Technical variance is dependent on the protocol employed within a quantitative proteomics experiment and accumulated with every additional step.
It has been widely assumed, although not yet empirically verified, that early combination of samples minimises technical variance in relative quantification.
Our results indicate that the technical variance dominantly contributes to the total variance in mouse brain and the genetic background has a negligible effect on the total variation.
Different allocations of biological and technical replicates were evaluated by minimizing the variance of the ratio of technical variance (measurement error) to the total variance (sum of sampling error and measurement error).
The biological variance in protein expression of interest to biologists can only be accessed if the technical variance of the protein quantification method is low compared with the biological variance.
According to these guidelines, ΔCT values ≤ +/−0.5 are considered fluctuation in gene expression that is largely due to technical variance (e.g., unequal loading, PCR efficiency, etc).
The protocol estimates biological and technical variance between the groups using a mixed effects linear model involving a fixed term for the difference between groups and random deviations representing differences between samples and gels.
One possible explanation for this apparent contradiction would be that the first two principle components and the clustering trees obtained after data integration reflect biologically irrelevant technical variance not related to data source.
To assess the degree of technical variance in quantification of relative protein abundance levels, we calculated the coefficient of variation (CV) based on log mean average of protein ratios estimated from independent experimental runs.
Thus, reference genes that present with ΔCT values ≥ 1.0 should not be used for normalization, if indeed the observed changes in gene expression are due to biological versus technical variance, which may be difficult to discern in some circumstances.
Comparable to the data set obtained with the probes hybridizing to the ribosomal protein-encoding transcripts, the 69 respiratory chain proteins exhibited significantly more inter-tissue variance (median = 0.55) than technical variance (median = 0.01) (p<.0001 using Wilcoxon's rank sum test) (Figure S2B).
