Cited by 225Statistical Methods in Clinical TrialsGene expression and cancer classificationStatistical Methods and Inference

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Effects of dependence in high-dimensional multiple testing problems

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Kim, Kyung In, van de Wiel, Mark A·2008·BMC Bioinformatics

DOI 10.1186/1471-2105-9-114PMC PMC2375137

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Abstract

BACKGROUND: We consider effects of dependence among variables of high-dimensional data in multiple hypothesis testing problems, in particular the False Discovery Rate (FDR) control procedures. Recent simulation studies consider only simple correlation structures among variables, which is hardly inspired by real data features. Our aim is to systematically study effects of several network features like sparsity and correlation strength by imposing dependence structures among variables using random correlation matrices. RESULTS: We study the robustness against dependence of several FDR procedures that are popular in microarray studies, such as Benjamin-Hochberg FDR, Storey's q-value, SAM and resampling based FDR procedures. False Non-discovery Rates and estimates of the number of null hypotheses are computed from those methods and compared. Our simulation study shows that methods such as SAM and the q-value do not adequately control the FDR to the level claimed under dependence conditions. On the other hand, the adaptive Benjamini-Hochberg procedure seems to be most robust while remaining conservative. Finally, the estimates of the number of true null hypotheses under various dependence conditions are variable. CONCLUSION: We discuss a new method for efficient guided simulation of dependent data, which satisfy imposed network constraints as conditional independence structures. Our simulation set-up allows for a structural study of the effect of dependencies on multiple testing criterions and is useful for testing a potentially new method on pi0 or FDR estimation in a dependency context.

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Figure 1 Average FDR results under dependence when π0 = 0.8. The x-axis corresponds to the proportion of edges in the network and the y-axis corresponds to FDR estimates for various procedures. Testing cut-off c is tuned such that true FDR is 0.1 under independence. FDR(c) (solid black) represents true FDR values in terms of (5) using the fixed c. The FDR procedures and corresponding lines in this figure are the following ones: BH (dashed red), BY (dotted green), SAM (dot-dashed blue), Qvalue (dashed cyan), ABH (purple), the upper limit RBH (dashed black), the point RBH (dotted red).CC BY · PubMed Central

Figure 2 Average FDR results under dependence when π0 = 0.95. See Figure 1 for explanation.CC BY · PubMed Central

Figure 3 Average FNR results under dependence when π0 = 0.8. The y-axis corresponds to FNR estimates for various procedures. For the other explanation, see Figure 1.CC BY · PubMed Central

Figure 4 Average π0 estimates under dependence when π0 = 0.8. The x-axis corresponds to the proportion of edges in the network and the y-axis corresponds to π0 estimates for various procedures. The π0 estimators and corresponding lines are SAM (solid black), Qvalue (dashed red), ABH (dotted green) and the convex estimator of Langaas et al [10] (dot-dashed).CC BY · PubMed Central

Figure 5 Variances of correlations and FDR(c) when π0 = 0.8. The solid line represents variance of correlations and the dashed line represents FDR(c). For comparison, we transform var(ρij) to var(ρij)/10 + 0.1 so that two quantities have same scale.CC BY · PubMed Central

Figure 6 FDR(c) with different M values. For various M - m values, FDR(c) is computed. The M - m values and corresponding lines are 1001 (solid black), 1010 (dashed red), 1025 (dotted green), 1046 (dot-dashed blue) and 1073 (dashed cyan).CC BY · PubMed Central

Figure 7 Graphical representation of conditional independence structures when m = 4. A sequence of possible nested structure is depicted when the number of nodes is 4. The left most graph represents complete independence between variables and the right most graph represents complete dependence between variables. The dependence structure of every left graph is contained to the structure of the graph right to it.CC BY · PubMed Central

