proteinSummarization: Summarizing peptide level quantification to protein level...

In huang704/MSstatsTMT: Protein Significance Analysis in shotgun mass spectrometry-based proteomic experiments with tandem mass tag (TMT) labeling

Description

We assume missing values are censored and then impute the missing values. Protein-level summarization from peptide level quantification are performed. After all, global median normalization on peptide level data and normalization between MS runs using reference channels will be implemented.

Usage

proteinSummarization(
  data,
  method = "msstats",
  global_norm = TRUE,
  reference_norm = TRUE,
  remove_norm_channel = TRUE,
  remove_empty_channel = TRUE,
  MBimpute = TRUE,
  maxQuantileforCensored = NULL
)

Arguments

data	Name of the output of PDtoMSstatsTMTFormat function or peptide-level quantified data from other tools. It 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 peptide level data (equalizing the medians across all the channels and MS runs). Default is TRUE. It will be performed before protein-level summarization.
reference_norm	Reference channel based normalization between MS runs on 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.
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.
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",
                                         global_norm=TRUE,
                                         reference_norm=TRUE)
head(quant.pd.msstats)

huang704/MSstatsTMT documentation built on Jan. 1, 2021, 3:21 a.m.