050899834
## [5] TRUE chr17:26453603-26453.. 44.5738050899834
## ... ... ... ...
## [1693] TRUE chr17:32707322-32707.. 22.310089406231
## [1694] TRUE chr17:32707322-32707.. 22.310089406231
## [1695] TRUE chr17:32707322-32707.. 44.5738050899834
## [1696] TRUE chr17:32708847-32708.. 22.310089406231
## [1697] TRUE chr17:32708847-32708.. 22.310089406231
## -------
## seqinfo: 26 sequences (1 circular) from danRer7 genome
## BSgenome name: BSgenome.Drerio.UCSC.danRer7
Current implementation at the time the benchmark was written.
cumsums_viewApply <- function(ctss, clusters) {
cov <- Rle(rep(0, max(end(clusters), end(ctss))))
cov[start(ctss)] <- score(ctss)
cluster.cumsums <- Views(cov, start = start(clusters), end = end(clusters))
viewApply(cluster.cumsums, cumsum)
}
cumsums_viewApply_res <- cumsums_viewApply(ctss, clusters)
head(cumsums_viewApply_res)
## [[1]]
## numeric-Rle of length 1 with 1 run
## Lengths: 1
## Values : 22.3101
##
## [[2]]
## numeric-Rle of length 1 with 1 run
## Lengths: 1
## Values : 22.3101
##
## [[3]]
## numeric-Rle of length 1 with 1 run
## Lengths: 1
## Values : 22.3101
##
## [[4]]
## numeric-Rle of length 1 with 1 run
## Lengths: 1
## Values : 44.5738
##
## [[5]]
## numeric-Rle of length 77 with 16 runs
## Lengths: 11 4 14 ... 2 5 1
## Values : 44.5738 66.8839 89.1940 ... 912.4182 979.2382 1023.8120
##
## [[6]]
## numeric-Rle of length 1 with 1 run
## Lengths: 1
## Values : 22.3101
Alternative functions.
cumsums_extractList_sapply is a reduced version of the bioC2_cc_iqw currently
used in consensusClustersGR.
cumsums_extractList_sapply <- function(ctss, clusters) {
# Fill gaps with zeros
fill <- GPos(clusters)
score(fill) <- Rle(0L)
ctss <- c(ctss, fill[!fill %in% ctss]) |> sort()
o <- findOverlaps(query = clusters, subject = ctss)
grouped_scores <- extractList(score(ctss), o)
sapply(grouped_scores, cumsum)
}
cumsums_extractList_sapply_res <- cumsums_extractList_sapply(ctss, clusters)
cumsums_extractList_aslist <- function(ctss, clusters) {
# Fill gaps with zeros
fill <- GPos(clusters)
score(fill) <- Rle(0L)
ctss <- c(ctss, fill[!fill %in% ctss]) |> sort()
o <- findOverlaps(query = clusters, subject = ctss)
grouped_scores <- extractList(score(ctss), o)
cumsum(grouped_scores) |> as.list()
}
cumsums_extractList_aslist_res <- cumsums_extractList_aslist(ctss, clusters)
cumsums_extractList_RleList <- function(ctss, clusters) {
# Fill gaps with zeros
fill <- GPos(clusters)
score(fill) <- Rle(0L)
ctss <- c(ctss, fill[!fill %in% ctss]) |> sort()
o <- findOverlaps(query = clusters, subject = ctss)
grouped_scores <- extractList(score(ctss), o)
cumsum(grouped_scores)
}
cumsums_extractList_RleList_res <- cumsums_extractList_RleList(ctss, clusters)
The results are almost the same. The differences appear to be rounding errors.
