Nothing

```
#' Detect significantly interacting complexes in a chromatogram matrix
#'
#' Use a permutation testing approach to identify complexes that show a
#' significant tendency to interact, relative to random sets of complexes
#' of equivalent size. The function begins by calculating the Pearson
#' correlation or Euclidean distance between all proteins in the matrix, and
#'
#' @param profile_matrix a matrix of chromatograms, with proteins in the rows
#' and fractions in the columns, or a \code{\linkS4class{MSnSet}} object
#' @param complexes a named list of protein complexes, where the name is the
#' complex name and the entries are proteins within that complex
#' @param min_pairs the minimum number of pairwise observations to count a
#' correlation or distance towards the z score
#' @param method method to use to calculate edge weights;
#' one of \code{pearson} or \code{euclidean}
#' @param bootstraps number of bootstraps to execute to estimate z scores
#' @param progress whether to show the progress of the function
#'
#' @return a named vector of z scores for each complex in the input list
#'
#' @examples
#' data(scott)
#' data(gold_standard)
#' complexes <- gold_standard[lengths(gold_standard) >= 3]
#' z_scores <- detect_complexes(t(scott), complexes)
#' length(na.omit(z_scores)) ## number of complexes that could be tested
#' z_scores[which.max(z_scores)] ## most significant complex
#'
#' @importFrom stats cor dist na.omit median sd
#' @importFrom utils combn
#' @importFrom progress progress_bar
#' @importFrom purrr map_dbl
#' @importFrom MSnbase exprs
#' @importFrom methods is
#'
#' @export
detect_complexes <- function(profile_matrix, complexes,
method = c("pearson", "euclidean"),
min_pairs = 10,
bootstraps = 100,
progress = TRUE) {
method <- match.arg(method)
if (is(profile_matrix, "MSnSet")) {
profile_matrix <- exprs(profile_matrix)
}
# transpose for correlations
profile_matrix = t(profile_matrix)
# construct network
n <- crossprod(!is.na(profile_matrix))
if (method == "pearson") {
network <- cor(profile_matrix, use = 'pairwise.complete.obs')
} else if (method == "euclidean") {
network <- as.matrix(dist(t(profile_matrix)))
}
network[n < min_pairs] = NA
# do permutation testing
z_scores <- numeric(0)
pb <- progress_bar$new(
format = "complex :what [:bar] :percent eta: :eta",
clear = FALSE, total = length(complexes), width = 80)
for (i in seq_along(complexes)) {
complex_name <- names(complexes)[i]
complex <- complexes[[i]]
# subset complex to proteins present in this network
nodes <- colnames(network)
overlap <- intersect(complex, nodes)
# abort if overlap size is < 3
if (length(overlap) < 3) {
z_scores[complex_name] <- NA
} else {
# calculate median PCC for intra-complex interactions
idxing_mat <- t(combn(overlap, 2))
edge_weights <- na.omit(network[idxing_mat])
obs <- median(edge_weights, na.rm = TRUE)
# compare to random expectation
rnd <- map_dbl(seq_len(bootstraps), ~ {
# generate random set of proteins equivalent to # of complex subunits
# present in this network
rnd_complex <- sample(nodes, length(overlap))
# calculate observed D statistic
idxing_mat <- t(combn(rnd_complex, 2))
rnd_weights <- as.numeric(na.omit(network[idxing_mat]))
median(rnd_weights, na.rm = TRUE)
})
# calculate Z score
z <- (obs - mean(rnd, na.rm = TRUE)) / sd(rnd, na.rm = TRUE)
z_scores[complex_name] <- z
}
# tick progress bar
if (progress) {
pb$tick(tokens = list(what = sprintf(
paste0("%-", nchar(length(complexes)), "s"), i)))
}
}
return(z_scores)
}
```

**Any scripts or data that you put into this service are public.**

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.