| Title: | Implement the Group Walk Algorithm |
| Version: | 0.1.2 |
| Description: | A procedure that uses target-decoy competition (or knockoffs) to reject multiple hypotheses in the presence of group structure. The procedure controls the false discovery rate (FDR) at a user-specified threshold. |
| URL: | https://www.biorxiv.org/content/10.1101/2022.01.30.478144v1, https://github.com/freejstone/groupwalk |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.1.2 |
| Suggests: | testthat (≥ 3.0.0) |
| Config/testthat/edition: | 3 |
| NeedsCompilation: | no |
| Packaged: | 2022-06-17 23:49:57 UTC; jackfreestone |
| Author: | Jack Freestone [aut, cre, cph], Uri Keich [aut, cph] |
| Maintainer: | Jack Freestone <jfre0619@uni.sydney.edu.au> |
| Repository: | CRAN |
| Date/Publication: | 2022-06-18 06:30:02 UTC |
Implements group-walk algorithm
Description
This function returns a list of q-values corresponding to hypotheses that have been partitioned into groups. For FDR control, users should report the target-hypotheses with q-values less than or equal to their choice of threshold, alpha. For further details about how group-walk works, see: https://www.biorxiv.org/content/10.1101/2022.01.30.478144v1
Usage
group_walk(
winning_scores,
labels,
all_group_ids,
K = 40,
return_frontier = FALSE,
correction = 1
)
Arguments
winning_scores |
A numerical vector of winning scores generated from the target-decoy competitions for each hypothesis. |
labels |
A vector of winning labels indicating whether it was a target (= 1) or a decoy (!= 1) for each hypothesis. |
all_group_ids |
A vector of group IDs associated to each hypothesis (can be recorded as integers, factors, characters). |
K |
A window size parameter (integer). |
return_frontier |
A boolean indicating whether the function should return the complete sequence of frontiers. |
correction |
A correction factor used to in the numerator of the estimated false discovery rate (FDR) (Use 1 for FDR control). |
Value
A sequence of q-values for each hypothesis. If return_frontier = T, additionally the sequence of frontiers will be returned.
Examples
create_uncalibrated_hypotheses <- function(m_vec, pi_0_vec, mus, sds) {
total <- sum(m_vec)
g_total <- length(m_vec)
data <- matrix(0, ncol = 4, nrow = total)
for (g in 1:length(m_vec)){
m <- m_vec[g]
pi_0 <- pi_0_vec[g]
mu <- mus[g]
sd <- sds[g]
if (g == 1) {
start <- 0
} else {
start <- sum(m_vec[1:(g - 1)])
}
targets_nonnull <- rnorm(floor(m*pi_0), mean = mu, sd = sd)
targets_null <- rnorm(m - floor(m*pi_0), mean = 0, sd = 1)
decoys <- rnorm(m, mean = 0, sd = 1)
targets <- c(targets_nonnull, targets_null)
W <- pmax(targets, decoys)
data[(start + 1):(start + m), 1] <- W
data[(start + 1):(start + m), 2] <- g
decoy_inds <- which(decoys > targets)
inc_native_inds <- (which(targets_null > decoys[(floor(m*pi_0) + 1):m])) + floor(m*pi_0)
X <- rep(0, m)
X[decoy_inds] <- -1
X[inc_native_inds] <- 1
Y <- X
X[X == 0] <- 1
data[(start + 1):(start + m), 3] <- Y
data[(start + 1):(start + m), 4] <- X
}
return(data)
}
data <- create_uncalibrated_hypotheses(m_vec = rep(1000, 3),
pi_0_vec = rep(0.6, 3), mus = c(2.5, 3, 3.5), sds = rep(1, 3))
winning_scores <- data[, 1]
all_group_ids <- data[, 2]
labels <- data[, 4]
q_vals <- group_walk(winning_scores, labels, all_group_ids)