Differential expression was assessed using DESeq2, which estimates variance-mean dependence in read counts and uses negative binomial distribution to test for differential expression.
DESeq estimates variance-mean dependence in count data from DGE-tag libraries and tests for differential expression based on a model using the negative binomial distribution.
Count data was then used as input for the program DESeq [ 47] which estimates variance-mean dependence in the data and tests for differential expression based on the negative binomial distribution.
The DESeq package, supported by a model based on negative binomial distribution, estimates variance-mean dependence in count data from high-throughput sequencing assays and tests for differential expression.
DESeq is a R bioconductor package which estimates the variance-mean dependence in count data from high-throughput sequencing assays, normalizes the count data for library sizes and dispersion, and tests for differential expression based on a model using the negative binomial distribution.
Yet improvements in energy efficiency mean dependence on oil is not what it once was.
For nutrition, vision, incontinence and cognitive impairment the difference in mean dependence score between these sub-groups was statistically significant.
Briefly, the joint adaptive regularization procedure simultaneously overcomes the lack of degrees of freedom and the variance-mean dependence issue in this type of dataset where the number of variables hugely dominates the number of sample [ 54].
The index exists of six items, a higher score on the Katz index of ADL means more dependence in ADL.
The method adopted to identify differentially expressed genes takes into account the variance-versus-mean dependence that exists in microarray data, which typically follows a power law [11].
To this end, we first fitted each set of biological replicates to a power law global error model (PLGEM), previously shown to faithfully describe the variance- vs.-mean dependence that exists in replicated high-density oligonucleotide microarray datasets as well as mass spectrometry-based proteomics datasets (Pavelka et al. 2004, 2008).
