This demonstrates that the DFC method does accurately take into account expression dependence of fold change and total variance, which are very much different in MAS5 and RMA pre-processed data, see, for example, Figure 1 for variance dependences.
Moreover, such percentiles have low variance, low dependence on sample size, and are amenable to bootstrap estimations of confidence intervals, when compared to the largest individual per sample [ 108].
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.
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 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.
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.
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.
Especially any variance-to-mean dependence remaining after the log2 variance stabilizing transformation will affect the regularized t-statistic when this is based on the assumption of homoscedasticity (i.e. homogeneity of variance), which is the case for limma and SAM.
And finally, there is the famous problem of "translation variance" or "framework dependence" of judgements of likeness and of truthlikeness.
