Handle censored missing values

MSstatsHandleMissing(
  input,
  summary_method,
  impute,
  missing_symbol,
  censored_cutoff
)

Arguments

input

`data.table` in MSstats data format

summary_method

summarization method (`summaryMethod` parameter to `dataProcess`)

impute

if TRUE, missing values are supposed to be imputed (`MBimpute` parameter to `dataProcess`)

missing_symbol

`censoredInt` parameter to `dataProcess`

censored_cutoff

`maxQuantileforCensored` parameter to `dataProcess`

Value

data.table

Examples

raw = DDARawData method = "TMP" cens = "NA" impute = TRUE MSstatsConvert::MSstatsLogsSettings(FALSE) input = MSstatsPrepareForDataProcess(raw, 2, NULL)
#> INFO [2021-07-05 20:05:27] ** Features with one or two measurements across runs are removed. #> INFO [2021-07-05 20:05:27] ** Fractionation handled. #> INFO [2021-07-05 20:05:27] ** 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 20:05:27] ** Log2 intensities under cutoff = 13.456 were considered as censored missing values. #> INFO [2021-07-05 20:05:27] ** Log2 intensities = NA were considered as censored missing values.
head(input)
#> PROTEIN PEPTIDE TRANSITION #> 1: bovine D.GPLTGTYR_23_23 NA_NA #> 2: bovine F.HFHWGSSDDQGSEHTVDR_402_402 NA_NA #> 3: bovine F.HWGSSDDQGSEHTVDR_229_229 NA_NA #> 4: bovine G.PLTGTYR_8_8 NA_NA #> 5: bovine H.SFNVEYDDSQDK_465_465 NA_NA #> 6: bovine K.AVVQDPALKPL_156_156 NA_NA #> FEATURE LABEL GROUP_ORIGINAL SUBJECT_ORIGINAL RUN #> 1: D.GPLTGTYR_23_23_NA_NA L C1 1 1 #> 2: F.HFHWGSSDDQGSEHTVDR_402_402_NA_NA L C1 1 1 #> 3: F.HWGSSDDQGSEHTVDR_229_229_NA_NA L C1 1 1 #> 4: G.PLTGTYR_8_8_NA_NA L C1 1 1 #> 5: H.SFNVEYDDSQDK_465_465_NA_NA L C1 1 1 #> 6: K.AVVQDPALKPL_156_156_NA_NA L C1 1 1 #> GROUP SUBJECT FRACTION INTENSITY ABUNDANCE originalRUN censored #> 1: 1 1 1 757400.1 19.83052 1 FALSE #> 2: 1 1 1 2087125.8 21.29291 1 FALSE #> 3: 1 1 1 1485145.8 20.80200 1 FALSE #> 4: 1 1 1 4986404.0 22.54939 1 FALSE #> 5: 1 1 1 2488141.2 21.54646 1 FALSE #> 6: 1 1 1 7519322.0 23.14200 1 FALSE