# Identical
identical(cumsums_viewApply_res[[3]], cumsums_extractList_sapply_res[[3]])
## [1] TRUE
cumsums_viewApply_res[[3]] - cumsums_extractList_sapply_res[[3]]
## numeric-Rle of length 1 with 1 run
## Lengths: 1
## Values : 0
identical(cumsums_viewApply_res, cumsums_extractList_sapply_res)
## [1] TRUE
# Slightly different
identical(cumsums_viewApply_res[[3]], cumsums_extractList_RleList_res[[3]])
## [1] FALSE
cumsums_viewApply_res[[3]] - cumsums_extractList_aslist_res[[3]]
## numeric-Rle of length 1 with 1 run
## Lengths: 1
## Values : 3.55271e-15
# Identical
identical(cumsums_extractList_RleList_res[[3]], cumsums_extractList_aslist_res[[3]])
## [1] TRUE
Benchmark
(microbench_out <- microbenchmark::microbenchmark(
times = 100,
viewApply = cumsums_viewApply(ctss, clusters),
extractList_sapply = cumsums_extractList_sapply(ctss, clusters),
extractList_lapply = cumsums_extractList_aslist(ctss, clusters),
extractList_RleList = cumsums_extractList_RleList(ctss, clusters)
))
## Unit: milliseconds
## expr min lq mean median uq max
## viewApply 892.9003 923.1840 1170.8610 976.8460 1697.3491 1776.6821
## extractList_sapply 760.2620 782.2829 843.7974 801.2297 835.7868 1683.2709
## extractList_lapply 699.5029 718.5011 776.4053 734.9823 758.7260 1603.3350
## extractList_RleList 122.8387 125.9715 138.9411 129.3399 131.6845 981.3279
## neval cld
## 100 c
## 100 b
## 100 b
## 100 a
# https://statisticsglobe.com/microbenchmark-package-r
ggplot(microbench_out, aes(x = time / 1e6, y = expr, color = expr)) + # Plot performance comparison
geom_boxplot() +
scale_x_log10("time (milliseconds)")
Result
The winner is about 10 times faster!
sessionInfo()
## R version 4.2.2 Patched (2022-11-10 r83330)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Debian GNU/Linux 12 (bookworm)
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.11.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.11.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [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
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] ggplot2_3.4.2 CAGEr_2.7.1
## [3] MultiAssayExperiment_1.24.0 SummarizedExperiment_1.28.0
## [5] Biobase_2.58.0 GenomicRanges_1.50.0
## [7] GenomeInfoDb_1.34.0 IRanges_2.32.0
## [9] S4Vectors_0.36.0 BiocGenerics_0.44.0
## [11] MatrixGenerics_1.10.0 matrixStats_0.62.0
##
## loaded via a namespace (and not attached):
## [1] backports_1.4.1 Hmisc_4.7-1 VGAM_1.1-7
## [4] BiocFileCache_2.6.0 plyr_1.8.7 lazyeval_0.2.2
## [7] splines_4.2.2 operator.tools_1.6.3 BiocParallel_1.32.0
## [10] TH.data_1.1-1 digest_0.6.30 ensembldb_2.22.0
