dist_approx_impl: Distance method for 'proDAFit' object

In proDA: Differential Abundance Analysis of Label-Free Mass Spectrometry Data

Description

The method calculates either the euclidean distance between samples or proteins taking into account the missing values and the associated uncertainty. Because with missing value no single deterministic distance can be calculated two objects are returned: the mean and the associated standard deviation of the distance estimates.

Usage

## S4 method for signature 'proDAFit'
dist_approx(object, by_sample = TRUE, blind = TRUE)

## S4 method for signature 'SummarizedExperiment'
dist_approx(object, by_sample = TRUE, blind = TRUE, ...)

## S4 method for signature 'ANY'
dist_approx(object, by_sample = TRUE, blind = TRUE, ...)

Arguments

object	the 'proDAFit' object for which we calculate the distance or a matrix like object for which 'proDAFit' is created internally
by_sample	a boolean that indicates if the distances is calculated between the samples ('by_sample = TRUE') or between the proteins ('by_sample = FALSE'). Default: 'TRUE'
blind	fit an intercept model for the missing values to make sure that the results are not biased for the expected result. Default: 'TRUE'
...	additional arguments to proDA() in case object is a SummarizedExperiment or a matrix

Value

a list with two elements: 'mean' and 'sd' both are formally of class "dist"

Examples

  syn_data <- generate_synthetic_data(n_proteins = 10)
  fit <- proDA(syn_data$Y, design = syn_data$groups)
  dist_approx(fit)

proDA documentation built on Nov. 8, 2020, 5:01 p.m.