False Discovery Rate Control

• False Discovery Rate (FDR) is the expected ratio of false rejections to total rejections, providing a foundational metric in multiple testing correction.
• Competition-based methods such as target–decoy and knockoff frameworks use score comparisons to estimate and manage the false discovery proportion effectively.
• Advanced procedures like FDP-SD and prediction bands offer probabilistic guarantees on the realized FDP, ensuring high-confidence control in practical applications.

False Discovery Rate (FDR) control is the cornerstone of modern multiple testing, designed to bound the expected proportion of false positives among rejected hypotheses. While procedures such as the Benjamini–Hochberg method ensure that the expected false discovery proportion (FDP) does not exceed a nominal level α, the realized FDP in any specific dataset may substantially exceed α. In the context of competition-based FDR control—chiefly, target–decoy competition (TDC) in proteomics and "knockoff"-based variable selection in regression—a combinatorial structure enables robust FDR control, but further tools are needed for high-confidence control of the realized FDP. Methods such as FDP-SD ("FDP step-down") provide probabilistic guarantees on the FDP, closing the gap between average-case and probabilistic control.

1. Formal Definitions and Competition-Based Frameworks

Let $m$ hypotheses be tested, with $R$ the number of rejections and $V$ the number of false rejections (true nulls rejected). The False Discovery Proportion is

$$\mathrm{FDP} = \frac{V}{R} \;\;\; (\mathrm{FDP}=0\;\text{if}\;R=0),$$

and the False Discovery Rate is its expectation, $\mathrm{FDR} = \mathbb{E}[\mathrm{FDP}]$. FDR control at level $\alpha$ ensures $\mathrm{FDR} \leq \alpha$.

In Target–Decoy Competition (TDC) frameworks, each hypothesis (e.g., peptide-spectrum match or feature) is assigned a “target” score $Z_i$ (signal) and a “decoy” score $\tilde Z_i$ (null), typically constructed via data shuffling or synthetically generating knockoff variables. For each hypothesis:

• $W_i = \max\{Z_i, \tilde Z_i\}$ is the “winning” score.
• $R$0 if $R$1 (target wins), $R$2 if $R$3 (decoy wins).

The hypotheses are sorted in decreasing $R$4. For any $R$5,

$R$6

TDC estimates the FDP among the top $R$7 hypotheses as $R$8 and reports all target wins up to

$R$9

Under the key assumption that, for true nulls, $V$0 is i.i.d. Rademacher$V$1 (exchangeability), the procedure guarantees FDR control at level $V$2 (Luo et al., 2020).

In the knockoff framework for linear regression, a synthetic "knockoff" feature matrix $V$3 is constructed with exchangeability: null features are indistinguishable from their knockoffs. Each feature/knockoff pair yields statistics $V$4, and the entire procedure mirrors the TDC logic (Luo et al., 2020).

2. Limitation of FDR and the Need for High-Confidence FDP Control

While competition-based FDR procedures robustly control the expected value $V$5, they provide no guarantee that the realized FDP in any given discovery list is near $V$6. Instances where FDP $V$7 can occur with non-negligible probability—a concern for practitioners requiring strong guarantees on false findings within specific results (Luo et al., 2020, Ebadi et al., 2023).

A probabilistic strengthening of FDR control is False Discovery Proportion-exceedance control (FDX):

$V$8

for a user-specified tolerance $V$9. While classic FDR procedures do not address FDX, post-hoc prediction bands or step-down methods can provide explicit bounds on the realized FDP with high probability.

3. The FDP-SD Step-Down Procedure

FDP-SD is an adaptation of generalized step-down control for the competition context. Its core innovation is a set of data-dependent bounds that guarantee, with prescribed confidence $$\mathrm{FDP} = \frac{V}{R} \;\;\; (\mathrm{FDP}=0\;\text{if}\;R=0),$$0, that the FDP does not exceed $$\mathrm{FDP} = \frac{V}{R} \;\;\; (\mathrm{FDP}=0\;\text{if}\;R=0),$$1. The method proceeds as follows (Luo et al., 2020):

Critical values construction: Given the sorted sequence $$\mathrm{FDP} = \frac{V}{R} \;\;\; (\mathrm{FDP}=0\;\text{if}\;R=0),$$2, for each $$\mathrm{FDP} = \frac{V}{R} \;\;\; (\mathrm{FDP}=0\;\text{if}\;R=0),$$3, FDP-SD computes

• $$\mathrm{FDP} = \frac{V}{R} \;\;\; (\mathrm{FDP}=0\;\text{if}\;R=0),$$4
• $$\mathrm{FDP} = \frac{V}{R} \;\;\; (\mathrm{FDP}=0\;\text{if}\;R=0),$$5

where $$\mathrm{FDP} = \frac{V}{R} \;\;\; (\mathrm{FDP}=0\;\text{if}\;R=0),$$6.

Algorithm:

1. For each $$\mathrm{FDP} = \frac{V}{R} \;\;\; (\mathrm{FDP}=0\;\text{if}\;R=0),$$7, compute $$\mathrm{FDP} = \frac{V}{R} \;\;\; (\mathrm{FDP}=0\;\text{if}\;R=0),$$8.
2. For $$\mathrm{FDP} = \frac{V}{R} \;\;\; (\mathrm{FDP}=0\;\text{if}\;R=0),$$9, define $\mathrm{FDR} = \mathbb{E}[\mathrm{FDP}]$0 as the largest $\mathrm{FDR} = \mathbb{E}[\mathrm{FDP}]$1 such that $\mathrm{FDR} = \mathbb{E}[\mathrm{FDP}]$2 for all $\mathrm{FDR} = \mathbb{E}[\mathrm{FDP}]$3.
3. Report all target wins among the top $\mathrm{FDR} = \mathbb{E}[\mathrm{FDP}]$4.

