SWATH2stats (version 1.2.3)

mscore4protfdr: Find m_score cutoff to reach a desired FDR on protein level (over the entire OpenSWATH/pyProphet output table)

Description

This function estimates the m_score cutoff required in a dataset to reach a given overall protein level FDR. This filter is to be used with caution as the resulting quantitative matrix is relatively sparse. It can be filled with quantitative values at a lower FDR quality level. It counts target and decoy peptides (unique ProteinName) at high resolution across the m_score cutoffs and reports a useful m_score cutoff - peptide FDR pair close to the supplied fdr_target level over the entire dataset. The m_score cutoff is returned by the function and can be used in the context of the filtering functions, e.g.:

data.protFDR5pc<-filter_mscore(data, mscore4protfdr(data, fdr_target=0.02))

To arrive from decoy counts at an estimation of the false discovery rate (false positives among the targets remaining at a given mscore cutoff) the ratio of false positives to true negatives (decoys) (FFT) must be supplied. It is estimated for each run individually by pyProphet and contained in the pyProphet statistics [Injection_name]_full_stat.csv. As an approximation, the FFTs of multiple runs are averaged and supplied as argument FFT. For further details see the Vignette Section 1.3 and 4.1.

For FDR evaluations on assay and peptide level, please refer to functions mscore4assayfdr mscore4pepfdr

Usage

mscore4protfdr(data, FFT, fdr_target)

Arguments

data

Annotated OpenSWATH/pyProphet data table. See function sample_annotation from this package.

FFT

Ratio of false positives to true negatives, q-values from [Injection_name]_full_stat.csv in pyProphet stats output. As an approximation, the q-values of multiple runs are averaged and supplied as argument FFT. Numeric from 0 to 1. Defaults to 1, the most conservative value (1 Decoy indicates 1 False target).

fdr_target

FDR target, numeric, defaults to 0.01. An m_score cutoff achieving an FDR < fdr_target will be selected. Calculated as FDR = (TN*FFT/T); TN=decoys, T=targets, FFT=see above.

Value

Returns the m_score cutoff selected to arrive at the desired FDR quality

Examples

data("OpenSWATH_data", package="SWATH2stats")
data("Study_design", package="SWATH2stats")
data <- sample_annotation(OpenSWATH_data, Study_design)
mscore4protfdr(data, FFT=0.7, fdr_target=0.01)