sva.id: Surrogate Variable Analysis

In ProteoMM: Multi-Dataset Model-based Differential Expression Proteomics Analysis Platform

Description

Surrogate Variable Analysis function used internatlly by eig_norm1 and eig_norm2 Here we incorporate the model matrix from EigenMS normalization to find the significant trends in the matrix of residuals.

Usage

sva.id(dat, n.u.treatment, lm.fm, B = 500, sv.sig = 0.05)

Arguments

dat	number of peptides/genes x number of samples matrix of expression data with no missing values
n.u.treatment	number of treatment groups
lm.fm	formular for treatment to be use on the right side of the call to stats::lm() as generated by makeLMFormula()
B	The number of null iterations to perform
sv.sig	The significance cutoff for the surrogate variables

Value

A data structure with the following values:

n.sv

Number of significant surrogate variables

p.sv

Significance for the returned surrogate variables

ProteoMM documentation built on Nov. 8, 2020, 5:57 p.m.