benchmarks/paraclcu.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: "Paraclu benchmark"
author: "Charles Plessy"
date: "2023-07-14"
output:
html_document:
keep_md: yes
editor_options:
chunk_output_type: console
Purpose
While refactoring the Paraclu code, I wondered if some data structures would be
more efficient than data.frames.
Example data
Let's focus on the clustering of positions with scores from one strand of one
chromosome, because this is where the algorithm is recursive and hard to
parallelise.
In the original implementation the input in two columns of a data.frame. Here
I also try a Pairs object, or just two separate arguments for the positions
and scores.
ctss <- CTSSnormalizedTpmGR(exampleCAGEexp,1)
isSorted(ctss)
## [1] TRUE
ctss.df <- as.data.frame(granges(ctss))
ctss.df$tpm <- decode(score(ctss))
ctss.df$pos <- ctss.df$start
ctss.df$end <- ctss.df$start <- ctss.df$width <- NULL
pair <- Pairs(pos(ctss), decode(score(ctss)))
DF <- DataFrame(pos = pos(ctss), score = decode(score(ctss)))
l <- list(pos = pos(ctss), score = decode(score(ctss)))
The paraclu parameters function
The main recursive functions call a subfunction that compute the density and
the next break point. First, let's see if it can be optimised.
paraclu_params_df was originally called .paraclu1. The other functions
are rewrites.
paraclu_params_df <- function(ctss) {
sit <- nrow(ctss)
tot <- sum(ctss$tpm)
if(sit == 1) {
min_density <- Inf
br <- NA
}else{
densities_forward <- cumsum(ctss$tpm)[-sit]/(ctss$pos[2:sit] - ctss$pos[1])
densities_reverse <- cumsum(rev(ctss$tpm))[-sit]/(ctss$pos[sit] - ctss$pos[(sit-1):1])
min_densities = c(min(densities_forward), min(densities_reverse))
breaks <- c(which(densities_forward == min_densities[1])[1] + 1, sit + 1 - which(densities_reverse == min_densities[2])[1])
min_density <- min(min_densities)
br <- breaks[tail(which(min_densities == min_density),1)]
}
list(br = br, min_density = min_density, tot = tot, sit = sit)
}
paraclu_params_df(ctss.df)
## $br
## [1] 2
##
## $min_density
## [1] 6.547829e-05
##
## $tot
## [1] 220525.6
##
## $sit
## [1] 1697
paraclu_params_Pairs <- function(pair) {
sit <- length(pair)
score <- second(pair)
tot <- sum(score)
if(sit == 1) return(list(br = NA, min_density = Inf, tot = tot, sit = sit))
pos <- first(pair)
densities_forward <- cumsum( score) [-sit] / (pos[2:sit] - pos[1] )
densities_reverse <- cumsum(rev(score))[-sit] / (pos[sit] - pos[(sit-1):1])
min_densities = c(min(densities_forward), min(densities_reverse))
breaks <- c( 1 + which(densities_forward == min_densities[1])[1]
, sit + 1 - which(densities_reverse == min_densities[2])[1])
min_density <- min(min_densities)
br <- breaks[tail(which(min_densities == min_density),1)]
list(br = br, min_density = min_density, tot = tot, sit = sit)
}
identical(paraclu_params_Pairs(pair), paraclu_params_df(ctss.df))
## [1] TRUE
paraclu_params_DF <- function(DF) {
sit <- nrow(DF)
tot <- sum(DF$score)
if(sit == 1) return(list(br = NA, min_density = Inf, tot = tot, sit = sit))
densities_forward <- cumsum( DF$score) [-sit] / (DF$pos[2:sit] - DF$pos[1] )
densities_reverse <- cumsum(rev(DF$score))[-sit] / (DF$pos[sit] - DF$pos[(sit-1):1])
min_densities = c(min(densities_forward), min(densities_reverse))
breaks <- c( 1 + which(densities_forward == min_densities[1])[1]
, sit + 1 - which(densities_reverse == min_densities[2])[1])
min_density <- min(min_densities)
br <- breaks[tail(which(min_densities == min_density),1)]
list(br = br, min_density = min_density, tot = tot, sit = sit)
}
identical(paraclu_params_DF(DF), paraclu_params_df(ctss.df))
## [1] TRUE
paraclu_params_twoargs <- function(pos, score) {
sit <- length(pos)
tot <- sum(score)
if(sit == 1) return(list(br = NA, min_density = Inf, tot = tot, sit = sit))
densities_forward <- cumsum( score) [-sit] / (pos[2:sit] - pos[1] )
densities_reverse <- cumsum(rev(score))[-sit] / (pos[sit] - pos[(sit-1):1])
min_densities = c(min(densities_forward), min(densities_reverse))
breaks <- c( 1 + which(densities_forward == min_densities[1])[1]
, sit + 1 - which(densities_reverse == min_densities[2])[1])
min_density <- min(min_densities)
br <- breaks[tail(which(min_densities == min_density),1)]
list(br = br, min_density = min_density, tot = tot, sit = sit)
}
identical(paraclu_params_twoargs(l$pos, l$score), paraclu_params_df(ctss.df))
## [1] TRUE
microbenchmark::microbenchmark(
data.frame = paraclu_params_df(ctss.df),
Pairs = paraclu_params_Pairs(pair),
DataFrame = paraclu_params_DF(DF),
two_args = paraclu_params_twoargs(l$pos, l$score),
times = 1000)
## Unit: microseconds
## expr min lq mean median uq max neval
## data.frame 88.760 106.4695 151.6922 117.5020 149.7100 6851.252 1000
## Pairs 190.730 223.2810 313.0306 248.9615 310.5180 9349.667 1000
## DataFrame 3952.067 4382.7235 4808.1692 4657.3280 5071.8605 12485.169 1000
## two_args 79.675 90.0480 130.7938 99.4555 123.4375 7440.558 1000
## cld
## a
## b
## c
## a
All functions are microsecond-fast except the DF one. Let's use the two-
arguments version in all the benchmarks below.
Recursive Paraclu function
The original recursive function was originally called .paraclu2. It computes
extra information like dominant peak etc, which is nice but we have a good
function to do that a posteriori. Therefore we will compare
implementations that skip that calculation.
