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Empirical versus theoretical power and type I error (false-positive) rates estimated from real murine aging research data.

ABSTRACT: We assess the degree of phenotypic variation in a cohort of 24-month-old male C57BL/6 mice. Because murine studies often use small sample sizes, if the commonly relied upon assumption of a normal distribution of residuals is not met, it may inflate type I error rates. In this study, 3-20 mice are resampled from the empirical distributions of 376 mice to create plasmodes, an approach for computing type I error rates and power for commonly used statistical tests without assuming a normal distribution of residuals. While all of the phenotypic and metabolic variables studied show considerable variability, the number of animals required to achieve adequate power is markedly different depending on the statistical test being performed. Overall, this work provides an analysis with which researchers can make informed decisions about the sample size required to achieve statistical power from specific measurements without a priori assumptions of a theoretical distribution.

SUBMITTER: Alfaras I

PROVIDER: S-EPMC8449850 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

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Empirical versus theoretical power and type I error (false-positive) rates estimated from real murine aging research data.

Alfaras Irene I Ejima Keisuke K Vieira Ligo Teixeira Camila C Di Germanio Clara C Mitchell Sarah J SJ Hamilton Samuel S Ferrucci Luigi L Price Nathan L NL Allison David B DB Bernier Michel M de Cabo Rafael R

Cell reports 20210801 7

We assess the degree of phenotypic variation in a cohort of 24-month-old male C57BL/6 mice. Because murine studies often use small sample sizes, if the commonly relied upon assumption of a normal distribution of residuals is not met, it may inflate type I error rates. In this study, 3-20 mice are resampled from the empirical distributions of 376 mice to create plasmodes, an approach for computing type I error rates and power for commonly used statistical tests without assuming a normal distribut ...[more]

PMID: 34407413

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Project description:BackgroundNext-generation sequencing has been used by investigators to address a diverse range of biological problems through, for example, polymorphism and mutation discovery and microRNA profiling. However, compared to conventional sequencing, the error rates for next-generation sequencing are often higher, which impacts the downstream genomic analysis. Recently, Wang et al. (BMC Bioinformatics 13:185, 2012) proposed a shadow regression approach to estimate the error rates for next-generation sequencing data based on the assumption of a linear relationship between the number of reads sequenced and the number of reads containing errors (denoted as shadows). However, this linear read-shadow relationship may not be appropriate for all types of sequence data. Therefore, it is necessary to estimate the error rates in a more reliable way without assuming linearity. We proposed an empirical error rate estimation approach that employs cubic and robust smoothing splines to model the relationship between the number of reads sequenced and the number of shadows.ResultsWe performed simulation studies using a frequency-based approach to generate the read and shadow counts directly, which can mimic the real sequence counts data structure. Using simulation, we investigated the performance of the proposed approach and compared it to that of shadow linear regression. The proposed approach provided more accurate error rate estimations than the shadow linear regression approach for all the scenarios tested. We also applied the proposed approach to assess the error rates for the sequence data from the MicroArray Quality Control project, a mutation screening study, the Encyclopedia of DNA Elements project, and bacteriophage PhiX DNA samples.ConclusionsThe proposed empirical error rate estimation approach does not assume a linear relationship between the error-free read and shadow counts and provides more accurate estimations of error rates for next-generation, short-read sequencing data.

Dataset Information

Empirical versus theoretical power and type I error (false-positive) rates estimated from real murine aging research data.

Publications

Empirical versus theoretical power and type I error (false-positive) rates estimated from real murine aging research data.

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