build_gaussians {PrInCE} | R Documentation

## Deconvolve profiles into Gaussian mixture models

### 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)

[Package PrInCE version 1.2.0 Index]