# Originally called .paraclu2
# Minor changes applied to keep ctss.df colnames.
# Removed re-sorting of output
paraclu_orig <- function(ctss, min_density = -Inf, clusters.df = data.frame()) {
if(nrow(ctss)>0){
params <- paraclu_params_twoargs(ctss$pos, ctss$tpm)
br <- params[[1]]
max_density <- params[[2]]
tot <- params[[3]]
sit <- params[[4]]
if(!(max_density == Inf)){
new_min <- max(min_density, max_density)
clusters.df <- rbind(paraclu_orig(ctss = ctss[1:(br-1),], min_density = new_min, clusters.df = clusters.df),
paraclu_orig(ctss = ctss[br:nrow(ctss),], min_density = new_min, clusters.df = clusters.df))
}
return(rbind(clusters.df, data.frame(seqnames = ctss$seqnames[1],
start = min(ctss$pos),
end = max(ctss$pos),
strand = ctss$strand[1],
nr_ctss = sit,
dominant_ctss = ctss$pos[which(ctss$tpm == max(ctss$tpm))[ceiling(length(which(ctss$tpm == max(ctss$tpm)))/2)]],
tpm = tot,
tpm.dominant_ctss = ctss$tpm[which(ctss$tpm == max(ctss$tpm))[ceiling(length(which(ctss$tpm == max(ctss$tpm)))/2)]],
min_d = min_density, max_d= max_density)
)
)
}else{
return(clusters.df)
}
}
paraclu_orig(ctss.df) |> head()
## seqnames start end strand nr_ctss dominant_ctss tpm
## 1 chr17 26050540 26050540 + 1 26050540 22.31009
## 2 chr17 26391265 26391265 + 1 26391265 22.31009
## 3 chr17 26446219 26446219 + 1 26446219 22.31009
## 4 chr17 26453605 26453605 + 1 26453605 44.57381
## 5 chr17 26453632 26453632 + 1 26453632 44.57381
## 6 chr17 26453643 26453643 + 1 26453643 22.31009
## tpm.dominant_ctss min_d max_d
## 1 22.31009 6.547829e-05 Inf
## 2 22.31009 4.059775e-04 Inf
## 3 22.31009 3.020592e-03 Inf
## 4 44.57381 1.650882e+00 Inf
## 5 44.57381 3.075655e+00 Inf
## 6 22.31009 5.577522e+00 Inf
paraclu_orig_simpler_output <- function(ctss, min_density = -Inf, clusters.df = data.frame()) {
if(nrow(ctss)>0){
params <- paraclu_params_twoargs(ctss$pos, ctss$tpm)
br <- params[[1]]
max_density <- params[[2]]
tot <- params[[3]]
sit <- params[[4]]
if(!(max_density == Inf)){
new_min <- max(min_density, max_density)
clusters.df <- rbind(paraclu_orig_simpler_output(ctss = ctss[1:(br-1),], min_density = new_min, clusters.df = clusters.df),
paraclu_orig_simpler_output(ctss = ctss[br:nrow(ctss),], min_density = new_min, clusters.df = clusters.df))
}
return(rbind(clusters.df, data.frame(seqnames = ctss$seqnames[1],
start = min(ctss$pos),
end = max(ctss$pos),
min_d = min_density, max_d= max_density)
)
)
}else{
return(clusters.df)
}
}
paraclu_orig_simpler_output(ctss.df) |> head()
## seqnames start end min_d max_d
## 1 chr17 26050540 26050540 6.547829e-05 Inf
## 2 chr17 26391265 26391265 4.059775e-04 Inf
## 3 chr17 26446219 26446219 3.020592e-03 Inf
## 4 chr17 26453605 26453605 1.650882e+00 Inf
## 5 chr17 26453632 26453632 3.075655e+00 Inf
## 6 chr17 26453643 26453643 5.577522e+00 Inf
Representing the clusters in the IRanges class looks elegant. But is it
efficient? Also, since we need only two vectors, how about keeping them
together as a pair?
paraclu_Pair_IRanges <- function(pair, min_density = -Inf, clusters = IRanges()) {
params <- paraclu_params_twoargs(first(pair), second(pair))
if (!is.na(params$br)) {
new_min <- max(min_density, params$min_density)
clusters <- c(paraclu_Pair_IRanges(pair[ 1 : (params$br-1)], new_min, clusters),
paraclu_Pair_IRanges(pair[params$br : length(pair)], new_min, clusters))
}
c( clusters
, IRanges( start = min(first(pair))
, end = max(first(pair))
, min_d = min_density
, max_d = params$min_density))
}
paraclu_Pair_IRanges(pair)
## IRanges object with 3393 ranges and 2 metadata columns:
## start end width | min_d max_d
## <integer> <integer> <integer> | <numeric> <numeric>
## [1] 26050540 26050540 1 | 6.54783e-05 Inf
## [2] 26391265 26391265 1 | 4.05978e-04 Inf
## [3] 26446219 26446219 1 | 3.02059e-03 Inf
## [4] 26453605 26453605 1 | 1.65088e+00 Inf
## [5] 26453632 26453632 1 | 3.07565e+00 Inf
## ... ... ... ... . ... ...
## [3389] 26562802 32708942 6146141 | 1.10095e-02 1.29295e-02
## [3390] 26453605 32708942 6255338 | 3.02059e-03 1.10095e-02
## [3391] 26446219 32708942 6262724 | 4.05978e-04 3.02059e-03
## [3392] 26391265 32708942 6317678 | 6.54783e-05 4.05978e-04
## [3393] 26050540 32708942 6658403 | -Inf 6.54783e-05
If it is too slow, is it because of the Pairs input? Let's replace it with
two arguments for position and score separately.
paraclu_twoargs_IRanges <- function(pos, score, min_density = -Inf, clusters = IRanges()) {
params <- paraclu_params_twoargs(pos, score)
if (!is.na(params$br)) {
new_min <- max(min_density, params$min_density)
left <- 1 : (params$br-1)
right <- params$br : length(pos)
clusters <- c(paraclu_twoargs_IRanges(pos[left], score[left], new_min, clusters),
paraclu_twoargs_IRanges(pos[right], score[right], new_min, clusters))
}
c( clusters
, IRanges( start = min(pos)
, end = max(pos)
, min_d = min_density
, max_d = params$min_density))
}
paraclu_twoargs_IRanges(l$pos, l$score)
## IRanges object with 3393 ranges and 2 metadata columns:
## start end width | min_d max_d
## <integer> <integer> <integer> | <numeric> <numeric>
## [1] 26050540 26050540 1 | 6.54783e-05 Inf
## [2] 26391265 26391265 1 | 4.05978e-04 Inf
## [3] 26446219 26446219 1 | 3.02059e-03 Inf
## [4] 26453605 26453605 1 | 1.65088e+00 Inf
## [5] 26453632 26453632 1 | 3.07565e+00 Inf
## ... ... ... ... . ... ...
## [3389] 26562802 32708942 6146141 | 1.10095e-02 1.29295e-02
## [3390] 26453605 32708942 6255338 | 3.02059e-03 1.10095e-02
## [3391] 26446219 32708942 6262724 | 4.05978e-04 3.02059e-03
## [3392] 26391265 32708942 6317678 | 6.54783e-05 4.05978e-04
## [3393] 26050540 32708942 6658403 | -Inf 6.54783e-05
If it is still two slow, would it be better with a DataFrame output?
