In charles-plessy/CAGEr: Analysis of CAGE (Cap Analysis of Gene Expression) sequencing data for precise mapping of transcription start sites and promoterome mining

Purpose

The function to determine quantile positions is one of the slowest in CAGEr. Can we speed it up?

Setup

library("CAGEr")   |> suppressPackageStartupMessages()
library("ggplot2") |> suppressPackageStartupMessages()

Example data

cum.sums <- CTSScumulativesTagClusters(exampleCAGEexp)[[1]]
clusters <- tagClustersGR(exampleCAGEexp)[[1]]

Current implementation at the time the benchmark was written.

getQuantilepos_Vectorized_tail_decoded <- Vectorize(vectorize.args = "cum.sum", function(q, cum.sum) {
  cum.sum <- decode(cum.sum) # A microbenchmark showed it it 3 times faster when applying decode() now
  c.max <- tail(cum.sum,1) # Max is last element since x is a cumulative sums.
  treshold <- c.max * q
  which.max(cum.sum >= treshold)
})
getQuantilepos_Vectorized_tail_decoded(.9, cum.sums) |> head()

Alternative functions.

getQuantilepos_Vectorized_tail <- Vectorize(vectorize.args = "cum.sum", function(q, cum.sum) {
  c.max <- tail(cum.sum,1) # Max is last element since x is a cumulative sums.
  treshold <- c.max * q
  which.max(cum.sum >= treshold)
})
getQuantilepos_Vectorized_tail(.9, cum.sums) |> head()

getQuantilepos_Vectorized_max_decoded <- Vectorize(vectorize.args = "cum.sum", function(q, cum.sum) {
  cum.sum <- decode(cum.sum) 
  c.max <- max(cum.sum) # Max is last element since x is a cumulative sums.
  treshold <- c.max * q
  which.max(cum.sum >= treshold)
})
getQuantilepos_Vectorized_max_decoded(.9, cum.sums) |> head()

getQuantilepos_Vectorized_max <- Vectorize(vectorize.args = "cum.sum", function(q, cum.sum) {
  c.max <- max(cum.sum) # Max is last element since x is a cumulative sums.
  treshold <- c.max * q
  which.max(cum.sum >= treshold)
})
getQuantilepos_Vectorized_max(.9, cum.sums) |> head()

getQuantilepos_lapply <- function(q, cum.sums, decode = FALSE) {
  if (isTRUE(decode)) cum.sums <- lapply(cum.sums, decode)
  l <- sapply(cum.sums, \(cum.sum) {
  c.max <- tail(cum.sum,1) # Max is last element since x is a cumulative sums.
  treshold <- c.max * q
  which.max(cum.sum >= treshold)})
  l
}
getQuantilepos_lapply(.9, cum.sums) |> head()

getQuantilepos_Rle_native <- function(q, cum.sums) {
  (cum.sums >= max(cum.sums) * q )|> sapply(which.max)
}
getQuantilepos_Rle_native(.9, cum.sums) |> head()

getQuantilepos_NL_native <- function(q, cum.sums) {
  nl <- as(cum.sums, "NumericList")
  min(which(nl >= max(nl) * q))
}
getQuantilepos_NL_native(.9, cum.sums) |> head()

getQuantilepos_ChatGPT  <- function(q, cum.sums, decode = FALSE) {
  if (isTRUE(decode)) cum.sums <- lapply(cum.sums, decode)
  find_quantile_and_index <- function(x, q) {
    # Interestingly ChatGPT's definition of quantiles appears more sound,
    # quantile_value <- quantile(x, q)
    # But the definition below is the one orgiginally used in CAGEr.
    quantile_value <- max(x) * q
    which(x >= quantile_value)[1]
  }
  sapply(cum.sums, find_quantile_and_index, q)
}
getQuantilepos_ChatGPT(.9, cum.sums) |> head()

Benchmark

(microbench_out <- microbenchmark::microbenchmark(
  times = 100,
  Vectorized_tail_decoded = getQuantilepos_Vectorized_tail_decoded(.9, cum.sums),
  Vectorized_tail         = getQuantilepos_Vectorized_tail(.9, cum.sums),
  Vectorized_max_decoded  = getQuantilepos_Vectorized_max_decoded(.9, cum.sums),
  Vectorized_max          = getQuantilepos_Vectorized_max(.9, cum.sums),
  lapply                  = getQuantilepos_lapply(.9, cum.sums),
  lapply_decoded          = getQuantilepos_lapply(.9, cum.sums, decode = TRUE),
  Rle_native              = getQuantilepos_Rle_native(.9, cum.sums),
  NL_native               = getQuantilepos_NL_native(.9, cum.sums),
  ChatGPT                 = getQuantilepos_ChatGPT(.9, cum.sums),
  ChatGPT_decoded         = getQuantilepos_ChatGPT(.9, cum.sums, decode = TRUE)
))
# https://statisticsglobe.com/microbenchmark-package-r
ggplot(microbench_out, aes(x = time / 1e9, y = expr, color = expr)) +  # Plot performance comparison
  geom_boxplot() + 
  scale_x_log10("time (seconds)")

Result

The winner is 20 times faster!

getQuantilepos_NL_native <- function(q, cum.sums) {
  nl <- as(cum.sums, "NumericList")
  min(which(nl >= max(nl) * q))
}

identical(
  getQuantilepos_Vectorized_tail_decoded(.9, cum.sums),
  getQuantilepos_NL_native(.9, cum.sums)
)

Session information

sessionInfo()

ChatGPT

Charles 10:15 AM

Good morning ChatGPT

APP 10:15 AM

Good morning! How can I assist you today?

