Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Extracting replicable associations across multiple studies: algorithms
  for controlling the false discovery rate   Extracting associations that recur across multiple studies while controlling
the false discovery rate is a fundamental challenge. Here, we consider an
extension of Efron's single-study two-groups model to allow joint analysis of
multiple studies. We assume that given a set of p-values obtained from each
study, the researcher is interested in associations that recur in at least
$k>1$ studies. We propose new algorithms that differ in how the study
dependencies are modeled. We compared our new methods and others using various
simulated scenarios. The top performing algorithm, SCREEN (Scalable
Cluster-based REplicability ENhancement), is our new algorithm that is based on
three stages: (1) clustering an estimated correlation network of the studies,
(2) learning replicability (e.g., of genes) within clusters, and (3) merging
the results across the clusters using dynamic programming. We applied SCREEN to
two real datasets and demonstrated that it greatly outperforms the results
obtained via standard meta-analysis. First, on a collection of 29 case-control
large-scale gene expression cancer studies, we detected a large up-regulated
module of genes related to proliferation and cell cycle regulation. These genes
are both consistently up-regulated across many cancer studies, and are well
connected in known gene networks. Second, on a recent pan-cancer study that
examined the expression profiles of patients with or without mutations in the
HLA complex, we detected an active module of up-regulated genes that are
related to immune responses. Thanks to our ability to quantify the false
discovery rate, we detected thrice more genes as compared to the original
study. Our module contains most of the genes reported in the original study,
and many new ones. Interestingly, the newly discovered genes are needed to
establish the connectivity of the module.

15
The categories of this paper are: nucl-ex
7
The categories of this paper are: hep-th
27
The categories of this paper are: comp-gas
21
The categories of this paper are: adap-org
8
The categories of this paper are: hep-ex
18
The categories of this paper are: econ
9
The categories of this paper are: nlin
23
The categories of this paper are: solv-int
36
The categories of this paper are: cmp-lg
22
The categories of this paper are: chao-dyn
1
The categories of this paper are: cs
31
The categories of this paper are: ao-sci
13
The categories of this paper are: quant-ph
12
The categories of this paper are: nucl-th
5
The categories of this paper are: gr-qc
26
The categories of this paper are: bayes-an