dataSummarizationLiP: Summarizes LiP and TrP datasets seperately using methods from...

In Vitek-Lab/MSstatsLiP: LiP Significance Analysis in shotgun mass spectrometry-based proteomic experiments

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)

Vitek-Lab/MSstatsLiP documentation built on April 5, 2024, 3:25 a.m.