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 1.1.2 Index]