Abstract Integrative analysis of copy number and gene expression data can help in understanding the cis and trans effect of copy number aberrations on transcription levels of genes involved in a pathway. To analyse how these copy number mediated gene-gene interactions differ between groups of samples we propose a new method, named dNET. Our method uses ridge regression to model the network topology involving one gene’s expression level, its gene dosage and the expression levels of other genes in the network. The interaction parameters are estimated by fitting the model per gene for all samples together. However, instead of testing for differential network topology per gene, dNET tests for an overall difference in estimated parameters between two groups of samples and produces a single p -value. With the help of several simulation studies, we show that dNET can detect differential network nodes with high accuracy and low rate of false positives even in the presence of differential cis effects. We also apply dNET to publicly available TCGA cancer datasets and identify pathways where copy number mediated gene-gene interactions differ between samples with cancer stage lower than stage 3 and samples with cancer stage 3 or above.
When testing for the association of a single SNP with a phenotypic response, one usually considers an additive genetic model, assuming that the mean of of the response for the heterozygous state is the average of the means for the two homozygous states. However, this simplification often does not hold. In this paper, we present a novel framework for testing the association of a single SNP and a phenotype. Different from the predominant standard approach, our methodology is guaranteed to detect all dependencies expressed by classical genetic association models. The asymptotic distribution under mild regularity assumptions is derived. Moreover, the finite sample distribution under Gaussianity is provided in which the exact p-value can be efficiently evaluated via the classical Appell hypergeometric series. Both results are extended to a regression-type setting with nuisance covariates, enabling hypotheses testing in a wide range of scenarios. A connection of our approach to score tests is explored, leading to intuitive interpretations as locally most powerful tests. A simulation study demonstrates the computational efficiency and excellent statistical performance of the proposed methodology. A real data example is provided.
Abstract The value of iron-based MRI changes for the diagnosis and staging of Alzheimer’s disease (AD) depends on an association between cortical iron accumulation and AD pathology. Therefore, this study determined the cortical distribution pattern of MRI contrast changes in cortical regions selected based on the known distribution pattern of tau pathology and investigated whether MRI contrast changes reflect the underlying AD pathology in the different lobes. -weighted MRI was performed on post-mortem cortical tissue of controls, late-onset AD, and early-onset AD followed by histology and correlation analyses. Combining ex-vivo high-resolution MRI and histopathology revealed that: LOAD and EOAD have a different distribution pattern of AD pathological hallmarks and MRI contrast changes over the cortex, with EOAD showing more severe MRI changes; (2) per lobe, severity of AD pathological hallmarks correlates with iron accumulation, and hence with MRI. Therefore, iron-sensitive MRI sequences allow detection of the cortical distribution pattern of AD pathology ex-vivo. Abbreviations AD Alzheimer’s disease EOAD early-onset AD GM gray matter IRP iron regulating proteins LOAD late-onset AD MCI mild cognitive impairment PBS phosphate buffered saline QSM quantitative susceptibility mapping WM white matter
The prevalence of hairy leukoplakia was determined among 176 symptomatic HIV seropositive patients seen at the outpatient department of the Institute of Tropical Medicine in Antwerp, Belgium. Moreover, systematic tongue biopsies were performed during postmortem examination of 21 patients with AIDS, 100 HIV seronegative immunocompromised patients with haematologic or other malignancies and 100 HIV seronegative non-immunocompromised patients who died at the University Hospital Antwerp. Hairy leukoplakia was observed in 52 (29.5%) of the outpatients, but only in one (5%) of the AIDS patients in the postmortem study (P = 0.03). An explanation for this difference may be that significantly more AIDS patients who died had received either acyclovir or ganciclovir during the 3 months prior to the postmortem examination than the HIV seropositive outpatients during the 3 months prior to examination. Hairy leukoplakia occurred more often in Caucasian homosexual men with HIV infection (38%) than among heterosexual Africans with HIV infection (17%) (P = 0.06). Hairy leukoplakia was observed in none of the HIV seronegative patients.
When a ranking of institutions such as medical centers or universities is based on an indicator provided with a standard error, confidence intervals should be calculated to assess the quality of these ranks. We consider the problem of constructing simultaneous confidence intervals for the ranks of means based on an observed sample. For this aim, the only available method from the literature uses Monte-Carlo simulations and is highly anticonservative especially when the means are close to each other or have ties. We present a novel method based on Tukey's honest significant difference test (HSD). Our new method is on the contrary conservative when there are no ties. By properly rescaling these two methods to the nominal confidence level, they surprisingly perform very similarly. The Monte-Carlo method is however unscalable when the number of institutions is large than 30 to 50 and stays thus anticonservative. We provide extensive simulations to support our claims and the two methods are compared in terms of their simultaneous coverage and their efficiency. We provide a data analysis for 64 hospitals in the Netherlands and compare both methods. Software for our new methods is available online in package ICRanks downloadable from CRAN. Supplementary materials include supplementary R code for the simulations and proofs of the propositions presented in this paper.
Abstract The Globaltest is a powerful test for the global null hypothesis that there is no association between a group of features and a response of interest, which is popular in pathway testing in metabolomics. Evaluating multiple feature sets, however, requires multiple testing correction. In this paper, we propose a multiple testing method, based on closed testing, specifically designed for the Globaltest. The proposed method controls the familywise error rate simultaneously over all possible feature sets, and therefore allows post hoc inference, that is, the researcher may choose feature sets of interest after seeing the data without jeopardizing error control. To circumvent the exponential computation time of closed testing, we derive a novel shortcut that allows exact closed testing to be performed on the scale of metabolomics data. An R package ctgt is available on comprehensive R archive network for the implementation of the shortcut procedure, with applications on several real metabolomics data examples.
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