README.md

In bandsfdp: Compute Upper Prediction Bounds on the FDP in Competition-Based Setups

bandsfdp

This package provides functions that compute upper prediction bounds on the FDP in competition-based setups (see Ebadi et al. (2022)). Such setups include target-decoy competition (TDC) in computational mass spectrometry and the knockoff construction in regression. Note we typically use the terminology of TDC throughout.

In (single-decoy) TDC, each hypothesis is associated to a winning score and a label ($1$ for a target win and $-1$ for a decoy win). Functions in this package assume that the hypotheses are ordered in decreasing order of winning scores (with ties broken at random).

The functions tdc_sb() and tdc_ub() give an upper prediction bound on the FDP in TDC’s discovery list. Given TDC’s rejection threshold, the target/decoy labels, and a desired confidence level $1 - \gamma$, these functions return a real number $[\eta$ such that the FDP in the list of discoveries is $\leq \eta$ with probability $\geq 1 - \gamma$.

The function sim_bound() provides simultaneous bounds on the FDP. It computes an upper prediction bound on the FDP of target wins among the top $k$ hypotheses of TDC (the hypotheses of the $k$ largest winning scores), for each $k = 1,\ldots,n$ where $n$ is the total number of hypotheses. Similarly, the function gen_bound() provides a bound on the FDP among target wins in an arbitrary set $R$ of hypotheses of TDC.

Note that upper prediction bounds are derived from upper prediction bands. In particular, the bounds in this package are derived from the standardized band (SB) and uniform band (UB), hence the name “bandsfdp”.

Installation

You can install the development version of bandsfdp from GitHub with:

# install.packages("devtools")
devtools::install_github("uni-Arya/bandsfdp")

Usage

The standardized and uniform bands require pre-computed Monte Carlo statistics. These can be downloaded using devtools::install_github("uni-Arya/fdpbandsdata") (approximately 81Mb). The user can also view the code used to generate these tables at fdpbandsdata.

For tdc_sb() and tdc_ub(), the following inputs are required:

1. A vector of (non-negative integer valued) rejection thresholds. Typically only one is used: the rejection threshold of TDC.
2. A vector of labels ( for a decoy win, for a target win) that are ordered so the corresponding winning scores of TDC are decreasing.
3. A confidence parameter gamma (a number between 0 and 1), for a 1 - gamma confidence level. Note that the functions currently support gamma = 0.01, 0.025, 0.5, 0.1, 0.8, 0.5, but more data can be generated using the source code at fdpbandsdata.
4. The FDR tolerance alpha used in TDC (a number between 0 and 1).

With a Single Decoy Score

Typically, TDC uses a single decoy score in its competition step. Hence, both tdc_sb() and tdc_ub() assume this to be the case by default (the parameters c and lambda are both set to 0.5 by default).

Below is an example of how to use these functions. Note that the thresholds are not representative of the actual rejection threshold of TDC.

suppressPackageStartupMessages(library(bandsfdp))
set.seed(123)

if (requireNamespace("fdpbandsdata", quietly = TRUE)) {
  thresholds <- c(250, 500, 750, 1000)
  labels <- c(
    rep(1, 250),
    sample(c(1, -1), size = 250, replace = TRUE, prob = c(0.9, 0.1)),
    sample(c(1, -1), size = 250, replace = TRUE, prob = c(0.5, 0.5)),
    sample(c(1, -1), size = 250, replace = TRUE, prob = c(0.1, 0.9))
  )
  alpha <- 0.05
  gamma <- 0.05

  print(tdc_sb(thresholds, labels, alpha, gamma))
  print(tdc_ub(thresholds, labels, alpha, gamma))
}
#> [1] 0.02000000 0.09453782 0.26825127 0.29575163
#> [1] 0.02400000 0.08823529 0.26315789 0.29084967

With Multiple Decoy Scores

TDC can be extended to use multiple decoys. In that setup, the target score is competed with multiple decoy scores and the rank of the target score after competition is used to determine whether the hypothesis is a target win (label = $1$), decoy win ($-1$) or uncounted ($0$). The top c proportion of ranks are considered winning, the bottom 1-lambda losing, and all the rest uncounted. The parameters c and lambda must satisfy the following conditions:

1. $c \leq \lambda$
2. $c$ and $\lambda$ are of the form $k/(d+1)$ where $d$ is the number of decoys used and $1 \leq k \leq d$ is an integer.

As an example, if we use $3$ decoy scores for each hypothesis, we may take $c$ and $\lambda$ to be one of $1/4$, $1/2$, or $3/4$, subject to $c \leq \lambda$. For instance, if $c = 1/4$, $H_i$ is labelled as a target win whenever its corresponding target score is the highest ranked score among all decoys for that hypothesis.

