Nevertheless, it remained challenging to interpret correlations linking the structure and properties of chemical agents to their subcellular localization patterns in large numbers of cells, captured across large number of images.
In order to more easily interpret correlations, the ΔCPT and ΔCDT scores were inverted.
In order to interpret correlations, particularly when examinations differ in their reliability, it is helpful to 'disattenuate' them for differences in reliability.
Because origins, tRNAs, and Ty/LTRs are clustered throughout the genome, how do we interpret correlations between these features and breakpoints?
The second tool that Burant discussed was CoolMap (developed by colleagues G. Su and M. Fan), which enables researchers to visualize large, two-dimensional data and to interpret correlations between datasets.
In the absence of global remapping, it may thus not be safe to interpret correlations between changes in stimuli and place cell activity as representing information that is transmitted to other brain regions.
However, given the nonlinear dynamics of GRNs, the assumption of such linear relationship between cell state and TF expression, which also interprets correlation as causation, is simplistic.
While there may be some indication of a direct relationship between EMSA band II signal and reporter ELISA results, we suggest caution in interpreting correlation meta-analysis.
