A weighted FDR procedure under discrete and heterogeneous null distributions

Chen, Xiongzhi; Doerge, R. W.; Sarkar, Santanu

doi:10.1002/bimj.201900216

Cited by 5 publications

(5 citation statements)

References 28 publications

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“…The important variables in the partial least squares discriminant analysis (PLS-DA) projection were determined using the ropls R-package. P<0.05 with corresponding q-value (FDR)<0.05 were considered statistically significant [29]. A receiver operating characteristic (ROC) curve was constructed using pROC to plot significant metabolites [30].…”

Section: Discussionmentioning

confidence: 99%

Rodents on a high-fat diet born to mothers with gestational diabetes exhibit sex-specific lipidomic changes in reproductive organs

Wang¹,

Luo²,

Chen³

et al. 2022

ABBS

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Maternal gestatonal diabetes mellitus (GDM) and offspring high-fat diet (HFD) have been shown to have sex-specific detrimental effects on the health of the offspring. Maternal GDM combined with an offspring HFD alters the lipidomic profiles of offspring reproductive organs with sex hormones and increases insulin signaling, resulting in offspring obesity and diabetes. The pre-pregnancy maternal GDM mice model is established by feeding maternal C57BL/6 mice and their offspring are fed with either a HFD or a low-fat diet (LFD). Testis, ovary and liver are collected from offspring at 20 weeks of age. The lipidomic profiles of the testis and ovary are characterized using gas chromatography-mass spectrometry. Male offspring following a HFD have elevated body weight. In reproductive organs and hormones, male offspring from GDM mothers have decreased testes weights and testosterone levels, while female offspring from GDM mothers show increased ovary weights and estrogen levels. Maternal GDM aggravates the effects of an offspring HFD in male offspring on the AKT pathway, while increasing the risk of developing inflammation when expose to a HFD in female offspring liver. Testes are prone to the effect of maternal GDM, whereas ovarian metabolite profiles are upregulated in maternal GDM and downregulated in offspring following an HFD. Maternal GDM and an offspring HFD have different metabolic effects on offspring reproductive organs, and PUFAs may protect against detrimental outcomes in the offspring, such as obesity and diabetes.

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Section: Discussionmentioning

confidence: 99%

Rodents on a high-fat diet born to mothers with gestational diabetes exhibit sex-specific lipidomic changes in reproductive organs

Wang¹,

Luo²,

Chen³

et al. 2022

ABBS

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show abstract

“…We compare our method with Habiger's method and three other methods designed for multiple discrete testing (Gilbert, ; Heller and Gur, ; Chen and Doerge, ), in their true FDR and statistical power at different nominal FDR level

α

, where the statistical power is defined as the ratio between the number of correct rejections and the number of true non‐null hypotheses.…”

Section: Simulation Studymentioning

confidence: 99%

“…We then compared our method with the widely‐used q ‐value method under two nominal FDR levels

α = 0.05

and

0.1

, respectively. Because Habiger's method produces random decisions, and the method of Chen and Doerge () is computationally too intense to handle multiple testing at this large scale, we did not include them.…”

Section: Applicationsmentioning

confidence: 99%

“…This study made use of data generated by the NIH Roadmap Project (). In the simulations, we used the code from R package fdrDiscreteNull to implement the method of Chen and Doerge (). In addition, the authors wish to acknowledge the Co‐Editor and two anonymous reviewers, for their helpful comments and valuable suggestions.…”

Section: Acknowledgmentsmentioning

confidence: 99%

“…The second challenge lies in that discrete tests usually have different null distributions of P ‐values, for that many discrete tests are conditional tests, unlike P ‐values always follow Unif(0,1) in continuous tests. To handle this heterogeneity, Chen and Doerge () proposed grouping discrete tests into groups with similar null distributions of P ‐values, then controlling the FDR by a weighted FDR‐control procedure proposed by Hu et al (). We note that there are special cases where tests are highly homogeneous, that is, the P ‐values have discrete but identical distributions, such as those induced by permutation tests.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Non-Randomized Procedure for Large-Scale Heterogeneous Multiple Discrete Testing Based on Randomized Tests

Dai

Lin

et al. 2018

Biometrics

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In the analysis of next‐generation sequencing technology, massive discrete data are generated from short read counts with varying biological coverage. Conducting conditional hypothesis testing such as Fisher's Exact Test at every genomic region of interest thus leads to a heterogeneous multiple discrete testing problem. However, most existing multiple testing procedures for controlling the false discovery rate (FDR) assume that test statistics are continuous and become conservative for discrete tests. To overcome the conservativeness, in this article, we propose a novel multiple testing procedure for better FDR control on heterogeneous discrete tests. Our procedure makes decisions based on the marginal critical function (MCF) of randomized tests, which enables achieving a powerful and non‐randomized multiple testing procedure. We provide upper bounds of the positive FDR (pFDR) and the positive false non‐discovery rate (pFNR) corresponding to our procedure. We also prove that the set of detections made by our method contains every detection made by a naive application of the widely‐used q‐value method. We further demonstrate the improvement of our method over other existing multiple testing procedures by simulations and a real example of differentially methylated region (DMR) detection using whole‐genome bisulfite sequencing (WGBS) data.

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A rigorous and versatile statistical test for correlations between time series

Yuan

Shou

2022

Preprint

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Computing a correlation between a pair of time series is a routine task in disciplines from biology to climate science. How do we test whether such a correlation is statistically significant (i.e. unlikely under the null hypothesis that the time series are independent)? This problem is made especially challenging by two factors. First, time series typically exhibit autocorrelation, which renders standard statistical tests invalid. Second, researchers are increasingly turning to nonlinear correlation statistics with no known analytical null distribution, thus rendering parametric tests inviable. Several statistical tests attempt to address these two challenges, but none is perfect: A few are valid only under restrictive data conditions, others have differing degrees of correctness depending on user-supplied parameters, and some are simply inexact. Here we describe the truncated time-shift procedure, which can be used with any correlation statistic to test for dependence between time series. We show that this test is exactly valid as long as one time series is stationary, a minimally restrictive requirement among nonparametric tests. Using synthetic data, we demonstrate that our test performs correctly even while other tests suffer high false positive rates. We apply the test to data sets from climatology and microbiome science, verifying previously discovered dependence relationships.

show abstract

A weighted FDR procedure under discrete and heterogeneous null distributions

Abstract: This article has earned an open data badge "Reproducible Research" for making publicly available the code necessary to reproduce the reported results. The results reported in this article were reproduced partially due to their computational complexity.

Cited by 5 publications

References 28 publications

Rodents on a high-fat diet born to mothers with gestational diabetes exhibit sex-specific lipidomic changes in reproductive organs

Rodents on a high-fat diet born to mothers with gestational diabetes exhibit sex-specific lipidomic changes in reproductive organs

A Non-Randomized Procedure for Large-Scale Heterogeneous Multiple Discrete Testing Based on Randomized Tests

A rigorous and versatile statistical test for correlations between time series

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