Abstract
BackgroundmRNA expression data from next generation sequencing platforms is obtained in the form of counts per gene or exon. Counts have classically been assumed to follow a Poisson distribution in which the variance is equal to the mean. The Negative Binomial distribution which allows for over-dispersion, i.e., for the variance to be greater than the mean, is commonly used to model count data as well.ResultsIn mRNA-Seq data from 25 subjects, we found technical variation to generally follow a Poisson distribution as has been reported previously and biological variability was over-dispersed relative to the Poisson model. The mean-variance relationship across all genes was quadratic, in keeping with a Negative Binomial (NB) distribution. Over-dispersed Poisson and NB distributional assumptions demonstrated marked improvements in goodness-of-fit (GOF) over the standard Poisson model assumptions, but with evidence of over-fitting in some genes. Modeling of experimental effects improved GOF for high variance genes but increased the over-fitting problem.ConclusionsThese conclusions will guide development of analytical strategies for accurate modeling of variance structure in these data and sample size determination which in turn will aid in the identification of true biological signals that inform our understanding of biological systems.
Highlights
MRNA expression data from generation sequencing platforms is obtained in the form of counts per gene or exon
The top 5% of GOF statistics are indicated in alternate colors with the top 1% being red and the 4% being blue
We found that the within-gene variance structure is overdispersed relative to the Poisson distribution
Summary
MRNA expression data from generation sequencing platforms is obtained in the form of counts per gene or exon. Counts have classically been assumed to follow a Poisson distribution in which the variance is equal to the mean. The Negative Binomial distribution which allows for over-dispersion, i.e., for the variance to be greater than the mean, is commonly used to model count data as well. There are several reports on quality assessments of generation sequencing and comparisons with microarray gene expression [1,2]. Anders and Huber report on variation in pools of two fruit fly embryos [6]; to date there is no thorough report on the functional form of variance in variability in mRNA Sequencing data from true human biological replicates. We evaluated the structure of biological variability and statistical modeling strategies useful for
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