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XSCALE ISOCLUSTER: Difference between revisions
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For dataset analysis, the program uses the method of [https://dx.doi.org/10.1107/S1399004713025431 Brehm and Diederichs (2014) ''Acta Cryst'' '''D70''', 101-109] ([https://kops.uni-konstanz.de/bitstream/handle/123456789/26319/Brehm_263191.pdf?sequence=2&isAllowed=y PDF]) whose theoretical background is in [https://doi.org/10.1107/S2059798317000699 Diederichs (2017) ''Acta Cryst'' '''D73''', 286-293] (open access). This results in an arrangement of N datasets represented by N vectors in a low-dimensional space. Typically, the dimension of that space may be chosen as n=2 to 4, but may be higher if N is large. | For dataset analysis, the program uses the method of [https://dx.doi.org/10.1107/S1399004713025431 Brehm and Diederichs (2014) ''Acta Cryst'' '''D70''', 101-109] ([https://kops.uni-konstanz.de/bitstream/handle/123456789/26319/Brehm_263191.pdf?sequence=2&isAllowed=y PDF]) whose theoretical background is in [https://doi.org/10.1107/S2059798317000699 Diederichs (2017) ''Acta Cryst'' '''D73''', 286-293] (open access). This results in an arrangement of N datasets represented by N vectors in a low-dimensional space. Typically, the dimension of that space may be chosen as n=2 to 4, but may be higher if N is large. | ||
n=1 would be suitable if the datasets only differ in their random error. One more dimension is required for each additional systematic property which may vary between the datasets, e.g. n=2 is suitable if they only differ in their indexing mode (which then only should have two alternatives!), or in some other systematic property, like the length of | n=1 would be suitable if the datasets only differ in their random error (i.e. they are highly isomorphous). One more dimension is required for each additional systematic property which may vary between the datasets, e.g. n=2 is suitable if they only differ in their indexing mode (which then only should have two alternatives!), or in some other systematic property, like the length of a cell axis. Higher values of n (e.g. n=4) are appropriate if e.g. there are 4 indexing possibilities (which is the case in P3<sub>x</sub>), or more systematic ways in which the datasets may differ (like significant variations in the a, b and c axes), or conformational or compositional differences. In cases where datasets differ e.g. with respect to the composition or conformation of crystallized molecules, it is ''a priori'' unknown which value of n should be chosen, so several values need to be tried, and the results inspected (see [[Xscale_isocluster#Notes]]). | ||
An attempt is made to automatically identify clusters of datasets. The program writes files called XSCALE.1.INP with lines required for scaling the datasets of cluster 1, and similarly XSCALE.2.INP for cluster 2, and so on. Typically, one may want to create directories cluster1 cluster2 ..., and then establish symlinks (called XSCALE.INP) in these to the XSCALE.#.INP files. This enables separate scaling of each cluster. | An attempt is made to automatically identify clusters of datasets. The program writes files called XSCALE.1.INP with lines required for scaling the datasets of cluster 1, and similarly XSCALE.2.INP for cluster 2, and so on. Typically, one may want to create directories cluster1 cluster2 ..., and then establish symlinks (called XSCALE.INP) in these to the XSCALE.#.INP files. This enables separate scaling of each cluster. | ||