estimateVar: Estimate the mean abundance and variance of each protein in...

In MSstatsSampleSize: Simulation tool for optimal design of high-dimensional MS-based proteomics experiment

Description

Estimate the mean abundance and variance of each protein in each condition.

Usage

estimateVar(data, annotation)

Arguments

data	Data matrix with protein abundance. Rows are proteins and columns are Biological replicates or samples.
annotation	Group information for samples in data. ‘Run’ for MS run, ‘BioReplicate’ for biological subject ID and ‘Condition’ for group information are required. ‘Run’ information should be the same with the column of ‘data’. Multiple ‘Run’ may come from same ‘BioReplicate’.

Details

The function fits intensity-based linear model on the input data ‘data’. This function outputs variance components and mean abundance for each protein.

Value

model is the list of linear models trained for each protein.

mu is the mean abundance matrix of each protein in each phenotype group.

sigma is the sd matrix of each protein in each phenotype group.

promean is the mean abundance vector of each protein across all the samples.

protein is proteins, correpsonding to the rows in mu and sigma or the element of promean.

Author(s)

Ting Huang, Meena Choi, Olga Vitek

Examples

data(OV_SRM_train)
data(OV_SRM_train_annotation)

# estimate the mean protein abunadnce and variance in each condition
variance_estimation <- estimateVar(data = OV_SRM_train,
                                   annotation = OV_SRM_train_annotation)

# the mean protein abundance in each condition
head(variance_estimation$mu)

# the standard deviation in each condition
head(variance_estimation$sigma)

# the mean protein abundance across all the conditions
head(variance_estimation$promean)

MSstatsSampleSize documentation built on Nov. 8, 2020, 4:53 p.m.