This is an R package for finding mutliple replicating signals under the Partial Conjunction framework. Our method adaFilter contains two procedures: adaFilter Bonferroni and adaFilter BH that can effciently identify signals that replicate in at least
r out of
AdaFilter starts with a p-value matrix of size
M * n where
M is the number of hypotheses in one study and
n is the number of studies. In some high-throughput genetic experiments, it is common that some hypotheses have missing p-values in some studies. AdaFilter allows missing values in the p-value matrix (which are simply NA values).
Testing for replicability, we reject a hypothesis only when the individual hypotheses are nonnull in at least r studies. For instance, for genetic data, we reject a gene only when there is signal in at least r studies. Here, r is a user-specified replicability level, and should be at least 2.
As an example, we simulate p-values for M = 10000 hypotheses in n = 4 studies, and set 5% hypotheses to be true partial conjunction non-nulls with r = 3. In other words, there are 500 hypotheses that are non-null in both studies, and are the true signals that we want to find.
library(adaFilter) set.seed(1) data <- GenPMat(M = 10000, n = 4, r = 3, alternative.frac = 0.05, all.zero.frac = 0.8) head(data$pvalue.mat)
Then, we can run adaFilter to find signals that replicate in both two studies and control FDR at level 0.05. AdaFilter returns a data frame of 5 columns, storing the decision of whether rejecting a hypothesis or not, adaFilter adjusted p-values, the selection and filtering p-values and the adaFilter adjustment numbers (for more details, see reference).
result <- adaFilter(data$pvalue.mat, r = 3) head(result) ## show indices of true positives print(which(result$decision == 1 & data$truth.pc == 1))
AdaFilter finds 333 true positives.
In contrast, we can compare adaFilter with three other standard multiple testing procedures, that apply BH on three types of partial conjunction p-values.
## standard BH adjustment on Bonferroni PC p-values result <- ClassicalMTP(data$pvalue.mat, r = 3, alpha = 0.05, method = "Bonferroni") print(which(result$decision == 1 & data$truth.pc)) ## standard BH adjustment on Fisher PC p-values result <- ClassicalMTP(data$pvalue.mat, r = 3, alpha = 0.05, method = "Fisher") print(which(result$decision == 1 & data$truth.pc)) ## standard BH adjustment on Simes PC p-values result <- ClassicalMTP(data$pvalue.mat, r = 3, alpha = 0.05, method = "Simes") print(which(result$decision == 1 & data$truth.pc))
which only rejects 179, 183 and 180 true positives respectively.
J. Wang, L. Gui, W. J. Su, C. Sabatti and A. B. Owen (2020). Detecting Multiple Replicating Signals using Adaptive Filtering Procedures Arxiv