## [13] htmltools_0.5.3 fansi_1.0.4 magrittr_2.0.3
## [16] checkmate_2.1.0 memoise_2.0.1 BSgenome_1.66.0
## [19] cluster_2.1.4 Biostrings_2.66.0 formula.tools_1.7.1
## [22] sandwich_3.0-2 prettyunits_1.1.1 jpeg_0.1-9
## [25] colorspace_2.1-0 blob_1.2.3 rappdirs_0.3.3
## [28] xfun_0.34 dplyr_1.0.10 crayon_1.5.2
## [31] RCurl_1.98-1.9 microbenchmark_1.4.9 jsonlite_1.8.4
## [34] zoo_1.8-11 survival_3.5-3 VariantAnnotation_1.44.0
## [37] glue_1.6.2 gtable_0.3.3 zlibbioc_1.44.0
## [40] XVector_0.38.0 DelayedArray_0.24.0 scales_1.2.1
## [43] mvtnorm_1.1-3 DBI_1.1.3 som_0.3-5.1
## [46] Rcpp_1.0.9 progress_1.2.2 htmlTable_2.4.1
## [49] foreign_0.8-83 bit_4.0.4 Formula_1.2-4
## [52] htmlwidgets_1.5.4 httr_1.4.4 RColorBrewer_1.1-3
## [55] ellipsis_0.3.2 farver_2.1.1 pkgconfig_2.0.3
## [58] XML_3.99-0.12 Gviz_1.42.0 nnet_7.3-18
## [61] sass_0.4.2 dbplyr_2.2.1 deldir_1.0-6
## [64] utf8_1.2.3 tidyselect_1.2.0 rlang_1.1.0
## [67] reshape2_1.4.4 AnnotationDbi_1.60.0 munsell_0.5.0
## [70] tools_4.2.2 cachem_1.0.6 cli_3.6.1
## [73] generics_0.1.3 RSQLite_2.2.18 CAGEfightR_1.18.0
## [76] evaluate_0.17 stringr_1.4.1 fastmap_1.1.0
## [79] yaml_2.3.6 knitr_1.40 bit64_4.0.5
## [82] KEGGREST_1.38.0 AnnotationFilter_1.22.0 nlme_3.1-160
## [85] xml2_1.3.3 biomaRt_2.54.0 compiler_4.2.2
## [88] rstudioapi_0.14 filelock_1.0.2 curl_4.3.3
## [91] png_0.1-7 tibble_3.2.1 bslib_0.4.1
## [94] stringi_1.7.12 highr_0.9 GenomicFeatures_1.50.1
## [97] lattice_0.20-45 ProtGenerics_1.30.0 Matrix_1.5-3
## [100] vegan_2.6-4 permute_0.9-7 vctrs_0.6.1
## [103] stringdist_0.9.9 pillar_1.9.0 lifecycle_1.0.3
## [106] jquerylib_0.1.4 data.table_1.14.4 bitops_1.0-7
## [109] rtracklayer_1.58.0 R6_2.5.1 BiocIO_1.8.0
## [112] latticeExtra_0.6-30 KernSmooth_2.23-20 gridExtra_2.3
## [115] codetools_0.2-19 dichromat_2.0-0.1 MASS_7.3-58.3
## [118] gtools_3.9.3 assertthat_0.2.1 rjson_0.2.21
## [121] withr_2.5.0 GenomicAlignments_1.34.0 Rsamtools_2.14.0
## [124] multcomp_1.4-20 GenomeInfoDbData_1.2.9 mgcv_1.8-41
## [127] parallel_4.2.2 hms_1.1.2 grid_4.2.2
## [130] rpart_4.1.19 rmarkdown_2.17 biovizBase_1.46.0
## [133] base64enc_0.1-3 interp_1.1-3 restfulr_0.0.15
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title: "Cumulative sums" author: "Charles Plessy" date: "2023-07-18" output: html_document: keep_md: yes editor_options: chunk_output_type: console
Purpose
The function to compute cumulative sums is one of the slowest in CAGEr. Can we speed it up?
Setup
library("CAGEr") |> suppressPackageStartupMessages()
library("ggplot2") |> suppressPackageStartupMessages()
Example data
(clusters <- tagClustersGR(exampleCAGEexp)[[1]])