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Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
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“As a data-guided simulation using known gene sets [21,25], we introduce an algorithm for using real data in the Results section.”
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“The reason behind these phenomena is hard to explain because the implementation of FDR-AME is modified from the algorithms of Yekutieli et al. [12].”
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“Storey [4] introduces the positive false discovery rate (pFDR) and the q -value. pFDR is known to control error rate under the clumpy dependency condition [5].”
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“We found that in all our simulations, the simple adaptive Benjamini-Hochberg procedure [8] is most optimal under dependence, since it achieves relatively high power while remaining conservative.”
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Hagn-Meincke, R., Hart, P. A., Andersen, D. K., et al. (2023) · European Journal of Gastroenterology & Hepatology · doi:10.1097/meg.0000000000002691 · Cited by 5
“P-values were adjusted for multiple comparisons to control the false discovery rate at 5% [19,20].”
Method
Enhanced bovine genome annotation through integration of transcriptomics and epi-transcriptomics datasets facilitates genomic biology
Beiki, H., Murdoch, B. M., Park, C. A., Kern, C., Konetchy, D. E., Becker, G. M., Rincón, G., Jiang, H., Zhou, H., Thorne, J. W., et al. (2024) · GigaScience · doi:10.1093/gigascience/giae019 · Cited by 7
“The resulting P values were corrected for multiple testing by the Benjamini–Hochberg procedure [83].”
Influential
Bioinformatics analysis of gene expression data for the identification of critical genes in breast invasive carcinoma
Li, Y., & Wang, Y. (2017) · Molecular Medicine Reports · doi:10.3892/mmr.2017.7717 · Cited by 5
“Parathyroid hormone-related protein (12-48) is a plasma biomarker that is associated with breast cancer bone metastasis (13).”
Investment styles and the multiple testing of cross-sectional stock return predictability
Vincent, K., Hsu, Y., & Lin, H. W. (2020) · Journal of Financial Markets · doi:10.1016/j.finmar.2020.100598 · Cited by 5
RNAi silencing of the SoxE gene suppresses cell proliferation in silkworm BmN4 cells
Wei, L., Li, Z., Cheng, D., et al. (2014) · Molecular Biology Reports · doi:10.1007/s11033-014-3348-6 · Cited by 5
Enhancing the Vietoris–Rips simplicial complex for topological data analysis: applications in cancer gene expression datasets
Mashatola, L., Kader, Z., Abdulla, N., & Kaur, M. (2024) · International Journal of Data Science and Analytics · doi:10.1007/s41060-024-00534-9 · Cited by 4
“It helps control the proportion of false positives among signiﬁcant ﬁndings [49].”
Relationship of Heterologous Virus Responses and Outcomes in Hospitalized COVID-19 Patients
Rosenberg‐Hasson, Y., Holmes, T. H., Diray‐Arce, J., et al. (2023) · The Journal of Immunology · doi:10.4049/jimmunol.2300391 · Cited by 4
A false discovery approach for scanning spatial disease clusters with arbitrary shapes
Li, Y., Shu, L., & Tsung, F. (2016) · IIE Transactions · doi:10.1080/0740817x.2015.1133940 · Cited by 4
“…to the advancement and theoretical development; for example, Benjamini and Liu (1999), Benjamini and Hochberg (2000), Genovese and Wasserman (2002), Owen (2005), Benjamini et al. (2006), Sarkar (2006), Efron (2007), Far-comeni (2007), Kim and van de Wiel (2008), Wu (2008), and Sun and Cai (2009).”
Background
Human immunophenotyping via low-variance, low-bias, interpretive regression modeling of small, wide data sets: Application to aging and immune response to influenza vaccination
Holmes, T. H., & He, X.-S. (2016) · Journal of Immunological Methods · doi:10.1016/j.jim.2016.05.004 · Cited by 4
Hypothesis testing for shapes using vectorized persistence diagrams
Moon, C., & Lazar, N. A. (2023) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1093/jrsssc/qlad024 · Cited by 6
Cytokine profiles in patients with toxoplasmic lymphadenitis in the setting of pregnancy
Pomares-Estran, C., Holmes, T. H., Estran, R., et al. (2016) · Cytokine · doi:10.1016/j.cyto.2016.09.021 · Cited by 4
“Within each regression analysis, the false discovery rate was controlled at 5% [20,21], unadjusted p-values are also provided.”
Method
Control of the FWER in Multiple Testing Under Dependence
Causeur, D., Kloareg, M., & Friguet, C. (2009) · Communication in Statistics- Theory and Methods · doi:10.1080/03610910902936281 · Cited by 4
Effects of recombinant Agrocybe aegerita lectin as an immunoadjuvant on immune responses
Ma, L., Xu, B., Huang, M., et al. (2017) · Immunopharmacology and Immunotoxicology · doi:10.1080/08923973.2017.1392561 · Cited by 4
Functional genomics of cattle through integration of multi-omics dataPreprint
Beiki, H., Murdoch, B. M., Park, C. A., Kern, C., Konetchy, D. E., Becker, G. M., Rincón, G., Jiang, H., Zhou, H., Thorne, J. W., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.05.510963 · Cited by 4
Decoding coffee’s chlorogenic acid: genetic–network pharmacology insights on kidney stones
Hou, X., Meng, H., Ying, D., et al. (2025) · Urolithiasis · doi:10.1007/s00240-025-01776-3 · Cited by 3
“For cases where FDR > 0.05 but P-value 0.05, we regarded the findings as noteworthy [ 20].”
Influential
Characterizations of microRNAs involved in the molecular mechanisms underlying the therapeutic effects of noni (Morinda citrifolia L.) fruit juice on hyperuricemia in mice
Liu, Y., Liu, X., Wang, M., Chen, C., Li, X.-H., Liang, Z., Shan, Y., Yin, Y., Sun, F., Li, Z., et al. (2023) · Frontiers in Nutrition · doi:10.3389/fnut.2023.1121734 · Cited by 3