Guarantee: Under exchangeability, the realized FDP among reported discoveries is controlled:

$\mathrm{FDR} = \mathbb{E}[\mathrm{FDP}]$5

(Luo et al., 2020).

The method generalizes to multiple decoys and can accommodate further tuning via parameters $\mathrm{FDR} = \mathbb{E}[\mathrm{FDP}]$6 and $\mathrm{FDR} = \mathbb{E}[\mathrm{FDP}]$7 for aggressive TDC/knockoff variants.

4. Alternative FDP Prediction Bands: TDC-SB and TDC-UB

Instead of a step-down rule, prediction bands can wrap around any competition-based FDR method to provide a running upper confidence bound $\mathrm{FDR} = \mathbb{E}[\mathrm{FDP}]$8 on the realized FDP (Ebadi et al., 2023). The TDC-SB (Standardized Band) and TDC-UB (Uniform Band) procedures consider, for each number of decoy wins $\mathrm{FDR} = \mathbb{E}[\mathrm{FDP}]$9:

• $\alpha$0: number of true-null target-wins prior to the $\alpha$1th decoy win.
• The key stochastic upper bound on $\alpha$2 involves augmenting the observed process so that $\alpha$3 is stochastically dominated by a negative-binomial process $\alpha$4, with $\alpha$5 a function of TDC/knockoff parameters.

Two constructions:

• TDC-SB: Uses normal approximations to produce

$\alpha$6

where $\alpha$7 is the $\alpha$8 quantile of the standardized process, $\alpha$9.

• TDC-UB: Uses the quantile transformation of $\mathrm{FDR} \leq \alpha$0 to define

$\mathrm{FDR} \leq \alpha$1

where $\mathrm{FDR} \leq \alpha$2 is chosen so $\mathrm{FDR} \leq \alpha$3.

These bounds are then mapped back onto the sorted hypotheses; for any $\mathrm{FDR} \leq \alpha$4, an upper bound $\mathrm{FDR} \leq \alpha$5 on FDP is provided such that $\mathrm{FDR} \leq \alpha$6 (Ebadi et al., 2023).

Empirically, both SB and UB bands are much tighter than the Katsevich–Ramdas band, and in practical scenarios (proteomics, GWAS, model-X knockoff regression) the upper FDP bounds are near sharp, especially using UB (Ebadi et al., 2023).

5. Theoretical Underpinnings and Assumptions

The key probabilistic structure underlying competition-based FDR control is:

• Exchangeability: For each true-null, the distribution of $\mathrm{FDR} \leq \alpha$7 is invariant to permutation, so $\mathrm{FDR} \leq \alpha$8 is equally likely $\mathrm{FDR} \leq \alpha$9 or $Z_i$0 and independent across hypotheses.
• Independence: Labels $Z_i$1 for true nulls are independent, and their distribution does not depend on the scores of false-nulls or their own winning scores (Luo et al., 2020, Ebadi et al., 2023).

These assumptions support the exact calibration of binomial or negative-binomial upper bounding distributions, enabling both expectation control (FDR) and tail probability control (FDX/prediction bands).

6. Practical Implications, Computation, and Extensions

FDP-SD and prediction bands (TDC-SB, TDC-UB) are computationally tractable:

• Sorting $Z_i$2 requires $Z_i$3, while computation of $Z_i$4 or $Z_i$5 can be made $Z_i$6 per $Z_i$7 or $Z_i$8 via precomputed tables or incremental updates.
• Flexibility to trade off the nominal level $Z_i$9 and the tail probability $\tilde Z_i$0 is explicit, in contrast to basic FDR procedures.

Applications include:

• Proteomics: direct substitution of TDC final thresholding with FDP-SD for strong FDP control.
• High-dimensional regression: model-X knockoff pipelines may use the same combinatorial logic, with prediction bands for post-hoc validation.
• GWAS and simulation studies consistently demonstrate that FDP-SD dominates competing methods (e.g., Katsevich–Ramdas band) in terms of power and tightness of FDP bounds, with only minor sacrifice relative to ordinary FDR control.

Generalizations to multiple decoys and randomization for exact calibration are feasible. Future extensions suggested include the optimization of aggressiveness parameters and exploring improved sharpenings for multi-decoy frameworks (Luo et al., 2020, Ebadi et al., 2023).

7. Historical and Methodological Context

Competition-based FDR control, formalized in proteomics (target–decoy) and generalized in statistics through model-X knockoffs, represents a distinctive approach leveraging explicit null labeling and exchangeability. Standard FDR control operates in expectation; methods such as FDP-SD and its prediction-band analogues elevate the guarantee to the probability that the realized FDP never exceeds a threshold, thus filling a fundamental gap between average-case and post-hoc, reproducible false discovery control. The combination of combinatorial structure, probabilistic null modeling, and analytical prediction bands sets competition-based control apart as a unifying theme for rigorous, interpretable multiple testing (Luo et al., 2020, Ebadi et al., 2023).