Refined False Discovery Rate (FDR) Calculation

This function computes refined False Discovery Rate (FDR) estimates using a competitive approach between target and decoy identifications. It provides three types of FDR calculations: FDRn, FDRp, and FDRr, which adjust for different competitive scenarios between targets and decoys.

Usage

refined_fdr(data, levelRef, score, lower_better = TRUE, affix = "_REVERSED")

Arguments

data

A data frame containing the identification data, including columns for the reference level, a score, and whether each identification is a decoy (isDecoy).

levelRef

The column name containing the reference level for each identification (e.g., protein or gene reference). This should be an unquoted column name.

score

The column name of the score used to rank the identifications. This should be an unquoted column name.

lower_better

A logical value indicating whether lower scores are better (default is TRUE).

affix

A string indicating the suffix of prefix used to identify decoy entries in the reference level column. Default is "_REVERSED".

Value

A data frame with the original data and additional columns for the refined FDR estimates:

FDRn

Normal FDR estimation as cumulative minimum (q-value).

FDRp

Picked FDR estimation as cumulative minimum (q-value).

FDRr

Refined FDR estimationas cumulative minimum (q-value).

to

Target-only identifications count.

do

Decoy-only identifications count.

td

Count of identifications with the same target and decoy scores.

tb

Target-best identifications count.

db

Decoy-best identifications count.

See also

• Protein Probability Model for High-Throughput Protein Identification by Mass Spectrometry-Based Proteomics for more information on the refined FDR estimation.

Examples

# Example usage with a sample dataset
sample_data <- data.frame(
  proteinRef = c("P1", "P1_REVERSED", "P2", "P3", "P3_REVERSED"),
  score = c(0.1, 0.2, 0.3, 0.5, 0.4),
  isDecoy = c(FALSE, TRUE, FALSE, FALSE, TRUE)
)
refined_fdr(sample_data, levelRef = proteinRef, score = score, lower_better = TRUE)
#> # A tibble: 5 × 11
#>   proteinRef  score isDecoy    to    do    td    tb    db  FDRn  FDRp  FDRr
#>   <chr>       <dbl> <lgl>   <int> <int> <int> <int> <int> <dbl> <dbl> <dbl>
#> 1 P1            0.1 FALSE       1     0     0     0     0 0       0   0    
#> 2 P1_REVERSED   0.2 TRUE        0     0     0     1     0 0.5     0   0    
#> 3 P2            0.3 FALSE       1     0     0     1     0 0.5     0   0    
#> 4 P3_REVERSED   0.4 TRUE        1     1     0     1     0 0.667   0.5 0.5  
#> 5 P3            0.5 FALSE       1     0     0     1     1 0.667   0.5 0.667