dataSummarizationLiP {MSstatsLiP} | R Documentation

## Summarizes LiP and TrP datasets seperately using methods from MSstats.

### Description

Utilizes functionality from MSstats and MSstatsPTM to clean, summarize, and normalize LiP peptide and TrP global protein data. Imputes missing values, protein and LiP peptide level summarization from peptide level quantification. Applies global median normalization on peptide level data and normalizes between runs. Returns list of two summarized datasets.

### Usage

    dataSummarizationLiP(
      data,
      logTrans = 2,
      normalization = "equalizeMedians",
      normalization.LiP = "equalizeMedians",
      nameStandards = NULL,
      nameStandards.LiP = NULL,
      featureSubset = "all",
      featureSubset.LiP = "all",
      remove_uninformative_feature_outlier = FALSE,
      remove_uninformative_feature_outlier.LiP = FALSE,
      min_feature_count = 2,
      min_feature_count.LiP = 1,
      n_top_feature = 3,
      n_top_feature.LiP = 3,
      summaryMethod = "TMP",
      equalFeatureVar = TRUE,
      censoredInt = "NA",
      MBimpute = TRUE,
      MBimpute.LiP = FALSE,
      remove50missing = FALSE,
      fix_missing = NULL,
      maxQuantileforCensored = 0.999,
      use_log_file = FALSE,
      append = FALSE,
      verbose = TRUE,
      log_file_path = NULL,
      base = "MSstatsLiP_log_"
    )

### Arguments

`data` | name of the list with LiP and TrP data.tables, which can be the output of the MSstatsPTM converter functions
`logTrans` | logarithm transformation with base 2(default) or 10
`normalization` | normalization for the protein level dataset, to remove systematic bias between MS runs. There are three different normalizations supported. 'equalizeMedians'(default) represents constant normalization (equalizing the medians) based on reference signals is performed. 'quantile' represents quantile normalization based on reference signals is performed. 'globalStandards' represents normalization with global standards proteins. FALSE represents no normalization is performed
`normalization.LiP` | normalization for LiP level dataset. Default is 'equalizeMedians'. Can be adjusted to any of the options described above.
`nameStandards` | vector of global standard peptide names for protein dataset. only for normalization with global standard peptides.
`nameStandards.LiP` | Same as above for LiP dataset.
`featureSubset` | For protein dataset only. "all"(default) uses all features that the data set has. "top3" uses top 3 features which have highest average of log2(intensity) across runs. "topN" uses top N features which has highest average of log2(intensity) across runs. It needs the input for n_top_feature option. "highQuality" flags uninformative feature and outliers
`featureSubset.LiP` | For LiP dataset only. Options same as above.
`remove_uninformative_feature_outlier` | For protein dataset only. It only works after users used featureSubset="highQuality" in dataProcess. TRUE allows to remove 1) the features are flagged in the column, feature_quality="Uninformative" which are features with bad quality, 2) outliers that are flagged in the column, is_outlier=TRUE, for run-level summarization. FALSE (default) uses all features and intensities for run-level summarization.
`remove_uninformative_feature_outlier.LiP` | For LiP dataset only. Options same as above.
`min_feature_count` | optional. Only required if featureSubset = "highQuality". Defines a minimum number of informative features a protein needs to be considered in the feature selection algorithm.
`min_feature_count.LiP` | For LiP dataset only. Options the same as above.
`n_top_feature` | For protein dataset only. The number of top features for featureSubset='topN'. Default is 3, which means to use top 3 features.
`n_top_feature.LiP` | For LiP dataset only. Options same as above.
`summaryMethod` | "TMP"(default) means Tukey's median polish, which is robust estimation method. "linear" uses linear mixed model.
`equalFeatureVar` | only for summaryMethod="linear". default is TRUE. Logical variable for whether the model should account for heterogeneous variation among intensities from different features. Default is TRUE, which assume equal variance among intensities from features. FALSE means that we cannot assume equal variance among intensities from features, then we will account for heterogeneous variation from different features.
`censoredInt` | 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.
`MBimpute` | For protein dataset only. 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.
`MBimpute.LiP` | For LiP dataset only. Options same as above. Default is FALSE.
`remove50missing` | only for summaryMethod="TMP". TRUE removes the runs which have more than 50% missing values. FALSE is default.
`fix_missing` | Default is Null. Optional, same as the 'fix_missing' parameter in MSstatsConvert::MSstatsBalancedDesign function
`maxQuantileforCensored` | Maximum quantile for deciding censored missing values. default is 0.999
`use_log_file` | logical. If TRUE, information about data processing will be saved to a file.
`append` | logical. If TRUE, information about data processing will be added to an existing log file.
`verbose` | logical. If TRUE, information about data processing will be printed to the console.
`log_file_path` | character. Path to a file to which information about data processing will be saved. If not provided, such a file will be created automatically. If `append = TRUE`, has to be a valid path to a file.
`base` | start of the file name.

### Value

list of summarized LiP and TrP results. These results contain the reformatted input to the summarization function, as well as run-level summarization results.

### Examples

    # Use output of converter
    head(MSstatsLiP_data[["LiP"]])
    head(MSstatsLiP_data[["TrP"]])

    # Run summarization
    MSstatsLiP_model <- dataSummarizationLiP(MSstatsLiP_data)

[Package MSstatsLiP version 1.0.0 Index]