## TagClusters object with 532 ranges and 6 metadata columns:
## seqnames ranges strand | score nr_ctss
## <Rle> <IRanges> <Rle> | <Rle> <integer>
## 1 chr17 26050540 + | 22.310089406231 1
## 2 chr17 26391265 + | 22.310089406231 1
## 3 chr17 26446219 + | 22.310089406231 1
## 4 chr17 26453605 + | 44.5738050899834 1
## 5 chr17 26453632-26453708 + | 1023.81202126363 16
## ... ... ... ... . ... ...
## 528 chr17 32706269 + | 22.310089406231 1
## 529 chr17 32706379 + | 66.8200508203776 1
## 530 chr17 32707348-32707353 + | 89.1939839024454 3
## 531 chr17 32708868 + | 22.310089406231 1
## 532 chr17 32708942 + | 22.310089406231 1
## dominant_ctss tpm.dominant_ctss q_0.1 q_0.9
## <integer> <Rle> <Rle> <Rle>
## 1 26050540 22.310089406231 1 1
## 2 26391265 22.310089406231 1 1
## 3 26446219 22.310089406231 1 1
## 4 26453605 44.5738050899834 1 1
## 5 26453667 288.917306039892 30 72
## ... ... ... ... ...
## 528 32706269 22.310089406231 1 1
## 529 32706379 66.8200508203776 1 1
## 530 32707353 44.5738050899834 1 6
## 531 32708868 22.310089406231 1 1
## 532 32708942 22.310089406231 1 1
## -------
## seqinfo: 26 sequences (1 circular) from danRer7 genome
(ctss <- CTSSnormalizedTpmGR(exampleCAGEexp, 1))
## CTSS object with 1697 positions and 6 metadata columns:
## seqnames pos strand | genes annotation exprClass
## <Rle> <integer> <Rle> | <Rle> <Rle> <Rle>
## [1] chr17 26050540 + | grid1a promoter 4_4
## [2] chr17 26391265 + | si:ch73-34j14.2 exon 0_0
## [3] chr17 26446219 + | unknown 4_4
## [4] chr17 26453605 + | promoter 4_3
## [5] chr17 26453632 + | promoter 2_1
## ... ... ... ... . ... ... ...
## [1693] chr17 32707348 + | ywhaqb intron 4_4
## [1694] chr17 32707350 + | ywhaqb intron 4_4
## [1695] chr17 32707353 + | ywhaqb intron 4_4
## [1696] chr17 32708868 + | ywhaqb exon 0_2
## [1697] chr17 32708942 + | ywhaqb exon 4_4
## filteredCTSSidx cluster score
## <Rle> <Rle> <Rle>
## [1] TRUE chr17:26050540:+ 22.310089406231
## [2] TRUE chr17:26391265-26391.. 22.310089406231
## [3] TRUE chr17:26446219:+ 22.310089406231
## [4] TRUE chr17:26453603-26453.. 44.5738050899834
## [5] TRUE chr17:26453603-26453.. 44.5738050899834
## ... ... ... ...
## [1693] TRUE chr17:32707322-32707.. 22.310089406231
## [1694] TRUE chr17:32707322-32707.. 22.310089406231
## [1695] TRUE chr17:32707322-32707.. 44.5738050899834
## [1696] TRUE chr17:32708847-32708.. 22.310089406231
## [1697] TRUE chr17:32708847-32708.. 22.310089406231
## -------
## seqinfo: 26 sequences (1 circular) from danRer7 genome
## BSgenome name: BSgenome.Drerio.UCSC.danRer7
Current implementation at the time the benchmark was written.
cumsums_viewApply <- function(ctss, clusters) {
cov <- Rle(rep(0, max(end(clusters), end(ctss))))
cov[start(ctss)] <- score(ctss)
cluster.cumsums <- Views(cov, start = start(clusters), end = end(clusters))
viewApply(cluster.cumsums, cumsum)
}
cumsums_viewApply_res <- cumsums_viewApply(ctss, clusters)
head(cumsums_viewApply_res)
## [[1]]
## numeric-Rle of length 1 with 1 run
## Lengths: 1
## Values : 22.3101
##
## [[2]]
## numeric-Rle of length 1 with 1 run
## Lengths: 1
## Values : 22.3101
##
## [[3]]
## numeric-Rle of length 1 with 1 run
## Lengths: 1
## Values : 22.3101
##
## [[4]]
## numeric-Rle of length 1 with 1 run
## Lengths: 1
## Values : 44.5738
##
## [[5]]
## numeric-Rle of length 77 with 16 runs
## Lengths: 11 4 14 ... 2 5 1
## Values : 44.5738 66.8839 89.1940 ... 912.4182 979.2382 1023.8120
##
## [[6]]
## numeric-Rle of length 1 with 1 run
## Lengths: 1
## Values : 22.3101
Alternative functions.
cumsums_extractList_sapply is a reduced version of the bioC2_cc_iqw currently
used in consensusClustersGR.
