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Type: Package
Title: Replicability Analysis of High-Throughput Experiments
Version: 1.0.2
Description: Implementing a computationally scalable false discovery rate control procedure for replicability analysis based on maximum of p-values. Please cite the manuscript corresponding to this package [Lyu, P. et al., (2023), <doi:10.1093/bioinformatics/btad366>].
License: GPL-3
Encoding: UTF-8
Depends: R (≥ 4.1.2), Rcpp, splines, stats
LinkingTo: Rcpp, RcppArmadillo
RoxygenNote: 7.2.3
NeedsCompilation: yes
Packaged: 2025-05-30 12:28:20 UTC; P53
Author: Pengfei Lyu [aut, ctb], Yan Li [aut, cre, cph], Xiaoquan Wen [aut], Hongyuan Cao [aut, ctb]
Maintainer: Yan Li <yanli_@jlu.edu.cn>
Repository: CRAN
Date/Publication: 2025-05-30 12:40:02 UTC

Replicability Analysis of High-Throughput Experiments

Description

Replicability Analysis of High-Throughput Experiments

Usage

JUMP(pvals1, pvals2, alpha = 0.05, lambda = seq(0.01, 0.8, 0.01))

Arguments

pvals1

A numeric vector of p-values from study 1.

pvals2

A numeric vector of p-values from study 2.

alpha

The FDR level to control, default is 0.05.

lambda

The values of the tuning parameter to estimate pi_0. Must be in [0,1), default is seq(0.01, 0.8, 0.01).

Value

a list with the following elements:

p.max

The maximum of p-values across two studies.

jump.thr

The estimated threshold of p.max to control FDR at level alpha.

Examples

# Simulate p-values in two studies
m = 10000
h = sample(0:3, m, replace = TRUE, prob = c(0.9, 0.025, 0.025, 0.05))
states1 = rep(0, m); states2 = rep(0, m)
states1[which(h==2|h==3)] = 1; states2[which(h==1|h==3)] = 1
z1 = rnorm(m, states1*2, 1)
z2 = rnorm(m, states2*3, 1)
p1 = 1 - pnorm(z1); p2 = 1 - pnorm(z2)
# Run JUMP to identify replicable signals
res.jump = JUMP(p1, p2, alpha = 0.05)
sig.idx = which(res.jump$p.max <= res.jump$jump.thr)


Estimate threshold of maximum p-values across two studies to control FDR.

Description

Estimate threshold of maximum p-values across two studies to control FDR.

Usage

jump_cutoff(pa_in, pb_in, xi_in, alpha_in)

Arguments

pa_in

A numeric vector of p-values from study 1.

pb_in

A numeric vector of p-values from study 2.

xi_in

The estimates of proportions of three null components.

alpha_in

The FDR level to control, default is 0.05.

Value

A list including the maximum of p-values and estimated threshold for FDR control.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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