protti: filter_cv – R documentation

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filter_cv

Data filtering based on coefficients of variation (CV)

Description

Filters the input data based on precursor, peptide or protein intensity coefficients of variation. The function should be used to ensure that only robust measurements and quantifications are used for data analysis. It is advised to use the function after inspection of raw values (quality control) and median normalisation. Generally, the function calculates CVs of each peptide, precursor or protein for each condition and removes peptides, precursors or proteins that have a CV above the cutoff in less than the (user-defined) required number of conditions. Since the user-defined cutoff is fixed and does not depend on the number of conditions that have detected values, the function might bias for data completeness.

Usage

filter_cv(
  data,
  grouping,
  condition,
  log2_intensity,
  cv_limit = 0.25,
  min_conditions,
  silent = FALSE
)

Arguments

`data`	Data frame containing at least the input variables.
`grouping`	Column in the data frame containing the grouping variable that can be either precursors, peptides or proteins.
`condition`	Column in the data frame containing information on the sample condition.
`log2_intensity`	Column in the data frame containing log2 transformed intensities.
`cv_limit`	Optional argument specifying the CV cutoff that will be applied. Default is 0.25.
`min_conditions`	The minimum number of conditions for which grouping CVs should be below the cutoff.
`silent`	Logical argument specifiying if a message with the number of filtered out conditions should be returned.

Value

The CV filtered data frame.

Examples

## Not run: 
filter_cv(
  data,
  grouping = eg_precursor_id,
  condition = r_condition,
  log2_intensity = normalised_intensity_log2,
  cv_limit = 0.25,
  min_conditions = 5
)

## End(Not run)

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