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We quantified this variation between cells using the noise strength, defined by ratio of the variance to the mean.
This projection map is qualitative, because it assumes a 2-dimensional cell, and it eliminates the stochastic variation between cells.
This enables investigating the process at the single cell level and provides important qualitative information; however, it requires examining cells one by one, which is very time-consuming and limits the sample size, whereas variation between cells may be high.
For each independent experiment we carried out with a given [PSI+] strain, the cultures were derived from different colonies although for any given population one would expect to see similar variation between cells.
The second layer models the variation between cells.
We observed some variation between cells derived from different pigs.
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This section is divided into two sections for determine the performance variation between Cell A and Cell B. The first section compares the differences in electrochemical performance, and the second section compares the surface characteristics between the two electrolysers.
Values of the intron expression statistic calculated from these arrays suggest that technical variation as well as variation between cell passages make a contribution to the observed differences between arrays (Fig. 1a); nonetheless, the difference between cell lines was highly statistically significant, despite the variation between repeats (p = 1.0×10−5, from a two-tailed t-test).
As a result, there is a great deal of variation between cell lines; different groups have measured a variety of markers in a number of ways and results vary even in the same cell lines, perhaps, as we show here, due to different culture conditions or seeding densities.
Hierarchical clustering showed that the variation between cell lines exceeded variation between protocols.
However, some of these differences in pathway activation may be due in part to genetic variation between cell lines.
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dispersion between cells
variation between prisons
variation between nuclei
variation between units
variation between centers
diversity between cells
gap between cells
movement between cells
differentiation between cells
variation between settings
variation between companies
variation between countries
variation between patients
variation between communities
variation between arms
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