paraclu_twoargs_DF <- function(pos, score, min_density = -Inf
, clusters = DataFrame()) {
params <- paraclu_params_twoargs(pos, score)
if (!is.na(params$br)) {
new_min <- max(min_density, params$min_density)
left <- 1 : (params$br-1)
right <- params$br : length(pos)
clusters <- rbind(paraclu_twoargs_DF(pos[left], score[left], new_min, clusters),
paraclu_twoargs_DF(pos[right], score[right], new_min, clusters))
}
rbind( clusters
, DataFrame( start = min(pos)
, end = max(pos)
, min_d = min_density
, max_d = params$min_density))
}
paraclu_twoargs_DF(l$pos, l$score)
## DataFrame with 3393 rows and 4 columns
## start end min_d max_d
## <integer> <integer> <numeric> <numeric>
## 1 26050540 26050540 6.54783e-05 Inf
## 2 26391265 26391265 4.05978e-04 Inf
## 3 26446219 26446219 3.02059e-03 Inf
## 4 26453605 26453605 1.65088e+00 Inf
## 5 26453632 26453632 3.07565e+00 Inf
## ... ... ... ... ...
## 3389 26562802 32708942 1.10095e-02 1.29295e-02
## 3390 26453605 32708942 3.02059e-03 1.10095e-02
## 3391 26446219 32708942 4.05978e-04 3.02059e-03
## 3392 26391265 32708942 6.54783e-05 4.05978e-04
## 3393 26050540 32708942 -Inf 6.54783e-05
Or a tibble?
paraclu_twoargs_tibble <- function(pos, score, min_density = -Inf
, clusters = tibble::tibble()) {
params <- paraclu_params_twoargs(pos, score)
if (!is.na(params$br)) {
new_min <- max(min_density, params$min_density)
left <- 1 : (params$br-1)
right <- params$br : length(pos)
clusters <- rbind(paraclu_twoargs_tibble(pos[left], score[left], new_min, clusters),
paraclu_twoargs_tibble(pos[right], score[right], new_min, clusters))
}
rbind( clusters
, tibble::tibble( start = min(pos)
, end = max(pos)
, min_d = min_density
, max_d = params$min_density))
}
paraclu_twoargs_tibble(l$pos, l$score)
## # A tibble: 3,393 × 4
## start end min_d max_d
## <int> <int> <dbl> <dbl>
## 1 26050540 26050540 0.0000655 Inf
## 2 26391265 26391265 0.000406 Inf
## 3 26446219 26446219 0.00302 Inf
## 4 26453605 26453605 1.65 Inf
## 5 26453632 26453632 3.08 Inf
## 6 26453643 26453643 5.58 Inf
## 7 26453647 26453647 5.58 Inf
## 8 26453643 26453647 3.08 5.58
## 9 26453632 26453647 3.08 2.97
## 10 26453661 26453661 5.58 Inf
## # ℹ 3,383 more rows
Or a plain data.frame?
paraclu_twoargs_df <- function(pos, score, min_density = -Inf
, clusters = data.frame()) {
params <- paraclu_params_twoargs(pos, score)
if (!is.na(params$br)) {
new_min <- max(min_density, params$min_density)
left <- 1 : (params$br - 1)
right <- params$br : length(pos)
clusters <- rbind(paraclu_twoargs_df(pos[left], score[left], new_min, clusters),
paraclu_twoargs_df(pos[right], score[right], new_min, clusters))
}
rbind( clusters
, data.frame( start = min(pos)
, end = max(pos)
, min_d = min_density
, max_d = params$min_density))
}
paraclu_twoargs_df(l$pos, l$score) |> head()
## start end min_d max_d
## 1 26050540 26050540 6.547829e-05 Inf
## 2 26391265 26391265 4.059775e-04 Inf
## 3 26446219 26446219 3.020592e-03 Inf
## 4 26453605 26453605 1.650882e+00 Inf
## 5 26453632 26453632 3.075655e+00 Inf
## 6 26453643 26453643 5.577522e+00 Inf
Or do we lose time by keeping in memory slices of the original vector?
paraclu_fourargs_df <- function(pos, score, left, right, min_density = -Inf
, clusters = data.frame()) {
range <- left : right
params <- paraclu_params_twoargs(pos[range], score[range])
br <- left - 1 + params$br
if (!is.na(params$br)) {
new_min <- max(min_density, params$min_density)
clusters <- rbind(paraclu_fourargs_df(pos, score, left, br - 1, new_min, clusters),
paraclu_fourargs_df(pos, score, br , right , new_min, clusters))
}
rbind( clusters
, data.frame( start = min(pos)
, end = max(pos)
, min_d = min_density
, max_d = params$min_density))
}
paraclu_fourargs_df(l$pos, l$score, 1, length(l$pos)) |> head()
## start end min_d max_d
## 1 26050540 32708942 6.547829e-05 Inf
## 2 26050540 32708942 4.059775e-04 Inf
## 3 26050540 32708942 3.020592e-03 Inf
## 4 26050540 32708942 1.650882e+00 Inf
## 5 26050540 32708942 3.075655e+00 Inf
## 6 26050540 32708942 5.577522e+00 Inf
Results:
Using DataFrame or IRanges during recursion is very expensive, so let's not
bother benchmarking replicates.
microbenchmark::microbenchmark(times = 1
, tibble = paraclu_twoargs_tibble(l$pos, l$score)
, data.frame = paraclu_twoargs_df(l$pos, l$score)
, DataFrame = paraclu_twoargs_DF(l$pos, l$score)
, IRanges = paraclu_twoargs_IRanges(l$pos, l$score)
, IRanges_P = paraclu_Pair_IRanges(pair)
, orig_simple = paraclu_orig_simpler_output(ctss.df)
, orig = paraclu_orig(ctss.df)
)
## Unit: seconds
## expr min lq mean median uq max neval
## tibble 4.968984 4.968984 4.968984 4.968984 4.968984 4.968984 1
## data.frame 2.289044 2.289044 2.289044 2.289044 2.289044 2.289044 1
## DataFrame 50.811562 50.811562 50.811562 50.811562 50.811562 50.811562 1
## IRanges 59.778385 59.778385 59.778385 59.778385 59.778385 59.778385 1
## IRanges_P 63.499065 63.499065 63.499065 63.499065 63.499065 63.499065 1
## orig_simple 3.272010 3.272010 3.272010 3.272010 3.272010 3.272010 1
## orig 5.562422 5.562422 5.562422 5.562422 5.562422 5.562422 1
How about the other methods ?
