pepa.test: PEptide based Protein differential Abundance test

In prostarproteomics/DAPAR: Tools for the Differential Analysis of Proteins Abundance with R

PEptide based Protein differential Abundance test

Description

PEptide based Protein differential Abundance test

Usage

pepa.test(X, y, n1, n2, global = FALSE, use.lm = FALSE)

Arguments

X	Binary q x p design matrix for q peptides and p proteins. X_(ij)=1 if peptide i belongs to protein j, 0 otherwise.
y	q x n matrix representing the log intensities of q peptides among n MS samples.
n1	number of samples under condition 1. It is assumed that the first n1 columns of y correspond to observations under condition 1.
n2	number of samples under condition 2.
global	if TRUE, the test statistic for each protein uses all residues, including the ones for peptides in different connected components. Can be much faster as it does not require to compute connected components. However the p-values are not well calibrated in this case, as it amounts to adding a ridge to the test statistic. Calibrating the p-value would require knowing the amplitude of the ridge, which in turns would require computing the connected components.
use.lm	if TRUE (and if global=FALSE), use lm() rather than the result in Proposition 1 to compute the test statistic

Value

A list of the following elements: llr: log likelihood ratio statistic (maximum likelihood version). llr.map: log likelihood ratio statistic (maximum a posteriori version). llr.pv: p-value for llr. llr.map.pv: p-value for llr.map. mse.h0: Mean squared error under H0 mse.h1: Mean squared error under H1 s: selected regularization hyperparameter for llr.map. wchi2: weight used to make llr.map chi2-distributed under H0.

Author(s)

Thomas Burger, Laurent Jacob

Examples

data(Exp1_R25_pept, package="DAPARdata")
protID <- "Protein_group_IDs"
obj <- Exp1_R25_pept[seq_len(20)]
X <- BuildAdjacencyMatrix(obj, protID, FALSE)

prostarproteomics/DAPAR documentation built on Nov. 26, 2024, 6:32 p.m.