Tukey Median Polish summarization for a single protein

Tukey Median Polish summarization for a single protein

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 runs which have more than 50% missing values. 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)
#> INFO  [2021-07-05 20:05:30] ** Features with one or two measurements across runs are removed.
#> INFO  [2021-07-05 20:05:30] ** Fractionation handled.
#> INFO  [2021-07-05 20:05:30] ** Updated quantification data to make balanced design. Missing values are marked by NA
input = MSstatsNormalize(input, "EQUALIZEMEDIANS")
input = MSstatsMergeFractions(input)
input = MSstatsHandleMissing(input, "TMP", TRUE, "NA", 0.999)
#> INFO  [2021-07-05 20:05:30] ** Log2 intensities under cutoff = 13.456  were considered as censored missing values.
#> INFO  [2021-07-05 20:05:30] ** Log2 intensities = NA were considered as censored missing values.
input = MSstatsSelectFeatures(input, "all")
#> INFO  [2021-07-05 20:05:30] ** Use all features that the dataset originally has.
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]])
#>    RUN LogIntensities Protein
#> 1:   1       21.28437  bovine
#> 2:   2       20.85653  bovine
#> 3:   3       20.67521  bovine
#> 4:   4       21.60443  bovine
#> 5:   5       21.82186  bovine
#> 6:   6       21.20445  bovine