(benchmark <- microbenchmark::microbenchmark(times = 20
, tibble = paraclu_twoargs_tibble(l$pos, l$score)
, data.frame = paraclu_twoargs_df(l$pos, l$score)
, orig_simple = paraclu_orig_simpler_output(ctss.df)
, orig = paraclu_orig(ctss.df)
, fourargs = paraclu_fourargs_df(l$pos, l$score, 1, length(l$pos))
))
## Unit: seconds
## expr min lq mean median uq max neval cld
## tibble 4.887625 4.944239 5.033454 4.975493 5.056239 5.779613 20 d
## data.frame 2.263464 2.276392 2.301163 2.300864 2.325676 2.341267 20 a
## orig_simple 3.213745 3.246388 3.320182 3.287912 3.317299 4.076985 20 b
## orig 4.638512 4.655572 4.708796 4.712399 4.749578 4.778663 20 c
## fourargs 2.286275 2.307026 2.328763 2.319673 2.349748 2.408184 20 a
library("ggplot2") |> suppressPackageStartupMessages()
ggplot(benchmark, aes(x = time / 1e9, y = expr, color = expr)) +
geom_boxplot() +
scale_x_log10("time (seconds)")
The data.frame method is simplest and fastest.
orig_df <- paraclu_orig(ctss.df)
new_df <- paraclu_twoargs_df(l$pos, l$score)
all( identical(orig_df$start, new_df$start)
, identical(orig_df$end , new_df$end)
, identical(orig_df$min_d, new_df$min_d)
, identical(orig_df$max_d, new_df$max_d))
## [1] TRUE
paraclu_twoargs_df
## function(pos, score, min_density = -Inf
## , clusters = data.frame()) {
## params <- paraclu_params_twoargs(pos, score)
##
## if (!is.na(params$br)) {
## new_min <- max(min_density, params$min_density)
## left <- 1 : (params$br - 1)
## right <- params$br : length(pos)
## clusters <- rbind(paraclu_twoargs_df(pos[left], score[left], new_min, clusters),
## paraclu_twoargs_df(pos[right], score[right], new_min, clusters))
## }
## rbind( clusters
## , data.frame( start = min(pos)
## , end = max(pos)
## , min_d = min_density
## , max_d = params$min_density))
## }
## <bytecode: 0x55c8d859f0f8>
Session information
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_2.7.0
## [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
## [3] VGAM_1.1-7 BiocFileCache_2.6.0
## [5] plyr_1.8.7 lazyeval_0.2.2
## [7] splines_4.2.2 operator.tools_1.6.3
## [9] BiocParallel_1.32.0 TH.data_1.1-1
## [11] digest_0.6.30 ensembldb_2.22.0
## [13] htmltools_0.5.3 fansi_1.0.4
## [15] magrittr_2.0.3 checkmate_2.1.0
## [17] memoise_2.0.1 BSgenome_1.66.0
## [19] cluster_2.1.4 Biostrings_2.66.0
## [21] formula.tools_1.7.1 sandwich_3.0-2
## [23] prettyunits_1.1.1 jpeg_0.1-9
## [25] colorspace_2.1-0 blob_1.2.3
## [27] rappdirs_0.3.3 xfun_0.34
## [29] dplyr_1.0.10 crayon_1.5.2
## [31] RCurl_1.98-1.9 microbenchmark_1.4.9
## [33] jsonlite_1.8.4 zoo_1.8-11
## [35] survival_3.5-3 VariantAnnotation_1.44.0
## [37] glue_1.6.2 gtable_0.3.3
## [39] zlibbioc_1.44.0 XVector_0.38.0
## [41] DelayedArray_0.24.0 scales_1.2.1
## [43] mvtnorm_1.1-3 DBI_1.1.3
## [45] som_0.3-5.1 Rcpp_1.0.9
## [47] progress_1.2.2 htmlTable_2.4.1
## [49] foreign_0.8-83 bit_4.0.4
## [51] Formula_1.2-4 htmlwidgets_1.5.4
## [53] httr_1.4.4 RColorBrewer_1.1-3
## [55] ellipsis_0.3.2 farver_2.1.1
## [57] pkgconfig_2.0.3 XML_3.99-0.12
## [59] Gviz_1.42.0 nnet_7.3-18
## [61] sass_0.4.2 dbplyr_2.2.1
## [63] deldir_1.0-6 utf8_1.2.3
## [65] tidyselect_1.2.0 rlang_1.1.0
## [67] reshape2_1.4.4 AnnotationDbi_1.60.0
## [69] munsell_0.5.0 tools_4.2.2
## [71] cachem_1.0.6 cli_3.6.1
## [73] generics_0.1.3 RSQLite_2.2.18
## [75] CAGEfightR_1.18.0 evaluate_0.17
## [77] stringr_1.4.1 fastmap_1.1.0
## [79] yaml_2.3.6 knitr_1.40
## [81] bit64_4.0.5 KEGGREST_1.38.0
## [83] AnnotationFilter_1.22.0 nlme_3.1-160
## [85] sparseMatrixStats_1.10.0 xml2_1.3.3
## [87] biomaRt_2.54.0 compiler_4.2.2
## [89] rstudioapi_0.14 filelock_1.0.2
## [91] curl_4.3.3 png_0.1-7
## [93] tibble_3.2.1 bslib_0.4.1
## [95] stringi_1.7.12 highr_0.9
## [97] GenomicFeatures_1.50.1 lattice_0.20-45
## [99] ProtGenerics_1.30.0 Matrix_1.5-3
## [101] vegan_2.6-4 permute_0.9-7
## [103] vctrs_0.6.1 stringdist_0.9.9
## [105] pillar_1.9.0 lifecycle_1.0.3
## [107] jquerylib_0.1.4 data.table_1.14.4
## [109] bitops_1.0-7 rtracklayer_1.58.0
## [111] R6_2.5.1 BiocIO_1.8.0
## [113] latticeExtra_0.6-30 KernSmooth_2.23-20
## [115] gridExtra_2.3 codetools_0.2-19
## [117] dichromat_2.0-0.1 MASS_7.3-58.3
## [119] gtools_3.9.3 assertthat_0.2.1
## [121] rjson_0.2.21 withr_2.5.0
## [123] GenomicAlignments_1.34.0 Rsamtools_2.14.0
## [125] multcomp_1.4-20 GenomeInfoDbData_1.2.9
## [127] mgcv_1.8-41 parallel_4.2.2
## [129] hms_1.1.2 grid_4.2.2
## [131] rpart_4.1.19 rmarkdown_2.17
## [133] DelayedMatrixStats_1.20.0 biovizBase_1.46.0
## [135] base64enc_0.1-3 interp_1.1-3
## [137] restfulr_0.0.15
charles-plessy/CAGEr documentation built on Oct. 27, 2024, 10:11 p.m.
