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.
To assess fdr over the entire dataset, please refer to function assess_fdr_overall.
FDR is calculated as FDR = (TN*FFT/T); TN=decoys, T=targets, FFT=see above
assess_fdr_byrun(data, FFT, n.range = 20, output = "pdf_csv", plot = TRUE,
filename = "FDR_report_byrun")
data("OpenSWATH_data", package="SWATH2stats")
data("Study_design", package="SWATH2stats")
data <- sample_annotation(OpenSWATH_data, Study_design)
assess_fdr_byrun(data, FFT=0.7, output = "pdf_csv", plot = TRUE,
filename="Testoutput_assess_fdr_byrun")
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