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  • 1
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    A fast algorithm for determining bounds and accurate approximate p -values of the rank product statistic for replicate experiments (2014)
    BioMed Central
    Details
    Publication Date: 2014-11-27
    Description: Background: The rank product method is a powerful statistical technique for identifying differentially expressed molecules in replicated experiments. A critical issue in molecule selection is accurate calculation of the p-value of the rank product statistic to adequately address multiple testing. Both exact calculation and permutation and gamma approximations have been proposed to determine molecule-level significance. These current approaches have serious drawbacks as they are either computationally burdensome or provide inaccurate estimates in the tail of the p-value distribution. Results: We derive strict lower and upper bounds to the exact p-value along with an accurate approximation that can be used to assess the significance of the rank product statistic in a computationally fast manner. The bounds and the proposed approximation are shown to provide far better accuracy over existing approximate methods in determining tail probabilities, with the slightly conservative upper bound protecting against false positives. We illustrate the proposed method in the context of a recently published analysis on transcriptomic profiling performed in blood. Conclusions: We provide a method to determine upper bounds and accurate approximate p-values of the rank product statistic. The proposed algorithm provides an order of magnitude increase in throughput as compared with current approaches and offers the opportunity to explore new application domains with even larger multiple testing issue. The R code is published in one of the Additional files and is available at http://www.ru.nl/publish/pages/726696/rankprodbounds.zip.
    Electronic ISSN: 1471-2105
    Topics: Biology , Computer Science
    Published by BioMed Central
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  • 2
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    A comparative study of cell classifiers for image-based high-throughput screening (2014)
    BioMed Central
    Details
    Publication Date: 2014-10-22
    Description: Background: Millions of cells are present in thousands of images created in high-throughput screening (HTS). Biologists could classify each of these cells into a phenotype by visual inspection. But in the presence of millions of cells this visual classification task becomes infeasible. Biologists train classification models on a few thousand visually classified example cells and iteratively improve the training data by visual inspection of the important misclassified phenotypes. Classification methods differ in performance and performance evaluation time. We present a comparative study of computational performance of gentle boosting, joint boosting CellProfiler Analyst (CPA), support vector machines (linear and radial basis function) and linear discriminant analysis (LDA) on two data sets of HT29 and HeLa cancer cells. Results: For the HT29 data set we find that gentle boosting, SVM (linear) and SVM (RBF) are close in performance but SVM (linear) is faster than gentle boosting and SVM (RBF). For the HT29 data set the average performance difference between SVM (RBF) and SVM (linear) is 0.42%. For the HeLa data set we find that SVM (RBF) outperforms other classification methods and is on average 1.41% better in performance than SVM (linear). Conclusions: Our study proposes SVM (linear) for iterative improvement of the training data and SVM (RBF) for the final classifier to classify all unlabeled cells in the whole data set.
    Electronic ISSN: 1471-2105
    Topics: Biology , Computer Science
    Published by BioMed Central
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  • 3
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    A fast algorithm for determining bounds and accurate approximate p -values of the rank product statistic for replicate experiments (2014)
    BioMed Central
    Details
    Publication Date: 2014-11-22
    Description: Background: The rank product method is a powerful statistical technique for identifying differentially expressed molecules in replicated experiments. A critical issue in molecule selection is accurate calculation of the p-value of the rank product statistic to adequately address multiple testing. Both exact calculation and permutation and gamma approximations have been proposed to determine molecule-level significance. These current approaches have serious drawbacks as they are either computationally burdensome or provide inaccurate estimates in the tail of the p-value distribution. Results: We derive strict lower and upper bounds to the exact p-value along with an accurate approximation that can be used to assess the significance of the rank product statistic in a computationally fast manner. The bounds and the proposed approximation are shown to provide far better accuracy over existing approximate methods in determining tail probabilities, with the slightly conservative upper bound protecting against false positives. We illustrate the proposed method in the context of a recently published analysis on transcriptomic profiling performed in blood. Conclusions: We provide a method to determine upper bounds and accurate approximate p-values of the rank product statistic. The proposed algorithm provides an order of magnitude increase in throughput as compared with current approaches and offers the opportunity to explore new application domains with even larger multiple testing issue. The R code is published in one of the Additional files and is available athttp://www.ru.nl/publish/pages/726696/rankprodbounds.zip.
    Electronic ISSN: 1471-2105
    Topics: Biology , Computer Science
    Published by BioMed Central
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  • 4
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    Empirical Bayesian Random Censoring Threshold Model Improves Detection of Differentially Abundant Proteins (2014)
    American Chemical Society (ACS)
    Details
    Publication Date: 2014-08-24
    Description: Journal of Proteome Research DOI: 10.1021/pr500171u
    Print ISSN: 1535-3893
    Electronic ISSN: 1535-3907
    Topics: Chemistry and Pharmacology
    Published by American Chemical Society (ACS)
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  • 5
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    Conditional and interaction gene-set analysis reveals novel functional pathways for blood pressure (2018)
    Springer Nature
    Details
    Publication Date: 2018-09-15
    Description: Conditional and interaction gene-set analysis reveals novel functional pathways for blood pressure Conditional and interaction gene-set analysis reveals novel functional pathways for blood pressure, Published online: 14 September 2018; doi:10.1038/s41467-018-06022-6 Gene-set analysis (GSA) is widely used to infer functional and biological properties of a gene set. Here, the authors develop a conditional and interaction gene-set analysis approach that can considerably refine results from traditional GSA.
    Electronic ISSN: 2041-1723
    Topics: Biology , Chemistry and Pharmacology , Natural Sciences in General , Physics
    Published by Springer Nature
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  • 6
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    BCM: toolkit for Bayesian analysis of Computational Models using samplers (2016)
    BioMed Central
    Details
    Publication Date: 2016-10-23
    Description: Computational models in biology are characterized by a large degree of uncertainty. This uncertainty can be analyzed with Bayesian statistics, however, the sampling algorithms that are frequently used for calc...
    Electronic ISSN: 1752-0509
    Topics: Biology
    Published by BioMed Central
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  • 7
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    Exact p-values for pairwise comparison of Friedman rank sums, with application to comparing classifiers (2017)
    BioMed Central
    Details
    Publication Date: 2017-01-27
    Description: The Friedman rank sum test is a widely-used nonparametric method in computational biology. In addition to examining the overall null hypothesis of no significant difference among any of the rank sums, it is ty...
    Electronic ISSN: 1471-2105
    Topics: Biology , Computer Science
    Published by BioMed Central
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