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title: "Paraclu benchmark" author: "Charles Plessy" date: "2023-07-14" output: html_document: keep_md: yes editor_options: chunk_output_type: console
Purpose
While refactoring the Paraclu code, I wondered if some data structures would be
more efficient than data.frames.
Example data
Let's focus on the clustering of positions with scores from one strand of one chromosome, because this is where the algorithm is recursive and hard to parallelise.
In the original implementation the input in two columns of a data.frame. Here
I also try a Pairs object, or just two separate arguments for the positions
and scores.
ctss <- CTSSnormalizedTpmGR(exampleCAGEexp,1)
isSorted(ctss)
## [1] TRUE
ctss.df <- as.data.frame(granges(ctss))
ctss.df$tpm <- decode(score(ctss))
ctss.df$pos <- ctss.df$start
ctss.df$end <- ctss.df$start <- ctss.df$width <- NULL
pair <- Pairs(pos(ctss), decode(score(ctss)))
DF <- DataFrame(pos = pos(ctss), score = decode(score(ctss)))
l <- list(pos = pos(ctss), score = decode(score(ctss)))
The paraclu parameters function
The main recursive functions call a subfunction that compute the density and the next break point. First, let's see if it can be optimised.
paraclu_params_df was originally called .paraclu1. The other functions
are rewrites.
paraclu_params_df <- function(ctss) {
sit <- nrow(ctss)
tot <- sum(ctss$tpm)
if(sit == 1) {
min_density <- Inf
br <- NA
}else{
densities_forward <- cumsum(ctss$tpm)[-sit]/(ctss$pos[2:sit] - ctss$pos[1])
densities_reverse <- cumsum(rev(ctss$tpm))[-sit]/(ctss$pos[sit] - ctss$pos[(sit-1):1])
min_densities = c(min(densities_forward), min(densities_reverse))
breaks <- c(which(densities_forward == min_densities[1])[1] + 1, sit + 1 - which(densities_reverse == min_densities[2])[1])
min_density <- min(min_densities)
br <- breaks[tail(which(min_densities == min_density),1)]
}
list(br = br, min_density = min_density, tot = tot, sit = sit)
}
paraclu_params_df(ctss.df)
## $br
## [1] 2
##
## $min_density
## [1] 6.547829e-05
##
## $tot
## [1] 220525.6
##
## $sit
## [1] 1697
paraclu_params_Pairs <- function(pair) {
sit <- length(pair)
score <- second(pair)
tot <- sum(score)
if(sit == 1) return(list(br = NA, min_density = Inf, tot = tot, sit = sit))
pos <- first(pair)
densities_forward <- cumsum( score) [-sit] / (pos[2:sit] - pos[1] )
densities_reverse <- cumsum(rev(score))[-sit] / (pos[sit] - pos[(sit-1):1])
min_densities = c(min(densities_forward), min(densities_reverse))
breaks <- c( 1 + which(densities_forward == min_densities[1])[1]
, sit + 1 - which(densities_reverse == min_densities[2])[1])
min_density <- min(min_densities)
br <- breaks[tail(which(min_densities == min_density),1)]
list(br = br, min_density = min_density, tot = tot, sit = sit)
}
identical(paraclu_params_Pairs(pair), paraclu_params_df(ctss.df))
## [1] TRUE
paraclu_params_DF <- function(DF) {
sit <- nrow(DF)
tot <- sum(DF$score)
if(sit == 1) return(list(br = NA, min_density = Inf, tot = tot, sit = sit))
densities_forward <- cumsum( DF$score) [-sit] / (DF$pos[2:sit] - DF$pos[1] )
densities_reverse <- cumsum(rev(DF$score))[-sit] / (DF$pos[sit] - DF$pos[(sit-1):1])
min_densities = c(min(densities_forward), min(densities_reverse))
breaks <- c( 1 + which(densities_forward == min_densities[1])[1]
, sit + 1 - which(densities_reverse == min_densities[2])[1])
min_density <- min(min_densities)
br <- breaks[tail(which(min_densities == min_density),1)]
list(br = br, min_density = min_density, tot = tot, sit = sit)
}
identical(paraclu_params_DF(DF), paraclu_params_df(ctss.df))
## [1] TRUE
paraclu_params_twoargs <- function(pos, score) {
sit <- length(pos)
tot <- sum(score)
if(sit == 1) return(list(br = NA, min_density = Inf, tot = tot, sit = sit))
densities_forward <- cumsum( score) [-sit] / (pos[2:sit] - pos[1] )
densities_reverse <- cumsum(rev(score))[-sit] / (pos[sit] - pos[(sit-1):1])
min_densities = c(min(densities_forward), min(densities_reverse))
breaks <- c( 1 + which(densities_forward == min_densities[1])[1]
, sit + 1 - which(densities_reverse == min_densities[2])[1])
min_density <- min(min_densities)
br <- breaks[tail(which(min_densities == min_density),1)]
list(br = br, min_density = min_density, tot = tot, sit = sit)
}
identical(paraclu_params_twoargs(l$pos, l$score), paraclu_params_df(ctss.df))
## [1] TRUE
microbenchmark::microbenchmark(
data.frame = paraclu_params_df(ctss.df),
Pairs = paraclu_params_Pairs(pair),
DataFrame = paraclu_params_DF(DF),
two_args = paraclu_params_twoargs(l$pos, l$score),
times = 1000)
## Unit: microseconds
## expr min lq mean median uq max neval
## data.frame 88.760 106.4695 151.6922 117.5020 149.7100 6851.252 1000
## Pairs 190.730 223.2810 313.0306 248.9615 310.5180 9349.667 1000
## DataFrame 3952.067 4382.7235 4808.1692 4657.3280 5071.8605 12485.169 1000
## two_args 79.675 90.0480 130.7938 99.4555 123.4375 7440.558 1000
## cld
## a
## b
## c
## a
All functions are microsecond-fast except the DF one. Let's use the two- arguments version in all the benchmarks below.
Recursive Paraclu function
The original recursive function was originally called .paraclu2. It computes
extra information like dominant peak etc, which is nice but we have a good
function to do that a posteriori. Therefore we will compare
implementations that skip that calculation.