Below is an illustrative example of such a use.

suppressPackageStartupMessages(library(bandsfdp))
set.seed(123)

if (requireNamespace("fdpbandsdata", quietly = TRUE)) {
  thresholds <- c(250, 500, 750, 1000)
  labels <- c(
    rep(1, 250),
    sample(c(1, -1), size = 250, replace = TRUE, prob = c(0.9, 0.1)),
    sample(c(1, -1), size = 250, replace = TRUE, prob = c(0.5, 0.5)),
    sample(c(1, -1), size = 250, replace = TRUE, prob = c(0.1, 0.9))
  )
  alpha <- 0.05
  gamma <- 0.05
  c <- 0.25
  lambda <- 0.25

  print(tdc_sb(thresholds, labels, alpha, gamma, c, lambda))
  print(tdc_ub(thresholds, labels, alpha, gamma, c, lambda))
}
#> [1] 0.00800000 0.03991597 0.16298812 0.19444444
#> [1] 0.01200000 0.03781513 0.15449915 0.18627451

Interpolated Bands

All bands are interpolated by default, which requires the computation of a running maximum. This generally results in a slightly tighter bound, but at the cost of computational power. We recommend the use of interpolate = TRUE, unless it is too time-consuming.

If one wishes to use non-interpolated bands, the code below shows an example of such a use.

suppressPackageStartupMessages(library(bandsfdp))
set.seed(123)

if (requireNamespace("fdpbandsdata", quietly = TRUE)) {
  thresholds <- c(250, 500, 750, 1000)
  labels <- c(
    rep(1, 250),
    sample(c(1, -1), size = 250, replace = TRUE, prob = c(0.9, 0.1)),
    sample(c(1, -1), size = 250, replace = TRUE, prob = c(0.5, 0.5)),
    sample(c(1, -1), size = 250, replace = TRUE, prob = c(0.1, 0.9))
  )
  alpha <- 0.05
  gamma <- 0.05
  c <- 0.25
  lambda <- 0.25

  print(tdc_sb(thresholds, labels, alpha, gamma, c, lambda, interpolate = FALSE))
  print(tdc_ub(thresholds, labels, alpha, gamma, c, lambda, interpolate = FALSE))
}
#> [1] 0.00800000 0.03991597 1.00000000 1.00000000
#> [1] 0.01200000 0.03781513 1.00000000 1.00000000

Simultaneous FDP bounds

One may also be interested in computing a bound on the FDP of target wins among the top $k$ hypotheses for all $k = 1, \ldots, n$, where $n$ is the total number of hypotheses. In this case, the function sim_bound() should be used. This function requires the following arguments:

• A vector of (ordered) labels, confidence parameter gamma, and competition parameters c and lambda, as described in the previous sections.
• A character argument type which is either "stband" or "uniband", specifying the type of band to be used to compute the simultaneous FDP bounds.
• The maximum number of decoy wins considered for the bands d_max (defaults to NULL, in which case it is automatically computed using max_fdp below).
• The maximal considered FDP for the simultaneous bounds max_fdp (defaults to max_fdp = 0.5).

The arguments d_max and max_fdp control the rate at which the simultaneous bounds are increasing. More information is written in the details section of the R documentation of sim_bound(). We also refer the reader to Section 3 of Ebadi et al. (2022) for more details.

Below is an example of such a use of the function.

suppressPackageStartupMessages(library(bandsfdp))
set.seed(123)

if (requireNamespace("fdpbandsdata", quietly = TRUE)) {
  set.seed(123)
  labels <- c(
    rep(1, 250),
    sample(c(1, -1), size = 250, replace = TRUE, prob = c(0.9, 0.1)),
    sample(c(1, -1), size = 250, replace = TRUE, prob = c(0.5, 0.5)),
    sample(c(1, -1), size = 250, replace = TRUE, prob = c(0.1, 0.9))
  )
  gamma <- 0.05
  sim_bound(labels, gamma, type = "stband")[700:706]
}
#> [1] 0.2402827 0.2416226 0.2416226 0.2416226 0.2416226 0.2416226 0.2429577

Generalized FDP bounds

One may be interested in computing an upper prediction bound on the FDP among target wins in an arbitrary set $R$ of hypotheses. In this case, the function gen_bound() should be used. Here, one uses the same arguments as in sim_bound(), with an additional argument indices that specifies the set of indices $R$ for which to compute the upper prediction bound over.

Below is an example of such a use of the function.

suppressPackageStartupMessages(library(bandsfdp))
set.seed(123)

if (requireNamespace("fdpbandsdata", quietly = TRUE)) {
  set.seed(123)
  labels <- c(
    rep(1, 250),
    sample(c(1, -1), size = 250, replace = TRUE, prob = c(0.9, 0.1)),
    sample(c(1, -1), size = 250, replace = TRUE, prob = c(0.5, 0.5)),
    sample(c(1, -1), size = 250, replace = TRUE, prob = c(0.1, 0.9))
  )
  indices <- c(1:100, 300:400, 600:650)
  gamma <- 0.05
  gen_bound(labels, indices, gamma, type = "stband")
}
#> [1] 0.2546296

bandsfdp documentation built on May 31, 2023, 7:24 p.m.