MSstatsSummarizeSingleTMP: Tukey Median Polish summarization for a single protein

View source: R/dataProcess.R

MSstatsSummarizeSingleTMPR Documentation

Tukey Median Polish summarization for a single protein

Description

Tukey Median Polish summarization for a single protein

Usage

MSstatsSummarizeSingleTMP(
  single_protein,
  impute,
  censored_symbol,
  remove50missing
)

Arguments

single_protein

feature-level data for a single protein

impute

only for summaryMethod = "TMP" and censoredInt = 'NA' or '0'. TRUE (default) imputes 'NA' or '0' (depending on censoredInt option) by Accelated failure model. FALSE uses the values assigned by cutoffCensored

censored_symbol

Missing values are censored or at random. 'NA' (default) assumes that all 'NA's in 'Intensity' column are censored. '0' uses zero intensities as censored intensity. In this case, NA intensities are missing at random. The output from Skyline should use '0'. Null assumes that all NA intensites are randomly missing.

remove50missing

only for summaryMethod = "TMP". TRUE removes the proteins where every run has at least 50% missing values for each peptide. FALSE is default.

Value

list of two data.tables: one with fitted survival model, the other with protein-level data

Examples

raw = DDARawData 
method = "TMP"
cens = "NA"
impute = TRUE 
# currently, MSstats only supports MBimpute = FALSE for linear summarization
MSstatsConvert::MSstatsLogsSettings(FALSE)
input = MSstatsPrepareForDataProcess(raw, 2, NULL)
input = MSstatsNormalize(input, "EQUALIZEMEDIANS")
input = MSstatsMergeFractions(input)
input = MSstatsHandleMissing(input, "TMP", TRUE, "NA", 0.999)
input = MSstatsSelectFeatures(input, "all")
input = MSstatsPrepareForSummarization(input, method, impute, cens, FALSE)
input_split = split(input, input$PROTEIN)
single_protein_summary = MSstatsSummarizeSingleTMP(input_split[[1]],
                                                   impute, cens, FALSE)
head(single_protein_summary[[1]])


MeenaChoi/MSstats documentation built on July 13, 2024, 10:59 a.m.