Charles 10:17 AM

Using the R programming language, I want to find in a list of vectors of sorted numerical values the index where the value exceeds a given quantile.

APP 10:17 AM

In R programming, you can use the which function in combination with the quantile function to find the index in a list of sorted numerical vectors where the value exceeds a given quantile. Here's an example:

r
# Assume 'v' is your sorted numerical vector
v <- c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)

# Define your quantile, for example, the 50th percentile (median)
q <- quantile(v, 0.5)

# Find the indices where the value exceeds the quantile
indices <- which(v > q)

# Print the indices
print(indices)

This will give you the indices of the values in the vector 'v' that are greater than the 50th percentile. You can adjust the quantile value as needed.

Charles 10:18 AM

Thanks, the vectors are in a list, and I need to run the function on each of them in an efficient way. Can you write a vectorized function?

APP 10:18 AM

In R programming, you can use the lapply function in combination with which and quantile to create a vectorized function that finds the index in each vector of a list where the value exceeds a given quantile. Here's an example:

r
# Define your list of vectors
list_of_vectors <- list(c(1,2,3,4,5), c(6,7,8,9,10), c(11,12,13,14,15))

# Define the quantile
q <- 0.75

# Define the function
find_index <- function(x, q) {
  return(which(x > quantile(x, q)))
}

# Apply the function to each vector in the list
result <- lapply(list_of_vectors, find_index, q)

# Print the result
print(result)

In this example, find_index is a function that takes a vector and a quantile as input, and returns the indices of the vector where the value exceeds the given quantile. The lapply function applies this function to each vector in the list. The result is a list of vectors, where each vector contains the indices where the value exceeds the given quantile.

Charles 10:19 AM

Thanks, we are almost there. I want the function to compute the quantile specifically on each vector.

APP 10:19 AM

In R programming, you can use the lapply() function to apply a function to each element of a list. If you want to compute the quantile for each vector in the list, you can do it like this:

r
# Assuming 'my_list' is your list of vectors
result <- lapply(my_list, function(x) quantile(x, probs = c(0.25, 0.5, 0.75)))

This will return a new list where each element is the 25th, 50th, and 75th percentile of the corresponding vector in the original list. You can modify the probs parameter to compute different quantiles.

Charles 10:21 AM

Sorry, but what I mean is: I have a list of vectors, and for each vector of that list I need to find a given quantile, and the first index as which the value in the vector exceed the quantile.

APP 10:21 AM

In R, you can use the lapply function to apply a function to each element in a list. Here's how you can find for each vector in a list, a given quantile, and the first index at which the value in the vector exceeds the quantile:

r
# Assume 'lst' is your list of vectors and 'q' is your quantile
lst <- list(c(1,2,3,4,5), c(6,7,8,9,10), c(11,12,13,14,15))
q <- 0.5

# Function to calculate quantile and find the first index exceeding the quantile
find_quantile_and_index <- function(x, q) {
  quantile_value <- quantile(x, q)
  index <- which(x > quantile_value)[1]
  return(list("quantile" = quantile_value, "index" = index))
}

# Apply the function to each vector in the list
result <- lapply(lst, find_quantile_and_index, q)

# Print the result
print(result)

This will return a list of lists, where each sub-list contains the quantile and the first index at which the value in the vector exceeds the quantile for each vector in the original list.

Charles 10:29 AM

Thanks, but it is too slow in my benchmark. Can you propose me a faster version ? APP 10:29 AM

To optimize your function in R, you can use the lapply function which is faster than loops. Here's an example:

r
# Assuming 'lst' is your list of vectors and 'q' is your quantile
results <- lapply(lst, function(x) {
  quantile_val <- quantile(x, q)
  index <- which(x > quantile_val)[1]
  return(list("quantile" = quantile_val, "index" = index))
})

This function applies the anonymous function to each vector in the list. The function calculates the quantile, finds the first index where the value exceeds the quantile, and returns them as a list. The result is a list of these lists.

Remember, R is vectorized which means it's more efficient to apply a function to a whole vector at once rather than element by element. Functions like lapply take advantage of this. Also, using which with the condition directly is faster than looping through the vector to find the index.

charles-plessy/CAGEr documentation built on Oct. 27, 2024, 10:11 p.m.