# Originally called .paraclu2
# Minor changes applied to keep ctss.df colnames.
# Removed re-sorting of output
paraclu_orig <- function(ctss, min_density = -Inf, clusters.df = data.frame()) {
if(nrow(ctss)>0){
params <- paraclu_params_twoargs(ctss$pos, ctss$tpm)
br <- params[[1]]
max_density <- params[[2]]
tot <- params[[3]]
sit <- params[[4]]
if(!(max_density == Inf)){
new_min <- max(min_density, max_density)
clusters.df <- rbind(paraclu_orig(ctss = ctss[1:(br-1),], min_density = new_min, clusters.df = clusters.df),
paraclu_orig(ctss = ctss[br:nrow(ctss),], min_density = new_min, clusters.df = clusters.df))
}
return(rbind(clusters.df, data.frame(seqnames = ctss$seqnames[1],
start = min(ctss$pos),
end = max(ctss$pos),
strand = ctss$strand[1],
nr_ctss = sit,
dominant_ctss = ctss$pos[which(ctss$tpm == max(ctss$tpm))[ceiling(length(which(ctss$tpm == max(ctss$tpm)))/2)]],
tpm = tot,
tpm.dominant_ctss = ctss$tpm[which(ctss$tpm == max(ctss$tpm))[ceiling(length(which(ctss$tpm == max(ctss$tpm)))/2)]],
min_d = min_density, max_d= max_density)
)
)
}else{
return(clusters.df)
}
}
paraclu_orig(ctss.df) |> head()
## seqnames start end strand nr_ctss dominant_ctss tpm
## 1 chr17 26050540 26050540 + 1 26050540 22.31009
## 2 chr17 26391265 26391265 + 1 26391265 22.31009
## 3 chr17 26446219 26446219 + 1 26446219 22.31009
## 4 chr17 26453605 26453605 + 1 26453605 44.57381
## 5 chr17 26453632 26453632 + 1 26453632 44.57381
## 6 chr17 26453643 26453643 + 1 26453643 22.31009
## tpm.dominant_ctss min_d max_d
## 1 22.31009 6.547829e-05 Inf
## 2 22.31009 4.059775e-04 Inf
## 3 22.31009 3.020592e-03 Inf
## 4 44.57381 1.650882e+00 Inf
## 5 44.57381 3.075655e+00 Inf
## 6 22.31009 5.577522e+00 Inf
paraclu_orig_simpler_output <- function(ctss, min_density = -Inf, clusters.df = data.frame()) {
if(nrow(ctss)>0){
params <- paraclu_params_twoargs(ctss$pos, ctss$tpm)
br <- params[[1]]
max_density <- params[[2]]
tot <- params[[3]]
sit <- params[[4]]
if(!(max_density == Inf)){
new_min <- max(min_density, max_density)
clusters.df <- rbind(paraclu_orig_simpler_output(ctss = ctss[1:(br-1),], min_density = new_min, clusters.df = clusters.df),
paraclu_orig_simpler_output(ctss = ctss[br:nrow(ctss),], min_density = new_min, clusters.df = clusters.df))
}
return(rbind(clusters.df, data.frame(seqnames = ctss$seqnames[1],
start = min(ctss$pos),
end = max(ctss$pos),
min_d = min_density, max_d= max_density)
)
)
}else{
return(clusters.df)
}
}
paraclu_orig_simpler_output(ctss.df) |> head()
## seqnames start end min_d max_d
## 1 chr17 26050540 26050540 6.547829e-05 Inf
## 2 chr17 26391265 26391265 4.059775e-04 Inf
## 3 chr17 26446219 26446219 3.020592e-03 Inf
## 4 chr17 26453605 26453605 1.650882e+00 Inf
## 5 chr17 26453632 26453632 3.075655e+00 Inf
## 6 chr17 26453643 26453643 5.577522e+00 Inf
Representing the clusters in the IRanges class looks elegant. But is it
efficient? Also, since we need only two vectors, how about keeping them
together as a pair?
paraclu_Pair_IRanges <- function(pair, min_density = -Inf, clusters = IRanges()) {
params <- paraclu_params_twoargs(first(pair), second(pair))
if (!is.na(params$br)) {
new_min <- max(min_density, params$min_density)
clusters <- c(paraclu_Pair_IRanges(pair[ 1 : (params$br-1)], new_min, clusters),
paraclu_Pair_IRanges(pair[params$br : length(pair)], new_min, clusters))
}
c( clusters
, IRanges( start = min(first(pair))
, end = max(first(pair))
, min_d = min_density
, max_d = params$min_density))
}
paraclu_Pair_IRanges(pair)
## IRanges object with 3393 ranges and 2 metadata columns:
## start end width | min_d max_d
## <integer> <integer> <integer> | <numeric> <numeric>
## [1] 26050540 26050540 1 | 6.54783e-05 Inf
## [2] 26391265 26391265 1 | 4.05978e-04 Inf
## [3] 26446219 26446219 1 | 3.02059e-03 Inf
## [4] 26453605 26453605 1 | 1.65088e+00 Inf
## [5] 26453632 26453632 1 | 3.07565e+00 Inf
## ... ... ... ... . ... ...
## [3389] 26562802 32708942 6146141 | 1.10095e-02 1.29295e-02
## [3390] 26453605 32708942 6255338 | 3.02059e-03 1.10095e-02
## [3391] 26446219 32708942 6262724 | 4.05978e-04 3.02059e-03
## [3392] 26391265 32708942 6317678 | 6.54783e-05 4.05978e-04
## [3393] 26050540 32708942 6658403 | -Inf 6.54783e-05
If it is too slow, is it because of the Pairs input? Let's replace it with
two arguments for position and score separately.
paraclu_twoargs_IRanges <- function(pos, score, min_density = -Inf, clusters = IRanges()) {
params <- paraclu_params_twoargs(pos, score)
if (!is.na(params$br)) {
new_min <- max(min_density, params$min_density)
left <- 1 : (params$br-1)
right <- params$br : length(pos)
clusters <- c(paraclu_twoargs_IRanges(pos[left], score[left], new_min, clusters),
paraclu_twoargs_IRanges(pos[right], score[right], new_min, clusters))
}
c( clusters
, IRanges( start = min(pos)
, end = max(pos)
, min_d = min_density
, max_d = params$min_density))
}
paraclu_twoargs_IRanges(l$pos, l$score)
## IRanges object with 3393 ranges and 2 metadata columns:
## start end width | min_d max_d
## <integer> <integer> <integer> | <numeric> <numeric>
## [1] 26050540 26050540 1 | 6.54783e-05 Inf
## [2] 26391265 26391265 1 | 4.05978e-04 Inf
## [3] 26446219 26446219 1 | 3.02059e-03 Inf
## [4] 26453605 26453605 1 | 1.65088e+00 Inf
## [5] 26453632 26453632 1 | 3.07565e+00 Inf
## ... ... ... ... . ... ...
## [3389] 26562802 32708942 6146141 | 1.10095e-02 1.29295e-02
## [3390] 26453605 32708942 6255338 | 3.02059e-03 1.10095e-02
## [3391] 26446219 32708942 6262724 | 4.05978e-04 3.02059e-03
## [3392] 26391265 32708942 6317678 | 6.54783e-05 4.05978e-04
## [3393] 26050540 32708942 6658403 | -Inf 6.54783e-05
If it is still two slow, would it be better with a DataFrame output?
