共查询到20条相似文献，搜索用时 0 毫秒
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Jürgen Läuter Friedemann Horn Maciej Rosołowski Ekkehard Glimm 《Biometrical journal. Biometrische Zeitschrift》2009,51(2):235-251
The paper presents effective and mathematically exact procedures for selection of variables which are applicable in cases with a very high dimension as, for example, in gene expression analysis. Choosing sets of variables is an important method to increase the power of the statistical conclusions and to facilitate the biological interpretation. For the construction of sets, each single variable is considered as the centre of potential sets of variables. Testing for significance is carried out by means of the Westfall‐Young principle based on resampling or by the parametric method of spherical tests. The particular requirements for statistical stability are taken into account; each kind of overfitting is avoided. Thus, high power is attained and the familywise type I error can be kept in spite of the large dimension. To obtain graphical representations by heat maps and curves, a specific data compression technique is applied. Gene expression data from B‐cell lymphoma patients serve for the demonstration of the procedures. 相似文献
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O'Brien (1984, Biometrics 40, 1079-1087) introduced a simple nonparametric test procedure for testing whether multiple outcomes in one treatment group have consistently larger values than outcomes in the other treatment group. We first explore the theoretical properties of O'Brien's test. We then extend it to the general nonparametric Behrens-Fisher hypothesis problem when no assumption is made regarding the shape of the distributions. We provide conditions when O'Brien's test controls its error probability asymptotically and when it fails. We also provide adjusted tests when the conditions do not hold. Throughout this article, we do not assume that all outcomes are continuous. Simulations are performed to compare the adjusted tests to O'Brien's test. The difference is also illustrated using data from a Parkinson's disease clinical trial. 相似文献
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Benjamin A. Shaby Gaia Skibinski Michael Ando Eva S. LaDow Steven Finkbeiner 《Biometrics》2016,72(3):936-944
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Boulesteix AL Guillemot V Sauerbrei W 《Biometrical journal. Biometrische Zeitschrift》2011,53(4):673-688
Extreme values in predictors often strongly affect the results of statistical analyses in high-dimensional settings. Although they frequently occur with most high-throughput techniques, the problem is often ignored in the literature. We suggest to use a very simple transformation, proposed before in a different context by Royston and Sauerbrei, as an intermediary step between array preprocessing and high-level statistical analysis. This straightforward univariate transformation identifies extreme values in continuous features and can thus be used as a diagnostic tool for outliers. The use of the transformation and its effects is demonstrated for diverse univariate and multivariate statistical analyses using nine publicly available microarray data sets. 相似文献
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Attila Csala Michel H. Hof Aeilko H. Zwinderman 《Biometrical journal. Biometrische Zeitschrift》2019,61(2):406-423
Redundancy Analysis (RDA) is a well‐known method used to describe the directional relationship between related data sets. Recently, we proposed sparse Redundancy Analysis (sRDA) for high‐dimensional genomic data analysis to find explanatory variables that explain the most variance of the response variables. As more and more biomolecular data become available from different biological levels, such as genotypic and phenotypic data from different omics domains, a natural research direction is to apply an integrated analysis approach in order to explore the underlying biological mechanism of certain phenotypes of the given organism. We show that the multiset sparse Redundancy Analysis (multi‐sRDA) framework is a prominent candidate for high‐dimensional omics data analysis since it accounts for the directional information transfer between omics sets, and, through its sparse solutions, the interpretability of the result is improved. In this paper, we also describe a software implementation for multi‐sRDA, based on the Partial Least Squares Path Modeling algorithm. We test our method through simulation and real omics data analysis with data sets of 364,134 methylation markers, 18,424 gene expression markers, and 47 cytokine markers measured on 37 patients with Marfan syndrome. 相似文献
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Parallels between the discrete group-testing model and some closely-related continuous models are elucidated. It is shown that in both the discrete and continuous cases, the maximum likelihood estimators may suffer from similar lack of robustness. Isotonic regression and maximum likelihood estimation were therefore compared for a modified group testing model. 相似文献
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Duality in testing multivariate hypotheses 总被引：1，自引：0，他引：1
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Runwei Yang Jinglin Guo Zhiying Lin Haimin Song Zhanpeng Feng Yichao Ou Mingfeng Zhou Yaomin Li Guozhong Yi Ke Li Kaishu Li Manlan Guo Xiran Wang Guanglong Huang Zhifeng Liu Songtao Qi Yawei Liu 《Journal of biophotonics》2020,13(2)
Heterogeneity is regarded as the major factor leading to the poor outcomes of glioblastoma (GBM) patients. However, conventional two‐dimensional (2D) analysis methods, such as immunohistochemistry and immunofluorescence, have limited capacity to reveal GBM spatial heterogeneity. Thus, we sought to develop an effective analysis strategy to increase the understanding of GBM spatial heterogeneity. Here, 2D and three‐dimensional (3D) analysis methods were compared for the examination of cell morphology, cell distribution and large intact structures, and both types of methods were employed to dissect GBM spatial heterogeneity. The results showed that 2D assays showed only cross‐sections of specimens but provided a full view. To visualize intact GBM specimens in 3D without sectioning, the optical tissue clearing methods CUBIC and iDISCO+ were used to clear opaque specimens so that they would become more transparent, after which the specimens were imaged with a two‐photon microscope. The 3D analysis methods showed specimens at a large spatial scale at cell‐level resolution and had overwhelming advantages in comparison to the 2D methods. Furthermore, in 3D, heterogeneity in terms of cell stemness, the microvasculature, and immune cell infiltration within GBM was comprehensively observed and analysed. Overall, we propose that 2D and 3D analysis methods should be combined to provide much greater detail to increase the understanding of GBM spatial heterogeneity. 相似文献
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Summary : Lehmann and Romano (2005, Annals of Statistics 33, 1138–1154) discuss a Bonferroni-type procedure that bounds the probability that the number of false positives is larger than a specified number. We note that this procedure will have poor power as compared to a multivariate permutation test type procedure when the experimental design accommodates a permutation test. An example is given involving gene expression microarray data of breast cancer tumors. 相似文献