“Based on the detection method of differentially expressed genes (DEGs) as previously reported (36), a strict in-house algorithm was developed to screen the DEGs of miRNAs between two samples with the multiple hypothesis test corrections set up (37) to determine the domain value of p by controlling the false discovery…”
MethodInfluential
Comparative transcriptome analysis of different tissues of Solanum khasianum reveals candidate genes involved in steroidal glycoalkaloid biosynthesis
Zhang, S., Shan, T., Xu, J., et al. (2023) · Physiologia Plantarum · doi:10.1111/ppl.14010 · Cited by 3
Decreased levels and function of dendritic cells in blood and airways predict COVID ‐19 severity
Österberg, B., Falck‐Jones, S., Vangeti, S., Åhlberg, E., Yu, M., Granja, D., Snik, M. E., Falck‐Jones, R., Barros, G. W. F., Charles, A., et al. (2025) · Clinical & Translational Immunology · doi:10.1002/cti2.70026 · Cited by 4
GDNF triggers proliferation of rat C6 glioma cells via the NF-κB/CXCL1 signaling pathway
Wang, Y., Wu, Y., Li, L., et al. (2023) · PLoS ONE · doi:10.1371/journal.pone.0289071 · Cited by 3
Acute Respiratory Illness Is Associated with Memory T Cell Differentiation and Other Immune Cell Changes in an Age-Associated Manner
Ugale, S. S., Holmes, T. H., Maysel-Auslender, S., Boyd, S. D., Dekker, C. L., Davis, M. M., & Maecker, H. T. (2023) · ImmunoHorizons · doi:10.4049/immunohorizons.2300050 · Cited by 3
Comments on: Control of the false discovery rate under dependence using the bootstrap and subsampling
Ferreira, J. A., & van de Wiel, M. A. (2008) · Test · doi:10.1007/s11749-008-0127-5 · Cited by 3
Probing the transcriptome of Boehmeria nivea reveals candidate genes associated with the biosynthesis of chlorogenic acid
Zhao, L., Shan, C., Shan, T., et al. (2022) · Gene · doi:10.1016/j.gene.2022.146579 · Cited by 3
Physiological and Transcriptomic Analysis of Two Types of Hami Melons in Low-Temperature Storage
Liao, W., Xiao, L., Hao, X., et al. (2025) · Plants · doi:10.3390/plants14081153 · Cited by 2
“Dissociation curve analysis was performed as follows: 15 s at 95 ◦ C, 1 min at 60 ◦ C, 15 s at 95 ◦ C, and 30 s at 50 ◦ C. Amplification was carried out in a 96-well plate, and the SybrGreen signal was detected at the end of each extension step at 60 ◦ C in each cycle.”
Crotonylation and the Risk of Head and Neck Cancer: Insights from a Two-Sample Mendelian Randomization Study
Zhang, D., Zhang, X., Li, H., Liang, A., Liang, Y., Wang, Z., Shi, S., Ge, Y., Gao, W., & Liu, X. (2025) · International Dental Journal · doi:10.1016/j.identj.2025.109341 · Cited by 2
“31 Power analyses indicated that most correlations between GCDH and HNC exceeded 90%, supporting that the null ﬁndings are biologically driven rather than due to insufﬁcient power.”
Shifted BH methods for controlling false discovery rate in multiple testing of the means of correlated normals against two-sided alternatives
Sarkar, S. K., & Zhang, S. (2024) · Journal of Statistical Planning and Inference · doi:10.1016/j.jspi.2024.106238 · Cited by 2
Circulating carotenoid levels are negatively associated with previous reproductive success in Florida Scrub-Jays (Aphelocoma coerulescens)
Cohen, A. A., Bowman, R., Boughton, R. K., et al. (2012) · Canadian Journal of Zoology · doi:10.1139/cjz-2012-0243 · Cited by 2
Joint Analysis of Dependent Features within Compound Spectra Can Improve Detection of Differential Features
Trutschel, D., Schmidt, S., Große, I., et al. (2015) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2015.00129 · Cited by 2
A general approach to account for dependence in large-scale multiple testing
Friguet, C. (2012) · HAL · Cited by 2
A Simple Distribution-Free Algorithm for Generating Simulated High-Dimensional Correlated Data with an Autoregressive Structure
Azuero, A., Redden, D. T., Tiwari, H. K., et al. (2011) · Communications in Statistics - Simulation and Computation · doi:10.1080/03610918.2011.579368 · Cited by 2
COMPUTATIONAL IDENTIFICATION OF MICRORNA IN FIVE WOODY OIL TREE CROPS AND THEIR miRNA TARGET SEQUENCES
ChunTai, W. (2018) · Journal of Oil Palm Research · doi:10.21894/jopr.2018.0001 · Cited by 2
“A false discovery rate analysis was performed according to the procedure developed by Kim and van de Wiel (2008).”
Method
On k-FWE-based critical values for controlling the false discovery proportion under dependencePreprint
Delattre, S., & Roquain, É. (2013) · arXiv (Cornell University) · Cited by 2
Limited evidence for validity and reliability of non-navigated low and high frequency rTMS over the motor cortexPreprint
Prei, K., Kanig, C., Osnabruegge, M., Langguth, B., Mack, W., Abdelnaim, M. A., Schecklmann, M., & Schoisswohl, S. (2023) · medRxiv · doi:10.1101/2023.01.24.23284951 · Cited by 2
Vascular transcriptional and metabolic changes precede progressive intrarenal microvascular rarefaction in autosomal dominant polycystic kidney disease
Yilmaz, G., Singha, S. K., Savaliya, B., et al. (2026) · American Journal of Physiology-Renal Physiology · doi:10.1152/ajprenal.00266.2025 · Cited by 1
Introduction to Statistical Methods for Complex Systems
Mary‐Huard, T., & Robin, S. S. (2011) · doi:10.1002/9781119970606.ch2 · Cited by 1
CORRELATION AND VARIANCE STABILIZATION IN THE TWO GROUP COMPARISON CASE IN HIGH DIMENSIONAL DATA UNDER DEPENDENCIES
Paranagama, D. C. (2011) · K-State Research Exchange (Kansas State University) · Cited by 1
Phenome-wide HLA association landscape of 235,000 Finnish biobank participantsPreprint
Ritari, J., Koskela, S., Hyvärinen, K., et al. (2020) · medRxiv · doi:10.1101/2020.10.26.20219899 · Cited by 1

