Estimate the mean abundance and variance of each protein in each condition.
Data matrix with protein abundance. Rows are proteins and columns are Biological replicates or samples.
Group information for samples in data. ‘BioReplicate’ for sample ID and ‘Condition’ for group information are required. ‘BioReplicate’ information should be the same with the column of ‘data’.
The function fits intensity-based linear model on the input data ‘data’. This function outputs variance components and mean abundance for each protein.
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.
Ting Huang, Meena Choi, Olga Vitek
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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)
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