paraclu_twoargs_DF <- function(pos, score, min_density = -Inf
, clusters = DataFrame()) {
params <- paraclu_params_twoargs(pos, score)
if (!is.na(params$br)) {
new_min <- max(min_density, params$min_density)
left <- 1 : (params$br-1)
right <- params$br : length(pos)
clusters <- rbind(paraclu_twoargs_DF(pos[left], score[left], new_min, clusters),
paraclu_twoargs_DF(pos[right], score[right], new_min, clusters))
}
rbind( clusters
, DataFrame( start = min(pos)
, end = max(pos)
, min_d = min_density
, max_d = params$min_density))
}
paraclu_twoargs_DF(l$pos, l$score)
## DataFrame with 3393 rows and 4 columns
## start end min_d max_d
## <integer> <integer> <numeric> <numeric>
## 1 26050540 26050540 6.54783e-05 Inf
## 2 26391265 26391265 4.05978e-04 Inf
## 3 26446219 26446219 3.02059e-03 Inf
## 4 26453605 26453605 1.65088e+00 Inf
## 5 26453632 26453632 3.07565e+00 Inf
## ... ... ... ... ...
## 3389 26562802 32708942 1.10095e-02 1.29295e-02
## 3390 26453605 32708942 3.02059e-03 1.10095e-02
## 3391 26446219 32708942 4.05978e-04 3.02059e-03
## 3392 26391265 32708942 6.54783e-05 4.05978e-04
## 3393 26050540 32708942 -Inf 6.54783e-05
Or a tibble?
paraclu_twoargs_tibble <- function(pos, score, min_density = -Inf
, clusters = tibble::tibble()) {
params <- paraclu_params_twoargs(pos, score)
if (!is.na(params$br)) {
new_min <- max(min_density, params$min_density)
left <- 1 : (params$br-1)
right <- params$br : length(pos)
clusters <- rbind(paraclu_twoargs_tibble(pos[left], score[left], new_min, clusters),
paraclu_twoargs_tibble(pos[right], score[right], new_min, clusters))
}
rbind( clusters
, tibble::tibble( start = min(pos)
, end = max(pos)
, min_d = min_density
, max_d = params$min_density))
}
paraclu_twoargs_tibble(l$pos, l$score)
## # A tibble: 3,393 × 4
## start end min_d max_d
## <int> <int> <dbl> <dbl>
## 1 26050540 26050540 0.0000655 Inf
## 2 26391265 26391265 0.000406 Inf
## 3 26446219 26446219 0.00302 Inf
## 4 26453605 26453605 1.65 Inf
## 5 26453632 26453632 3.08 Inf
## 6 26453643 26453643 5.58 Inf
## 7 26453647 26453647 5.58 Inf
## 8 26453643 26453647 3.08 5.58
## 9 26453632 26453647 3.08 2.97
## 10 26453661 26453661 5.58 Inf
## # ℹ 3,383 more rows
Or a plain data.frame?
paraclu_twoargs_df <- function(pos, score, min_density = -Inf
, clusters = data.frame()) {
params <- paraclu_params_twoargs(pos, score)
if (!is.na(params$br)) {
new_min <- max(min_density, params$min_density)
left <- 1 : (params$br - 1)
right <- params$br : length(pos)
clusters <- rbind(paraclu_twoargs_df(pos[left], score[left], new_min, clusters),
paraclu_twoargs_df(pos[right], score[right], new_min, clusters))
}
rbind( clusters
, data.frame( start = min(pos)
, end = max(pos)
, min_d = min_density
, max_d = params$min_density))
}
paraclu_twoargs_df(l$pos, l$score) |> head()
## start end min_d max_d
## 1 26050540 26050540 6.547829e-05 Inf
## 2 26391265 26391265 4.059775e-04 Inf
## 3 26446219 26446219 3.020592e-03 Inf
## 4 26453605 26453605 1.650882e+00 Inf
## 5 26453632 26453632 3.075655e+00 Inf
## 6 26453643 26453643 5.577522e+00 Inf
Or do we lose time by keeping in memory slices of the original vector?
paraclu_fourargs_df <- function(pos, score, left, right, min_density = -Inf
, clusters = data.frame()) {
range <- left : right
params <- paraclu_params_twoargs(pos[range], score[range])
br <- left - 1 + params$br
if (!is.na(params$br)) {
new_min <- max(min_density, params$min_density)
clusters <- rbind(paraclu_fourargs_df(pos, score, left, br - 1, new_min, clusters),
paraclu_fourargs_df(pos, score, br , right , new_min, clusters))
}
rbind( clusters
, data.frame( start = min(pos)
, end = max(pos)
, min_d = min_density
, max_d = params$min_density))
}
paraclu_fourargs_df(l$pos, l$score, 1, length(l$pos)) |> head()
## start end min_d max_d
## 1 26050540 32708942 6.547829e-05 Inf
## 2 26050540 32708942 4.059775e-04 Inf
## 3 26050540 32708942 3.020592e-03 Inf
## 4 26050540 32708942 1.650882e+00 Inf
## 5 26050540 32708942 3.075655e+00 Inf
## 6 26050540 32708942 5.577522e+00 Inf
Results:
Using DataFrame or IRanges during recursion is very expensive, so let's not bother benchmarking replicates.
microbenchmark::microbenchmark(times = 1
, tibble = paraclu_twoargs_tibble(l$pos, l$score)
, data.frame = paraclu_twoargs_df(l$pos, l$score)
, DataFrame = paraclu_twoargs_DF(l$pos, l$score)
, IRanges = paraclu_twoargs_IRanges(l$pos, l$score)
, IRanges_P = paraclu_Pair_IRanges(pair)
, orig_simple = paraclu_orig_simpler_output(ctss.df)
, orig = paraclu_orig(ctss.df)
)
## Unit: seconds
## expr min lq mean median uq max neval
## tibble 4.968984 4.968984 4.968984 4.968984 4.968984 4.968984 1
## data.frame 2.289044 2.289044 2.289044 2.289044 2.289044 2.289044 1
## DataFrame 50.811562 50.811562 50.811562 50.811562 50.811562 50.811562 1
## IRanges 59.778385 59.778385 59.778385 59.778385 59.778385 59.778385 1
## IRanges_P 63.499065 63.499065 63.499065 63.499065 63.499065 63.499065 1
## orig_simple 3.272010 3.272010 3.272010 3.272010 3.272010 3.272010 1
## orig 5.562422 5.562422 5.562422 5.562422 5.562422 5.562422 1
How about the other methods ?
