dataSummarizationPTM_TMT: Process MS PTM and global protein data produced via tandem...

In devonjkohler/MSstatsPTM: Statistical Characterization of Post-translational Modifications

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)

devonjkohler/MSstatsPTM documentation built on Dec. 19, 2021, 11:01 p.m.