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```
#' Deconvolve profiles into Gaussian mixture models
#'
#' Identify peaks in co-fractionation profiles by deconvolving peaks in
#' Gaussian mixture models. Models are mixtures of between 1 and 5 Gaussians.
#' Profiles are pre-processed prior to building Gaussians by filtering and
#' cleaning. By default, profiles with fewer than 5 non-missing points, or
#' fewer than 5 consecutive points after imputation of single missing values,
#' are removed. Profiles are cleaned by replacing missing values with
#' near-zero noise, imputing single missing values as the mean of neighboring
#' points, and smoothing with a moving average filter.
#'
#' @param profile_matrix a numeric matrix of co-elution profiles, with proteins
#' in rows, or a \code{\linkS4class{MSnSet}} object
#' @param min_points filter profiles without at least this many total,
#' non-missing points; passed to \code{\link{filter_profiles}}
#' @param min_consecutive filter profiles without at least this many
#' consecutive, non-missing points; passed to \code{\link{filter_profiles}}
#' @param impute_NA if true, impute single missing values with the average of
#' neighboring values; passed to \code{\link{clean_profiles}}
#' @param smooth if true, smooth the chromatogram with a moving average filter;
#' passed to \code{\link{clean_profiles}}
#' @param smooth_width width of the moving average filter, in fractions;
#' passed to \code{\link{clean_profiles}}
#' @param max_gaussians the maximum number of Gaussians to fit; defaults to 5.
#' Note that Gaussian mixtures with more parameters than observed (i.e.,
#' non-zero or NA) points will not be fit. Passed to
#' \code{\link{choose_gaussians}}
#' @param criterion the criterion to use for model selection;
#' one of "AICc" (corrected AIC, and default), "AIC", or "BIC". Passed to
#' \code{\link{choose_gaussians}}
#' @param max_iterations the number of times to try fitting the curve with
#' different initial conditions; defaults to 50. Passed to
#' \code{\link{fit_gaussians}}
#' @param min_R_squared the minimum R-squared value to accept when fitting the
#' curve with different initial conditions; defaults to 0.5. Passed to
#' \code{\link{fit_gaussians}}
#' @param method the method used to select the initial conditions for
#' nonlinear least squares optimization (one of "guess" or "random");
#' see \code{\link{make_initial_conditions}} for details. Passed to
#' \code{\link{fit_gaussians}}
#' @param filter_gaussians_center true or false: filter Gaussians whose centres
#' fall outside the bounds of the chromatogram. Passed to
#' \code{\link{fit_gaussians}}
#' @param filter_gaussians_height Gaussians whose heights are below this
#' fraction of the chromatogram height will be filtered. Setting this value to
#' zero disables height-based filtering of fit Gaussians. Passed to
#' \code{\link{fit_gaussians}}
#' @param filter_gaussians_variance_min Gaussians whose variance falls below
#' this number of fractions will be filtered. Setting this value to
#' zero disables filtering. Passed to
#' \code{\link{fit_gaussians}}
#' @param filter_gaussians_variance_max Gaussians whose variance is above
#' this number of fractions will be filtered. Setting this value to
#' zero disables filtering. Passed to
#' \code{\link{fit_gaussians}}
#'
#' @return a list of fit Gaussian mixture models, where each item in the
#' list contains the following five fields: the number of Gaussians used to fit
#' the curve; the R^2 of the fit; the number of iterations used to
#' fit the curve with different initial conditions; the coefficients of the
#' fit model; and the curve predicted by the fit model. Profiles that
#' could not be fit by a Gaussian mixture model above the minimum R-squared
#' cutoff will be absent from the returned list.
#'
#' @examples
#' data(scott)
#' mat <- clean_profiles(scott[seq_len(5), ])
#' gauss <- build_gaussians(mat, max_gaussians = 3)
#'
#' @importFrom MSnbase exprs
#' @importFrom methods is
#' @importFrom progress progress_bar
#'
#' @export
build_gaussians <- function(profile_matrix,
min_points = 1, min_consecutive = 5,
impute_NA = TRUE, smooth = TRUE, smooth_width = 4,
max_gaussians = 5,
criterion = c("AICc", "AIC", "BIC"),
max_iterations = 50, min_R_squared = 0.5,
method = c("guess", "random"),
filter_gaussians_center = TRUE,
filter_gaussians_height = 0.15,
filter_gaussians_variance_min = 0.5,
filter_gaussians_variance_max = 50) {
if (is(profile_matrix, "MSnSet")) {
profile_matrix <- exprs(profile_matrix)
}
# preprocess chromatograms: filter and clean
filtered <- filter_profiles(profile_matrix,
min_points = min_points,
min_consecutive = min_consecutive)
cleaned <- clean_profiles(filtered,
impute_NA = impute_NA,
smooth = smooth,
smooth_width = smooth_width)
# fit Gaussians, displaying progress bar
gaussians <- list()
proteins <- rownames(cleaned)
P <- length(proteins)
message(".. fitting Gaussian mixture models to ", P, " profiles")
pb <- progress_bar$new(
format = "fitting :what [:bar] :percent eta: :eta",
clear = FALSE, total = P, width = 80)
max_len <- max(nchar(proteins))
for (i in seq_len(P)) {
protein <- proteins[i]
pb$tick(tokens = list(what = sprintf(paste0("%-", max_len, "s"), protein)))
chromatogram <- cleaned[protein,]
points <- sum(!is.na(profile_matrix[protein,]))
gaussian <- choose_gaussians(chromatogram, points,
max_gaussians, criterion,
max_iterations, min_R_squared,
method, filter_gaussians_center,
filter_gaussians_height,
filter_gaussians_variance_min,
filter_gaussians_variance_max)
gaussians[[protein]] <- gaussian
}
return(gaussians)
}
```

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