(benchmark <- microbenchmark::microbenchmark(times = 20
, tibble = paraclu_twoargs_tibble(l$pos, l$score)
, data.frame = paraclu_twoargs_df(l$pos, l$score)
, orig_simple = paraclu_orig_simpler_output(ctss.df)
, orig = paraclu_orig(ctss.df)
, fourargs = paraclu_fourargs_df(l$pos, l$score, 1, length(l$pos))
))
## Unit: seconds
## expr min lq mean median uq max neval cld
## tibble 4.887625 4.944239 5.033454 4.975493 5.056239 5.779613 20 d
## data.frame 2.263464 2.276392 2.301163 2.300864 2.325676 2.341267 20 a
## orig_simple 3.213745 3.246388 3.320182 3.287912 3.317299 4.076985 20 b
## orig 4.638512 4.655572 4.708796 4.712399 4.749578 4.778663 20 c
## fourargs 2.286275 2.307026 2.328763 2.319673 2.349748 2.408184 20 a
library("ggplot2") |> suppressPackageStartupMessages()
ggplot(benchmark, aes(x = time / 1e9, y = expr, color = expr)) +
geom_boxplot() +
scale_x_log10("time (seconds)")
The data.frame method is simplest and fastest.
orig_df <- paraclu_orig(ctss.df)
new_df <- paraclu_twoargs_df(l$pos, l$score)
all( identical(orig_df$start, new_df$start)
, identical(orig_df$end , new_df$end)
, identical(orig_df$min_d, new_df$min_d)
, identical(orig_df$max_d, new_df$max_d))
## [1] TRUE
paraclu_twoargs_df
## function(pos, score, min_density = -Inf
## , clusters = data.frame()) {
## params <- paraclu_params_twoargs(pos, score)
##
## if (!is.na(params$br)) {
## new_min <- max(min_density, params$min_density)
## left <- 1 : (params$br - 1)
## right <- params$br : length(pos)
## clusters <- rbind(paraclu_twoargs_df(pos[left], score[left], new_min, clusters),
## paraclu_twoargs_df(pos[right], score[right], new_min, clusters))
## }
## rbind( clusters
## , data.frame( start = min(pos)
## , end = max(pos)
## , min_d = min_density
## , max_d = params$min_density))
## }
## <bytecode: 0x55c8d859f0f8>
Session information
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_2.7.0
## [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
## [3] VGAM_1.1-7 BiocFileCache_2.6.0
## [5] plyr_1.8.7 lazyeval_0.2.2
## [7] splines_4.2.2 operator.tools_1.6.3
## [9] BiocParallel_1.32.0 TH.data_1.1-1
## [11] digest_0.6.30 ensembldb_2.22.0
## [13] htmltools_0.5.3 fansi_1.0.4
## [15] magrittr_2.0.3 checkmate_2.1.0
## [17] memoise_2.0.1 BSgenome_1.66.0
## [19] cluster_2.1.4 Biostrings_2.66.0
## [21] formula.tools_1.7.1 sandwich_3.0-2
## [23] prettyunits_1.1.1 jpeg_0.1-9
## [25] colorspace_2.1-0 blob_1.2.3
## [27] rappdirs_0.3.3 xfun_0.34
## [29] dplyr_1.0.10 crayon_1.5.2
## [31] RCurl_1.98-1.9 microbenchmark_1.4.9
## [33] jsonlite_1.8.4 zoo_1.8-11
## [35] survival_3.5-3 VariantAnnotation_1.44.0
## [37] glue_1.6.2 gtable_0.3.3
## [39] zlibbioc_1.44.0 XVector_0.38.0
## [41] DelayedArray_0.24.0 scales_1.2.1
## [43] mvtnorm_1.1-3 DBI_1.1.3
## [45] som_0.3-5.1 Rcpp_1.0.9
## [47] progress_1.2.2 htmlTable_2.4.1
## [49] foreign_0.8-83 bit_4.0.4
## [51] Formula_1.2-4 htmlwidgets_1.5.4
## [53] httr_1.4.4 RColorBrewer_1.1-3
## [55] ellipsis_0.3.2 farver_2.1.1
## [57] pkgconfig_2.0.3 XML_3.99-0.12
## [59] Gviz_1.42.0 nnet_7.3-18
## [61] sass_0.4.2 dbplyr_2.2.1
## [63] deldir_1.0-6 utf8_1.2.3
## [65] tidyselect_1.2.0 rlang_1.1.0
## [67] reshape2_1.4.4 AnnotationDbi_1.60.0
## [69] munsell_0.5.0 tools_4.2.2
## [71] cachem_1.0.6 cli_3.6.1
## [73] generics_0.1.3 RSQLite_2.2.18
## [75] CAGEfightR_1.18.0 evaluate_0.17
## [77] stringr_1.4.1 fastmap_1.1.0
## [79] yaml_2.3.6 knitr_1.40
## [81] bit64_4.0.5 KEGGREST_1.38.0
## [83] AnnotationFilter_1.22.0 nlme_3.1-160
## [85] sparseMatrixStats_1.10.0 xml2_1.3.3
## [87] biomaRt_2.54.0 compiler_4.2.2
## [89] rstudioapi_0.14 filelock_1.0.2
## [91] curl_4.3.3 png_0.1-7
## [93] tibble_3.2.1 bslib_0.4.1
## [95] stringi_1.7.12 highr_0.9
## [97] GenomicFeatures_1.50.1 lattice_0.20-45
## [99] ProtGenerics_1.30.0 Matrix_1.5-3
## [101] vegan_2.6-4 permute_0.9-7
## [103] vctrs_0.6.1 stringdist_0.9.9
## [105] pillar_1.9.0 lifecycle_1.0.3
## [107] jquerylib_0.1.4 data.table_1.14.4
## [109] bitops_1.0-7 rtracklayer_1.58.0
## [111] R6_2.5.1 BiocIO_1.8.0
## [113] latticeExtra_0.6-30 KernSmooth_2.23-20
## [115] gridExtra_2.3 codetools_0.2-19
## [117] dichromat_2.0-0.1 MASS_7.3-58.3
## [119] gtools_3.9.3 assertthat_0.2.1
## [121] rjson_0.2.21 withr_2.5.0
## [123] GenomicAlignments_1.34.0 Rsamtools_2.14.0
## [125] multcomp_1.4-20 GenomeInfoDbData_1.2.9
## [127] mgcv_1.8-41 parallel_4.2.2
## [129] hms_1.1.2 grid_4.2.2
## [131] rpart_4.1.19 rmarkdown_2.17
## [133] DelayedMatrixStats_1.20.0 biovizBase_1.46.0
## [135] base64enc_0.1-3 interp_1.1-3
## [137] restfulr_0.0.15
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