Linear model-based summarization for a single protein

Linear model-based summarization for a single protein

MSstatsSummarizeSingleLinear(single_protein, equal_variances = TRUE)

Arguments

single_protein

feature-level data for a single protein
equal_variances

if TRUE, observation are assumed to be homoskedastic

Value

list with protein-level data

Examples

raw = DDARawData
method = "linear"
cens = NULL
impute = FALSE
# 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 = MSstatsSummarizeSingleLinear(input_split[[1]])
head(single_protein_summary[[1]])
#>    Protein RUN LogIntensities
#> 1:  bovine   1       21.39496
#> 2:  bovine   2       20.94576
#> 3:  bovine   3       20.91096
#> 4:  bovine   4       21.83630
#> 5:  bovine   5       21.61791
#> 6:  bovine   6       21.35518