Package performs summarization of replicates, filtering by frequency, several different options for imputing missing data, and a variety of options for transforming, batch correcting, and normalizing data
Package for pre-analytic processing of mass spectrometry quantification data. Four functions are provided and are intended to be used in sequence (as a pipeline) to produce processed and normalized data. These are msSummarize(), msFilter(), msImpute(), and msNormalize(). The function msPrepare() is also provided as a wrapper function combining the four previously mentioned functions.
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# Load example data data(msquant) # Call function to tidy, summarize, filter, impute, and normalize data preparedDF <- msPrepare(msquant, minPropPresent = 1/3, missingValue = 1, filterPercent = 0.8, imputeMethod = "knn", normalizeMethod = "quantile + ComBat", transform = "log10", covariatesOfInterest = c("spike"), compVars = c("mz", "rt"), sampleVars = c("spike", "batch", "replicate", "subject_id"), colExtraText = "Neutral_Operator_Dif_Pos_", separator = "_")
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