A more powerful test for comparing two Poisson means

Copy number variants (CNVs) are widely distributed throughout the human genome, where they contribute to genetic variation and phenotypic diversity. Spontaneous CNVs are also a major cause of genetic and developmental disorders and arise frequently in cancer cells. As with all mutation classes, genetic and environmental factors almost certainly increase the risk for new and deleterious CNVs. However, despite the importance of CNVs, there is limited understanding of these precipitating risk factors and the mechanisms responsible for a large percentage of CNVs. Here we report that low doses of hydroxyurea, an inhibitor of ribonucleotide reductase and an important drug in the treatment of sickle cell disease and other diseases induces a high frequency of de novo CNVs in cultured human cells that resemble pathogenic and aphidicolin-induced CNVs in size and breakpoint structure. These CNVs are distributed throughout the genome, with some hotspots of de novo CNV formation. Sequencing revealed that CNV breakpoint junctions are characterized by short microhomologies, blunt ends, and short insertions. These data provide direct experimental support for models of replication-error origins of CNVs and suggest that any agent or condition that leads to replication stress has the potential to induce deleterious CNVs. In addition, they point to a need for further study of the genomic consequences of the therapeutic use of hydroxyurea.

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“…1D. Therefore, P values of treated vs. untreated samples were determined using the one-sided E test for comparing two Poisson mean rates (57).…”

Section: Methodsmentioning

confidence: 99%

Hydroxyurea induces de novo copy number variants in human cells

Arlt¹,

Ozdemir²,

Birkeland

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

103

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“…Poisson distribution statistics were used to calculate 95% confidence intervals for counts. The E-Test statistic described by Krishnamoorthy & Thomson (2004) was used to test for significant differences.…”

Section: Comparison With Conventional Microscopymentioning

confidence: 99%

Microzooplankton community structure investigated with imaging flow cytometry and automated live-cell staining

Brownlee

Olson²,

Sosik³

2016

Mar. Ecol. Prog. Ser.

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Protozoa play important roles in grazing and nutrient recycling, but quantifying these roles has been hindered by difficulties in collecting, culturing, and observing these often-delicate cells. During long-term deployments at the Martha's Vineyard Coastal Observatory (Massachusetts, USA), Imaging FlowCytobot (IFCB) has been shown to be useful for studying live cells in situ without the need to culture or preserve. IFCB records images of cells with chlorophyll fluorescence above a trigger threshold, so to date taxonomically resolved analysis of protozoa has presumably been limited to mixotrophs and herbivores which have eaten recently. To overcome this limitation, we have coupled a broad-application 'live cell' fluorescent stain with a modified IFCB so that protozoa which do not contain chlorophyll (such as consumers of unpigmented bacteria and other heterotrophs) can also be recorded. Staining IFCB (IFCB-S) revealed higher abundances of grazers than the original IFCB, as well as some cell types not previously detected. Feeding habits of certain morphotypes could be inferred from their fluorescence properties: grazers with stain fluorescence but without chlorophyll cannot be mixotrophs, but could be either starving or feeding on heterotrophs. Comparisons between cell counts for IFCB-S and manual light microscopy of Lugol's stained samples showed consistently similar or higher counts from IFCB-S. We show how automated classification through the extraction of image features and application of a machine-learning algorithm can be used to evaluate the large high-resolution data sets collected by IFCBs; the results reveal varying seasonal patterns in abundance among groups of protists.

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“…We used the method [19] to calculate the power of the LRT given a p-value from the equation (16) under LRT. This method originally borrowed a concept from this study [27] to calculate the power. Given a p-value based on the observed joint probability P (X 0 =x 0 , X 1 =x 1 ), the power under the assumption can be expressed as ( )…”

Section: Derivation Of Sample Size Based On the Likelihood Ratio Testmentioning

confidence: 99%

Inference and Sample Size Calculations Based on Statistical Tests in a Negative Binomial Distribution for Differential Gene Expression in RNAseq Data

Li¹,

Cooper²,

Shyr³

et al. 2017

J Biom Biostat

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The high throughput RNA sequencing (RNA-seq) technology has become the popular method of choice for transcriptomics and the detection of differentially expressed genes. Sample size calculations for RNA-seq experimental design are an important consideration in biological research and clinical trials. Currently, the sample size formulas derived from the Wald and the likelihood ratio statistical tests with a Poisson distribution to model RNA-seq data have been developed. However, since the mean read counts in the real RNA-seq data are not equal to the variance, an extended method to calculate sample sizes based on a negative binomial distribution using an exact test statistic was proposed by . In this study, we alternatively derive five sample size calculation methods based on the negative binomial distribution using the Wald test, the log-transformed Wald test and the log-likelihood ratio test statistics. A comparison of our five methods and an existing method was performed by calculating the sample sizes and the simulated power in different scenarios. We first calculated the sample sizes for testing a single gene using the six methods given a nominal significance level α at 0.05 and 80% power. Then, we calculated the sample sizes for testing multiple genes given a false discovery rate (FDR) at 0.05 and 0.10. The empirical power and true prognostic genes for differential gene expression analysis corresponding to the estimated sample sizes from the six methods are also estimated via the simulation studies. Using the sample size formulas derived from log-transformed and Wald-based tests, we observed smaller sample properties while maintaining the nominal power close to or higher than 80% in all the settings compared to other methods. Moreover, the Wald test based sample size calculation method is easier to compute and faster in an RNA-seq experimental design. Later, several sample size calculation methods that were derived from the score statistic and the log-likelihood ratio test (LRT) statistic using the Poisson distribution were proposed [16]. However, the assumption of a Poisson distribution that the expected mean and variance are equal usually does not hold for RNA-seq studies, where the variance is typically greater than the mean of the read counts [17]. Therefore, a negative binomial distribution with a dispersion parameter is used to model RNA-seq data by the existing software packages such as DESeq [17] and edgeR [18], in which an exact test is used to test DEGs between conditions. Subsequently, a sample size calculation method based on an exact test statistic with the aid of the edgeR package [18] was proposed [19]. However, sample size methods derived from other test statistics such as the Wald test, the LRT and an extension of Wald test via log-transformation using negative binomial distribution to model the RNA-seq data have not yet been explored.

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A more powerful test for comparing two Poisson means

Cited by 129 publications

References 10 publications

Hydroxyurea induces de novo copy number variants in human cells

Hydroxyurea induces de novo copy number variants in human cells

Microzooplankton community structure investigated with imaging flow cytometry and automated live-cell staining

Inference and Sample Size Calculations Based on Statistical Tests in a Negative Binomial Distribution for Differential Gene Expression in RNAseq Data

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