Authors: Kim, K. I., & van de Wiel, M. A.
Affiliations: Eindhoven University of Technology (NL)
Corresponding author: Kim, Kyung In
Venue: BMC Bioinformatics
Publisher: Springer Science and Business Media LLC
Published: 2008-02-25
Volume: 9
Issue: 1
Pages: 114-114
ISSN: 1471-2105 (ISSN-L)
DOI: 10.1186/1471-2105-9-114
PMC: PMC2375137
OpenAlex: W2116319049
Cited by: 220
References: 34
Citations by year: 2026: 3 · 2025: 5 · 2024: 6 · 2023: 15 · 2022: 11 · 2021: 13 · 2020: 29 · 2019: 33 · 2018: 15 · 2017: 19 · 2016: 14 · 2015: 10 · 2014: 14 · 2013: 5 · 2012: 5
FWCI: 7.24
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/2.0 (tdm)
Locations: BMC Bioinformatics (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
Amsterdam UMC (VU Amsterdam) - Institutional Repository (publishedVersion, other-oa)
TU/e Research Portal (publishedVersion, other-oa)
Pure Amsterdam UMC (submittedVersion, other-oa)
TU/e Research Portal (submittedVersion, other-oa)
TU/e Research Portal (submittedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
VU Research Portal (submittedVersion, other-oa)
Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
Keywords: multiple comparisons, false discovery rate, high-dimensional data, microarray data, conditional independence structure, sparsity
MeSH terms: Algorithms, Sensitivity and Specificity, Statistics as Topic, Reproducibility of Results, Artifacts, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling

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Effects of dependence in high-dimensional multiple testing problems

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