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>) | |||
Revision as of 15:49, 9 May 2008
What is the resolution of my dataset?
First of all, it is limited by completeness. In practical terms this means that the highest resolution you can get is the resolution at the edge of the detector. If you collected enough frames, you may be able to squeeze out 0.1A if you process data all the way to the corner. Usually the detector is positioned close enough to the crystal so that you don't have any diffraction at the edge and then resolution limits should be chosen based on strength of the diffraction.
This limit is commonly based on average [math]\displaystyle{ I/\sigma }[/math]. Examples of such choices are:
- [math]\displaystyle{ I/\sigma=1 }[/math] in the highest resolution shell
- [math]\displaystyle{ I/\sigma=2 }[/math] in the highest resolution shell
- at least 50% of reflections in the highest resolution shell have [math]\displaystyle{ I/\sigma }[/math] > 2
...
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 Rmerge. When the resolution limit is selected based on Rmerge 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 Rmerge. Rmerge may and should be used as the measure of the overall data quality (e.g. of two independent datasets the one that has higher Rmerge probably is noisier).
One thing you achieve by choosing resolution limit based on Rmerge (which generally means that your [math]\displaystyle{ 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.
Rmerge criticism
Finally, Rmerge is the wrong quantitiy to look at altogether, because
- it depends on the multiplicity (unfortunately often called redundancy): the higher the multiplicity, the higher Rmerge becomes
- it assesses data consistency, not the quality of the reduced data
This has been discussed by Diederichs and Karplus([1]), who suggest a multiplicity-independant version called Rmeas, which unfortunately is not used by everyone because the formula gives higher values than Rmerge. R-factors for data quality assessment were also suggested by Diederichs and Karplus, and Weiss and Hilgenfeld [2])
- ↑ K. Diederichs and P.A. Karplus (1997). Improved R-factors for diffraction data analysis in macromolecular crystallography. Nature Struct. Biol. 4, 269-275 [1]
- ↑ 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[2]