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Detecting rare and faint signals via thresholding maximum likelihood estimators. (English) Zbl 1392.62163

Summary: Motivated by the analysis of RNA sequencing (RNA-seq) data for genes differentially expressed across multiple conditions, we consider detecting rare and faint signals in high-dimensional response variables. We address the signal detection problem under a general framework, which includes generalized linear models for count-valued responses as special cases. We propose a test statistic that carries out a multi-level thresholding on maximum likelihood estimators (MLEs) of the signals, based on a new Cramér-type moderate deviation result for multidimensional MLEs. Based on the multi-level thresholding test, a multiple testing procedure is proposed for signal identification. Numerical simulations and a case study on maize RNA-seq data are conducted to demonstrate the effectiveness of the proposed approaches on signal detection and identification.

MSC:

62H15 Hypothesis testing in multivariate analysis
62G20 Asymptotic properties of nonparametric inference
62G32 Statistics of extreme values; tail inference

Software:

DEseq; QuasiSeq
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References:

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