protti: calculate_imputation – R documentation

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calculate_imputation

Sampling of values for imputation

Description

calculate_imputation is a helper function that is used in the impute function. Depending on the type of missingness and method, it samples values from a normal distribution that can be used for the imputation. Note: The input intensities should be log2 transformed.

Usage

calculate_imputation(
  min = NULL,
  noise = NULL,
  mean = NULL,
  sd,
  missingness = c("MNAR", "MAR"),
  method = c("ludovic", "noise"),
  skip_log2_transform_error = FALSE
)

Arguments

`min`	minimal intensity value of the precursor/peptide. Is only required if `method = "ludovic"` and `missingness = "MNAR"`.
`noise`	noise value for the precursor/peptide. Is only required if `method = "noise"` and `missingness = "MNAR"`.
`mean`	mean intensity value of the condition with missing values for a given precursor/peptide. Is only required if `missingness = "MAR"`.
`sd`	mean of the standard deviation of all conditions for a given precursor/peptide.
`missingness`	the missingness type of the data determines how values for imputation are sampled. This can be `"MAR"` or `"MNAR"`.
`method`	the method to be used for imputation. For `method = "ludovic"`, MNAR missingness is sampled around a value that is three lower (log2) than the lowest intensity value recorded for the precursor/peptide. For `method = "noise"`, MNAR missingness is sampled around the noise value for the precursor/peptide.
`skip_log2_transform_error`	logical, if FALSE a check is performed to validate that input values are log2 transformed. If input values are > 40 the test is failed and an error is thrown.

Value

A vector of values for the imputation of missing data. The length of the vector depends on the number of replicates.

protti

Bottom-Up Proteomics and LiP-MS Quality Control and Data Analysis Tools

v0.1.1

MIT + file LICENSE

Authors

Jan-Philipp Quast [aut, cre], Dina Schuster [aut], ETH Zurich [cph, fnd]

Initial release