cumsums_extractList_sapply <- function(ctss, clusters) {
# Fill gaps with zeros
fill <- GPos(clusters)
score(fill) <- Rle(0L)
ctss <- c(ctss, fill[!fill %in% ctss]) |> sort()
o <- findOverlaps(query = clusters, subject = ctss)
grouped_scores <- extractList(score(ctss), o)
sapply(grouped_scores, cumsum)
}
cumsums_extractList_sapply_res <- cumsums_extractList_sapply(ctss, clusters)
cumsums_extractList_aslist <- function(ctss, clusters) {
# Fill gaps with zeros
fill <- GPos(clusters)
score(fill) <- Rle(0L)
ctss <- c(ctss, fill[!fill %in% ctss]) |> sort()
o <- findOverlaps(query = clusters, subject = ctss)
grouped_scores <- extractList(score(ctss), o)
cumsum(grouped_scores) |> as.list()
}
cumsums_extractList_aslist_res <- cumsums_extractList_aslist(ctss, clusters)
cumsums_extractList_RleList <- function(ctss, clusters) {
# Fill gaps with zeros
fill <- GPos(clusters)
score(fill) <- Rle(0L)
ctss <- c(ctss, fill[!fill %in% ctss]) |> sort()
o <- findOverlaps(query = clusters, subject = ctss)
grouped_scores <- extractList(score(ctss), o)
cumsum(grouped_scores)
}
cumsums_extractList_RleList_res <- cumsums_extractList_RleList(ctss, clusters)
The results are almost the same. The differences appear to be rounding errors.
# Identical
identical(cumsums_viewApply_res[[3]], cumsums_extractList_sapply_res[[3]])
## [1] TRUE
cumsums_viewApply_res[[3]] - cumsums_extractList_sapply_res[[3]]
## numeric-Rle of length 1 with 1 run
## Lengths: 1
## Values : 0
identical(cumsums_viewApply_res, cumsums_extractList_sapply_res)
## [1] TRUE
# Slightly different
identical(cumsums_viewApply_res[[3]], cumsums_extractList_RleList_res[[3]])
## [1] FALSE
cumsums_viewApply_res[[3]] - cumsums_extractList_aslist_res[[3]]
## numeric-Rle of length 1 with 1 run
## Lengths: 1
## Values : 3.55271e-15
# Identical
identical(cumsums_extractList_RleList_res[[3]], cumsums_extractList_aslist_res[[3]])
## [1] TRUE
Benchmark
(microbench_out <- microbenchmark::microbenchmark(
times = 100,
viewApply = cumsums_viewApply(ctss, clusters),
extractList_sapply = cumsums_extractList_sapply(ctss, clusters),
extractList_lapply = cumsums_extractList_aslist(ctss, clusters),
extractList_RleList = cumsums_extractList_RleList(ctss, clusters)
))
## Unit: milliseconds
## expr min lq mean median uq max
## viewApply 892.9003 923.1840 1170.8610 976.8460 1697.3491 1776.6821
## extractList_sapply 760.2620 782.2829 843.7974 801.2297 835.7868 1683.2709
## extractList_lapply 699.5029 718.5011 776.4053 734.9823 758.7260 1603.3350
## extractList_RleList 122.8387 125.9715 138.9411 129.3399 131.6845 981.3279
## neval cld
## 100 c
## 100 b
## 100 b
## 100 a
# https://statisticsglobe.com/microbenchmark-package-r
ggplot(microbench_out, aes(x = time / 1e6, y = expr, color = expr)) + # Plot performance comparison
geom_boxplot() +
scale_x_log10("time (milliseconds)")
Result
The winner is about 10 times faster!
