Hi all, In the context of the above going discussion can anybody post links for a few relevant articles?
Thanks in advance, ARKO On Mon, Jan 30, 2012 at 3:05 AM, Randy Read <rj...@cam.ac.uk> wrote: > Just one thing to add to that very detailed response from Ian. > > We've tended to use a slightly different approach to determining a > sensible resolution cutoff, where we judge whether there's useful > information in the highest resolution data by whether it agrees with > calculated structure factors computed from a model that hasn't been refined > against those data. We first did this with the complex of the Shiga-like > toxin B-subunit pentamer with the Gb3 trisaccharide (Ling et al, 1998). > From memory, the point where the average I/sig(I) drops below 2 was around > 3.3A. However, we had a good molecular replacement model to solve this > structure and, after just carrying out rigid-body refinement, we computed a > SigmaA plot using data to the edge of the detector (somewhere around 2.7A, > again from memory). The SigmaA plot dropped off smoothly to 2.8A > resolution, with values well above zero (indicating significantly better > than random agreement), then dropped suddenly. So we chose 2.8A as the > cutoff. Because there were four pentamers in the asymmetric unit, we could > then use 20-fold NCS averaging, which gave a fantastic map. In this case, > the averaging certainly helped to pull out something very useful from a > very weak signal, because the maps weren't nearly as clear at lower > resolution. > > Since then, a number of other people have applied similar tests. Notably, > Axel Brunger has done some careful analysis to show that it can indeed be > useful to take data beyond the conventional limits. > > When you don't have a great MR model, you can do something similar by > limiting the resolution for the initial refinement and rebuilding, then > assessing whether there's useful information at higher resolution by using > the improved model (which hasn't seen the higher resolution data) to > compute Fcalcs. By the way, it's not necessary to use a SigmaA plot -- the > correlation between Fo and Fc probably works just as well. Note that, when > the model has been refined against the lower resolution data, you'll expect > a drop in correlation at the resolution cutoff you used for refinement, > unless you only use the cross-validation data for the resolution range used > in refinement. > > ----- > Randy J. Read > Department of Haematology, University of Cambridge > Cambridge Institute for Medical Research Tel: +44 1223 336500 > Wellcome Trust/MRC Building Fax: +44 1223 336827 > Hills Road > E-mail: rj...@cam.ac.uk > Cambridge CB2 0XY, U.K. > www-structmed.cimr.cam.ac.uk > > On 29 Jan 2012, at 17:25, Ian Tickle wrote: > > > Jacob, here's my (personal) take on this: > > > > The data quality metrics that everyone uses clearly fall into 2 > > classes: 'consistency' metrics, i.e. Rmerge/meas/pim and CC(1/2) which > > measure how well redundant observations agree, and signal/noise ratio > > metrics, i.e. mean(I/sigma) and completeness, which relate to the > > information content of the data. > > > > IMO the basic problem with all the consistency metrics is that they > > are not measuring the quantity that is relevant to refinement and > > electron density maps, namely the information content of the data, at > > least not in a direct and meaningful way. This is because there are 2 > > contributors to any consistency metric: the systematic errors (e.g. > > differences in illuminated volume and absorption) and the random > > errors (from counting statistics, detector noise etc.). If the data > > are collected with sufficient redundancy the systematic errors should > > hopefully largely cancel, and therefore only the random errors will > > determine the information content. Therefore the systematic error > > component of the consistency measure (which I suspect is the biggest > > component, at least for the strong reflections) is not relevant to > > measuring the information content. If the consistency measure only > > took into account the random error component (which it can't), then it > > would be essentially be a measure of information content, if only > > indirectly (but then why not simply use a direct measure such as the > > signal/noise ratio?). > > > > There are clearly at least 2 distinct problems with Rmerge, first it's > > including systematic errors in its measure of consistency, second it's > > not invariant with respect to the redundancy (and third it's useless > > as a statistic anyway because you can't do any significance tests on > > it!). The redundancy problem is fixed to some extent with Rpim etc, > > but that still leaves the other problems. It's not clear to me that > > CC(1/2) is any better in this respect, since (as far as I understand > > how it's implemented), one cannot be sure that the systematic errors > > will cancel for each half-dataset Imean, so it's still likely to > > contain a large contribution from the irrelevant systematic error > > component and so mislead in respect of the real data quality exactly > > in the same way that Rmerge/meas/pim do. One may as well use the > > Rmerge between the half dataset Imeans, since there would be no > > redundancy effect (i.e. the redundancy would be 2 for all included > > reflections). > > > > I did some significance tests on CC(1/2) and I got silly results, for > > example it says that the significance level for the CC is ~ 0.1, but > > this corresponded to a huge Rmerge (200%) and a tiny mean(I/sigma) > > (0.4). It seems that (without any basis in statistics whatsoever) the > > rule-of-thumb CC > 0.5 is what is generally used, but I would be > > worried that the statistics are so far divorced from the reality - it > > suggests that something is seriously wrong with the assumptions! > > > > Having said all that, the mean(I/sigma) metric, which on the face of > > it is much more closely related to the