Nothing

```
#' Calculate the co-apex score for every protein pair
#'
#' Calculate the co-apex score for every pair of proteins. This is defined as
#' the minimum Euclidean distance between any two Gaussians fit to each
#' profile.
#'
#' @param gaussians a list of Gaussian mixture models fit to the profile matrix
#' by \code{link{build_gaussians}}
#' @param proteins all proteins being scored, optionally including those
#' without Gaussian fits
#'
#' @return a matrix of co-apex scores
#'
#' @examples
#' data(scott_gaussians)
#' gauss <- scott_gaussians[seq_len(25)]
#' CA <- co_apex(gauss)
#'
#' @importFrom stats dist
#' @importFrom purrr map
#'
#' @export
co_apex <- function(gaussians, proteins = NULL) {
if (is.null(proteins)) {
proteins <- names(gaussians)
}
n_proteins <- length(proteins)
n_gaussians <- length(gaussians)
gaussian_names <- names(gaussians)
## first, calculate Euclidean distance between every pair of Gaussians
gaussian_centers <- map(gaussians, c("coefs", "mu"))
gaussian_sigmas <- map(gaussians, c("coefs", "sigma"))
gaussian_matrix <- cbind(unlist(gaussian_centers), unlist(gaussian_sigmas))
CA <- as.matrix(dist(gaussian_matrix))
## get indices for each protein
gaussian_indices <- rep(gaussian_names, lengths(gaussian_centers))
## calculate min co-apex score
co_apex <- matrix(NA, nrow = n_proteins, ncol = n_proteins,
dimnames = list(proteins, proteins))
gaussian_names <- intersect(names(gaussians), proteins)
for (protein_A in gaussian_names) {
idxs_A <- which(gaussian_indices == protein_A)
for (protein_B in gaussian_names) {
idxs_B <- which(gaussian_indices == protein_B)
co_apex[protein_A, protein_B] <- min(CA[idxs_A, idxs_B])
}
}
return(co_apex)
}
```

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