Thus, in the replicate plots we compare two high-variance experiments, while in the observed-expected plots the expected values are obtained from a low-variance experiment.
cMSE-ANOVA represents variance among experiments and RT-PCR reactions within experiments.
Taguchi method analyses the results in two ways: (1) analysis of variance for experiments with a single replicate, (2) the signal to noise ratio (S/N) for experiments with multiple replications where (N) is noise factor and (S) is controllable factors.
Such algorithms actually work well, as evidenced by the fact that fewer than 2% of the probes on our array had high variance across experiments.
To compare variance across experiments and transcripts, I first estimated the per transcript variance (σ2) across the mean log2 transcript accumulation per experimental unit for each transcribed locus measured within a given experimental dataset (Table S1).
The data show considerable variance between experiments, and a nested ANCOVA was performed on the uptake values and their standard deviations to detect possible dependence of uptake on the availability of DNA and the strength of the DUES, as well as to estimate the magnitude of the noise (i.e., error) emanating from the variation between experiments.
By varying dispersal rates and levels of standing additive genetic variance in experiments, these predictions can be tested.
When possible, a nested ANOVA method was used to calculate the variance between experiments and the variance between groups and to draw comparisons.
Micheletti and Lye (2006) have indicated in their review article that inhomogeneities, particularly with fluid addition, are the major source of variance between experiments.
ChIP-seq data were analyzed with the ArchTEx program (Lai et al. 2012) and Z-scored to normalize for variance between experiments after calculating the log2 ratio.
A conservative estimate of the per-gene variance in experiments with a small sample size is obtained by pooling the samples across conditions, i.e. (5) and.