information content and > > therefore should be a more relevant metric than Rmerge/meas/pim & > > CC(1/2), is not without its own problems (which probably explains the > > continuing popularity of the other metrics!). First and most obvious, > > it's a hostage to the estimate of sigma(I) used. I've never been > > happy with inflating the counting sigmas to include effects of > > systematic error based on the consistency of redundant measurements, > > since as I indicated above if the data are collected redundantly in > > such a way that the systematic errors largely cancel, it implies that > > the systematic errors should not be included in the estimate of sigma. > > The fact that then the sigma(I)'s would generally be smaller (at > > least for the large I's), so the sample variances would be much larger > > than the counting variances, is irrelevant, because the former > > includes the systematic errors. Also the I/sigma cut-off used would > > probably not need to be changed since it affects only the weakest > > reflections which are largely unaffected by the systematic error > > correction. > > > > The second problem with mean(I/sigma) is also obvious: i.e. it's a > > mean, and as such it's rather insensitive to the actual distribution > > of I/sigma(I). For example if a shell contained a few highly > > significant intensities these could be overwhelmed by a large number > > of weak data and give an insignificant mean(I/sigma). It seems to me > > that one should be considering the significance of individual > > reflections, not the shell averages. Also the average will depend on > > the width of the resolution bin, so one will get the strange effect > > that the apparent resolution will depend on how one bins at the data! > > The assumption being made in taking the bin average is that I/sigma(I) > > falls off smoothly with d* but that's unlikely to be the reality. > > > > It seems to me that a chi-square statistic which takes into account > > the actual distribution of I/sigma(I) would be a better bet than the > > bin average, though it's not entirely clear how one would formulate > > such a metric. One would have to consider subsets of the data as a > > whole sorted by increasing d* (i.e. not in resolution bins to avoid > > the 'bin averaging effect' described above), and apply the resolution > > cut-off where the chi-square statistic has maximum probability. This > > would automatically take care of incompleteness effects since all > > unmeasured reflections would be included with I/sigma = 0 just for the > > purposes of working out the cut-off point. I've skipped the details > > of implementation and I've no idea how it would work in practice! > > > > An obvious question is: do we really need to worry about the exact > > cut-off anyway, won't our sophisticated maximum likelihood refinement > > programs handle the weak data correctly? Note that in theory weak > > intensities should be handled correctly, however the problem may > > instead lie with incorrectly estimated sigmas: these are obviously > > much more of an issue for any software which depends critically on > > accurate estimates of uncertainty! I did some tests where I refined > > data for a known protein-ligand complex using the original apo model, > > and looked at the difference density for the ligand, using data cut at > > 2.5, 2 and 1.5 Ang where the standard metrics strongly suggested there > > was only data to 2.5 Ang. > > > > I have to say that the differences were tiny, well below what I would > > deem significant (i.e. not only the map resolutions but all the map > > details were essentially the same), and certainly I would question > > whether it was worth all the soul-searching on this topic over the > > years! So it seems that the refinement programs do indeed handle weak > > data correctly, but I guess this should hardly come as a surprise (but > > well done to the software developers anyway!). This was actually > > using Buster: Refmac seems to have more of a problem with scaling & > > TLS if you include a load of high resolution junk data. However, > > before anyone acts on this information I would _very_ strongly advise > > them to repeat the experiment and verify the results for themselves! > > The bottom line may be that the actual cut-off used only matters for > > the purpose of quoting the true resolution of the map, but it doesn't > > significantly affect the appearance of the map itself. > > > > Finally an effect which confounds all the quality metrics is data > > anisotropy: ideally the cut-off surface of significance in reciprocal > > space should perhaps be an ellipsoid, not a sphere. I know there are > > several programs for anisotropic scaling, but I'm not aware of any > > that apply anisotropic resolution cutoffs (or even whether this would > > be advisable). > > > > Cheers > > > > -- Ian > > > > On 27 January 2012 17:47, Jacob Keller <j-kell...@fsm.northwestern.edu> > wrote: > >> Dear Crystallographers, > >> > >> I cannot think why any of the various flavors of Rmerge/meas/pim > >> should be used as a data cutoff and not simply I/sigma--can somebody > >> make a good argument or point me to a good reference? My thinking is > >> that signal:noise of >2 is definitely still signal, no matter what the > >> R values are. Am I wrong? I was thinking also possibly the R value > >> cutoff was a historical accident/expedient from when one tried to > >> limit the amount of data in the face of limited computational > >> power--true? So perhaps now, when the computers are so much more > >> powerful, we have the luxury of including more weak data? > >> > >> JPK > >> > >> > >> -- > >> ******************************************* > >> Jacob Pearson Keller > >> Northwestern University > >> Medical Scientist Training Program > >> email: j-kell...@northwestern.edu > >> ******************************************* > -- *ARKA CHAKRABORTY* *CAS in Crystallography and Biophysics* *University of Madras* *Chennai,India*
