dataSummarizationPTM_TMT {MSstatsPTM} | R Documentation

## Process MS PTM and global protein data produced via tandem mass tag labeling

### Description

Utilizes functionality from MSstatsTMT to clean, summarize, and normalize PTM and protein level data. Imputes missing values, protein and PTM level summarization from peptide level quantification. Applies global median normalization on peptide level data and normalizes between runs.

### Usage

    dataSummarizationPTM_TMT(
      data,
      method = "msstats",
      global_norm = TRUE,
      global_norm.PTM = TRUE,
      reference_norm = TRUE,
      reference_norm.PTM = TRUE,
      remove_norm_channel = TRUE,
      remove_empty_channel = TRUE,
      MBimpute = TRUE,
      MBimpute.PTM = TRUE,
      maxQuantileforCensored = NULL,
      use_log_file = TRUE,
      append = FALSE,
      verbose = TRUE,
      log_file_path = NULL
    )

### Arguments

`data` | Name of the output of MSstatsPTM converter function or peptide-level quantified data from other tools. It should be a list containing one or two data tables, named PTM and PROTEIN for modified and unmodified datasets. The list must at least contain the PTM dataset. The data should have columns ProteinName, PeptideSequence, Charge, PSM, Mixture, TechRepMixture, Run, Channel, Condition, BioReplicate, Intensity
`method` | Four different summarization methods to protein-level can be performed : "msstats"(default), "MedianPolish", "Median", "LogSum".
`global_norm` | Global median normalization on for unmodified peptide level data (equalizing the medians across all the channels and MS runs). Default is TRUE. It will be performed before protein-level summarization.
`global_norm.PTM` | Same as above for modified peptide level data. Default is TRUE.
`reference_norm` | Reference channel based normalization between MS runs on unmodified protein level data. TRUE(default) needs at least one reference channel in each MS run, annotated by 'Norm' in Condtion column. It will be performed after protein-level summarization. FALSE will not perform this normalization step. If data only has one run, then reference_norm=FALSE.
`reference_norm.PTM` | Same as above for modified peptide level data. Default is TRUE.
`remove_norm_channel` | TRUE(default) removes 'Norm' channels from protein level data.
`remove_empty_channel` | TRUE(default) removes 'Empty' channels from protein level data.
`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 peptide precursor ion.
`MBimpute.PTM` | Same as above for modified peptide level data. Default is TRUE
`maxQuantileforCensored` | We assume missing values are censored. maxQuantileforCensored is Maximum quantile for deciding censored missing value, for instance, 0.999. Default is Null.
`use_log_file` | logical. If TRUE, information about data processing will be saved to a file.
`append` | logical. If TRUE, information about data processing will be added to an existing log file.
`verbose` | logical. If TRUE, information about data processing will be printed to the console.
`log_file_path` | character. Path to a file to which information about data processing will be saved. If not provided, such a file will be created automatically. If `append = TRUE`, has to be a valid path to a file.

### Value

list of two data.tables

### Examples

    head(raw.input.tmt$PTM)
    head(raw.input.tmt$PROTEIN)

    quant.tmt.msstatsptm <- dataSummarizationPTM_TMT(raw.input.tmt,
                                                     method = "msstats",
                                                     verbose = FALSE)
    head(quant.tmt.msstatsptm$PTM$ProteinLevelData)

[Package MSstatsPTM version 1.4.2 Index]