sessionInfo()
## R version 4.2.2 Patched (2022-11-10 r83330)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Debian GNU/Linux 12 (bookworm)
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.11.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.11.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [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
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] ggplot2_3.4.2 CAGEr_2.7.1
## [3] MultiAssayExperiment_1.24.0 SummarizedExperiment_1.28.0
## [5] Biobase_2.58.0 GenomicRanges_1.50.0
## [7] GenomeInfoDb_1.34.0 IRanges_2.32.0
## [9] S4Vectors_0.36.0 BiocGenerics_0.44.0
## [11] MatrixGenerics_1.10.0 matrixStats_0.62.0
##
## loaded via a namespace (and not attached):
## [1] backports_1.4.1 Hmisc_4.7-1 VGAM_1.1-7
## [4] BiocFileCache_2.6.0 plyr_1.8.7 lazyeval_0.2.2
## [7] splines_4.2.2 operator.tools_1.6.3 BiocParallel_1.32.0
## [10] TH.data_1.1-1 digest_0.6.30 ensembldb_2.22.0
## [13] htmltools_0.5.3 fansi_1.0.4 magrittr_2.0.3
## [16] checkmate_2.1.0 memoise_2.0.1 BSgenome_1.66.0
## [19] cluster_2.1.4 Biostrings_2.66.0 formula.tools_1.7.1
## [22] sandwich_3.0-2 prettyunits_1.1.1 jpeg_0.1-9
## [25] colorspace_2.1-0 blob_1.2.3 rappdirs_0.3.3
## [28] xfun_0.34 dplyr_1.0.10 crayon_1.5.2
## [31] RCurl_1.98-1.9 microbenchmark_1.4.9 jsonlite_1.8.4
## [34] zoo_1.8-11 survival_3.5-3 VariantAnnotation_1.44.0
## [37] glue_1.6.2 gtable_0.3.3 zlibbioc_1.44.0
## [40] XVector_0.38.0 DelayedArray_0.24.0 scales_1.2.1
## [43] mvtnorm_1.1-3 DBI_1.1.3 som_0.3-5.1
## [46] Rcpp_1.0.9 progress_1.2.2 htmlTable_2.4.1
## [49] foreign_0.8-83 bit_4.0.4 Formula_1.2-4
## [52] htmlwidgets_1.5.4 httr_1.4.4 RColorBrewer_1.1-3
## [55] ellipsis_0.3.2 farver_2.1.1 pkgconfig_2.0.3
## [58] XML_3.99-0.12 Gviz_1.42.0 nnet_7.3-18
## [61] sass_0.4.2 dbplyr_2.2.1 deldir_1.0-6
## [64] utf8_1.2.3 tidyselect_1.2.0 rlang_1.1.0
## [67] reshape2_1.4.4 AnnotationDbi_1.60.0 munsell_0.5.0
## [70] tools_4.2.2 cachem_1.0.6 cli_3.6.1
## [73] generics_0.1.3 RSQLite_2.2.18 CAGEfightR_1.18.0
## [76] evaluate_0.17 stringr_1.4.1 fastmap_1.1.0
## [79] yaml_2.3.6 knitr_1.40 bit64_4.0.5
## [82] KEGGREST_1.38.0 AnnotationFilter_1.22.0 nlme_3.1-160
## [85] xml2_1.3.3 biomaRt_2.54.0 compiler_4.2.2
## [88] rstudioapi_0.14 filelock_1.0.2 curl_4.3.3
## [91] png_0.1-7 tibble_3.2.1 bslib_0.4.1
## [94] stringi_1.7.12 highr_0.9 GenomicFeatures_1.50.1
## [97] lattice_0.20-45 ProtGenerics_1.30.0 Matrix_1.5-3
## [100] vegan_2.6-4 permute_0.9-7 vctrs_0.6.1
## [103] stringdist_0.9.9 pillar_1.9.0 lifecycle_1.0.3
## [106] jquerylib_0.1.4 data.table_1.14.4 bitops_1.0-7
## [109] rtracklayer_1.58.0 R6_2.5.1 BiocIO_1.8.0
## [112] latticeExtra_0.6-30 KernSmooth_2.23-20 gridExtra_2.3
## [115] codetools_0.2-19 dichromat_2.0-0.1 MASS_7.3-58.3
## [118] gtools_3.9.3 assertthat_0.2.1 rjson_0.2.21
## [121] withr_2.5.0 GenomicAlignments_1.34.0 Rsamtools_2.14.0
## [124] multcomp_1.4-20 GenomeInfoDbData_1.2.9 mgcv_1.8-41
## [127] parallel_4.2.2 hms_1.1.2 grid_4.2.2
## [130] rpart_4.1.19 rmarkdown_2.17 biovizBase_1.46.0
## [133] base64enc_0.1-3 interp_1.1-3 restfulr_0.0.15
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