build_gaussians: Deconvolve profiles into Gaussian mixture models

In PrInCE: Predicting Interactomes from Co-Elution

Description

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.

Usage

build_gaussians(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)

Arguments

profile_matrix	a numeric matrix of co-elution profiles, with proteins in rows, or a MSnSet object
min_points	filter profiles without at least this many total, non-missing points; passed to filter_profiles
min_consecutive	filter profiles without at least this many consecutive, non-missing points; passed to filter_profiles
impute_NA	if true, impute single missing values with the average of neighboring values; passed to clean_profiles
smooth	if true, smooth the chromatogram with a moving average filter; passed to clean_profiles
smooth_width	width of the moving average filter, in fractions; passed to clean_profiles
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 choose_gaussians
criterion	the criterion to use for model selection; one of "AICc" (corrected AIC, and default), "AIC", or "BIC". Passed to choose_gaussians
max_iterations	the number of times to try fitting the curve with different initial conditions; defaults to 50. Passed to fit_gaussians
min_R_squared	the minimum R-squared value to accept when fitting the curve with different initial conditions; defaults to 0.5. Passed to fit_gaussians
method	the method used to select the initial conditions for nonlinear least squares optimization (one of "guess" or "random"); see make_initial_conditions for details. Passed to fit_gaussians
filter_gaussians_center	true or false: filter Gaussians whose centres fall outside the bounds of the chromatogram. Passed to fit_gaussians
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 fit_gaussians
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 fit_gaussians
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 fit_gaussians

Value

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)

PrInCE documentation built on Nov. 8, 2020, 6:34 p.m.