predict_feature_parameters: Find feature parameters for elements of X with a fixed set of...

In const-ae/proDD: Identifying Differentially Abundant Proteins from Label-Free Mass Spectrometry Data

Description

Find feature parameters for elements of X with a fixed set of hyperparameters

Usage

predict_feature_parameters(X, experimental_design, hyper_params = NULL,
  mu0, sigma20, nu, eta, rho, zeta, mup = NULL, sigma2p = NULL,
  sigma2mup = NULL, max_iter = 10, epsilon = 0.001,
  verbose = FALSE)

Arguments

X	data matrix
experimental_design	a vector that specifies which samples belong to the same condition.
hyper_params	a list with 5 elements ('eta', 'nu', 'mu0', 'sigma20', 'rho', and 'zeta'). Alternatively the 'hyper_params' can be specified individually.
mu0	the global mean around which the row means are drawn
sigma20	the global variance specifying the spread of means around 'mu0'.
nu	degrees of freedom for the the global variance prior.
eta	scale of the global variance prior.
rho	vector specifying the intensity where the chance of a dropout is 50/50. Length is either one or ncol(X).
zeta	vector specifying the scale of the dropout curve. Length is either one or ncol(X).
mup	Optional matrix that fixes the mean for each row and condition. Default 'NULL'
sigma2p	Optional vector that fixes the variance for each row. Default 'NULL'
sigma2mup	Optional matrix that fixes the uncertainty of the mean for each row and condition. Default 'NULL'.
max_iter	the maximum number of iterations. Default: 10
epsilon	the error under which the result is considered converged. Default: 0.001
verbose	boolean that indicates if verbose output is printed to the console.

Value

list with three elements

mup

a matrix with size nrow(X) * unique(experimental_design) with the means for each feature

sigma20

a numeric vector with the variance for each feature

sigma2mup

a matrix with size nrow(X) * unique(experimental_design) with the uncertainty for each 'mup'

const-ae/proDD documentation built on Jan. 14, 2020, 9:34 a.m.