Prepare feature-level data for protein-level summarization

MSstatsPrepareForSummarization(
  input,
  method,
  impute,
  censored_symbol,
  remove_uninformative_feature_outlier
)

Arguments

input

feature-level data processed by dataProcess subfunctions

method

summarization method - `summaryMethod` parameter of the dataProcess function

impute

if TRUE, censored missing values will be imputed - `MBimpute` parameter of the dataProcess function

censored_symbol

censored missing value indicator - `censoredInt` parameter of the dataProcess function

remove_uninformative_feature_outlier

if TRUE, features labeled as outlier of uninformative by the MSstatsSelectFeatures function will not be used in summarization

Value

data.table

Examples

raw = DDARawData method = "TMP" cens = "NA" impute = TRUE MSstatsConvert::MSstatsLogsSettings(FALSE) input = MSstatsPrepareForDataProcess(raw, 2, NULL)
#> INFO [2021-07-05 20:05:28] ** Features with one or two measurements across runs are removed. #> INFO [2021-07-05 20:05:28] ** Fractionation handled. #> INFO [2021-07-05 20:05:28] ** Updated quantification data to make balanced design. Missing values are marked by NA
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 #> 1: 1 1 1 757400.1 19.53070 1 #> 2: 1 1 1 2087125.8 20.99309 1 #> 3: 1 1 1 1485145.8 20.50217 1 #> 4: 1 1 1 4986404.0 22.24957 1 #> 5: 1 1 1 2488141.2 21.24664 1 #> 6: 1 1 1 7519322.0 22.84217 1