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Data quality: Difference between revisions
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Some of these choices are more liberal than others (and so will give you higher resolution). It is probably not worthwhile to argue which choice is the best, since it is indeed a matter of personal preference. | Some of these choices are more liberal than others (and so will give you higher resolution). It is probably not worthwhile to argue which choice is the best, since it is indeed a matter of personal preference. | ||
There is not probably much reason to limit resolution by | There is not probably much reason to limit resolution by R<sub>merge</sub>. When the resolution limit is selected based on R<sub>merge</sub> being less than certain cutoff, the argument is that in higher resolution shells the variation among independent measurements of the intensity of the same reflection is too high. But such variation is bound to be high for weak reflections. Plus, factors such as redundancy may significantly affect R<sub>merge</sub>. R<sub>merge</sub> may and should be used as the measure of the overall data quality (e.g. of two independent datasets the one that has higher R<sub>merge</sub> probably is noisier). | ||
One thing you achieve by choosing resolution limit based on | One thing you achieve by choosing resolution limit based on R<sub>merge</sub> (which generally means that your <math>I/\sigma</math> in the highest resolution shell will be >4), of course, is lower R-factors in refinement. It is perfectly OK to aspire low R-factors, but to achieve this by throwing away data probably isn't. | ||
== R<sub>merge</sub> criticism == | |||
Finally, R<sub>merge</sub> is the wrong quantitiy to look at altogether, because | |||
* it depends on the multiplicity (unfortunately often called redundancy): the higher the multiplicity, the higher R<sub>merge</sub> becomes | |||
* it assesses data consistency, not the quality of the reduced data | |||
This has been discussed by Diederichs and Karplus(<ref name="DiKa97">K. Diederichs and P.A. Karplus (1997). Improved R-factors for diffraction data analysis in macromolecular crystallography. Nature Struct. Biol. 4, 269-275 [http://strucbio.biologie.uni-konstanz.de/strucbio/files/nsb-1997.pdf]</ref>), who suggest a multiplicity-independant version called R<sub>meas</sub>, which unfortunately is not used by everyone because the formula gives higher values than R<sub>merge</sub>. R-factors for data quality assessment were also suggested by Diederichs and Karplus, and Weiss and Hilgenfeld <ref name="WeHi97">M.S. Weiss and R. Hilgenfeld (1997) On the use of the merging R-factor as a quality indicator for X-ray data. J. Appl. Crystallogr. 30, 203-205[http://dx.doi.org/10.1107/S0021889897003907]</ref>) | |||