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The more DNA differences there are between two samples, for example, the longer it must be since they genetically diverged; hence, they form a kind of evolutionary clock.
We considered that the agreement of detected microRNAs in each sample and differentially expressed microRNAs between two samples for each microarray platform.
With a minimum coverage of 3×, we found more than 98% concordance between two samples for SNVs.
Because these biases are dependent on sequence context, fold change analysis between two samples for the same target may be less affected.
Briefly, an expression ratio between two samples for every gene (or exon) with > 500 reads is calculated, and the median of those ratios is determined.
Here we show, under some very simple assumptions, that when testing for differences between two samples for a given expression level, a longer gene will be more significant that a shorter gene.
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However, the additional Zn-OH MO states in the nanosheet modify these empty states, and thus, the relative ratio of E2 and E1 peak intensity is quite different between two samples: 2.1 for the hierarchical structure and 4.5 for the nanosheet.
A list of detected microRNAs in each sample and the differentially expressed microRNAs between two samples were identified for each assay.
A rigorous algorithm to identify differentially expressed genes between two samples was developed for significance testing [ 68].
Additional features such as different agglomeration methods for estimating distance between two samples are also added for clustering.
As the differences between the samples were small (differences between the two samples for all categories are under 5%%) and we considered that they were unlikely to affect results to a great extent, we did not adjust for them in the statistical analysis.
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