proteinSummarization {MSstatsTMT}R Documentation

Summarizing PSM level quantification to protein level quantification

Description

We assume missing values are censored and then impute the missing values. Protein-level summarization from PSM level quantification should be performed before testing differentially abundant proteins. After all, normalization between MS runs using normalization channels will be implemented.

Usage

proteinSummarization(data, method = "msstats", normalization = TRUE,
  MBimpute = TRUE, maxQuantileforCensored = NULL)

Arguments

data

Name of the output of PDtoMSstatsTMTFormat function or PSM-level quantified data from other tools. It should have columns named Protein, PSM, Mixture, Run, Channel, Condition, BioReplicate, Intensity

method

Four different summarization methods to protein-level can be performed : "msstats"(default), "MedianPolish", "Median", "LogSum".

normalization

Normalization between MS runs. TRUE(default) needs at least one normalization channel in each MS run, annotated by 'Norm' in Condtion column. It will be performed after protein-level summarization. FALSE will not perform normalization step. If data only has one run, then normalization=FALSE.

MBimpute

only for method="msstats". TRUE (default) imputes missing values by Accelated failure model. FALSE uses minimum value to impute the missing value for each PSM.

maxQuantileforCensored

We assume missing values are censored. maxQuantileforCensored is Maximum quantile for deciding censored missing value, for instance, 0.999. Default is Null.

Value

data.frame with protein-level summarization for each run and channel

Examples

data(input.pd)

quant.pd.msstats <- proteinSummarization(input.pd,
                                         method="msstats",
                                         normalization=TRUE)
head(quant.pd.msstats)

[Package MSstatsTMT version 1.2.0 Index]