Signal classification for the integrative analysis of multiple sequences of large‐scale multiple tests

Dongdong Xiang, Sihai Dave Zhao and T. Tony Cai

Journal of the Royal Statistical Society Series B, 2019, vol. 81, issue 4, 707-734

Abstract: The integrative analysis of multiple data sets is becoming increasingly important in many fields of research. When the same features are studied in several independent experiments, it can often be useful to analyse jointly the multiple sequences of multiple tests that result. It is frequently necessary to classify each feature into one of several categories, depending on the null and non‐null configuration of its corresponding test statistics. The paper studies this signal classification problem, motivated by a range of applications in large‐scale genomics. Two new types of misclassification rate are introduced, and two oracle procedures are developed to control each type while also achieving the largest expected number of correct classifications. Corresponding data‐driven procedures are also proposed, proved to be asymptotically valid and optimal under certain conditions and shown in numerical experiments to be nearly as powerful as the oracle procedures. In an application to psychiatric genetics, the procedures proposed are used to discover genetic variants that may affect both bipolar disorder and schizophrenia, as well as variants that may help to distinguish between these conditions.

Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://doi.org/10.1111/rssb.12323

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:jorssb:v:81:y:2019:i:4:p:707-734

Ordering information: This journal article can be ordered from
http://ordering.onli ... 1111/(ISSN)1467-9868

Access Statistics for this article

Journal of the Royal Statistical Society Series B is currently edited by P. Fryzlewicz and I. Van Keilegom

More articles in Journal of the Royal Statistical Society Series B from Royal Statistical Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().