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XSCALE ISOCLUSTER: Difference between revisions
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[ftp://turn5.biologie.uni-konstanz.de/pub/xscale_isocluster_linux.bz2 xscale_isocluster] is a program that clusters datasets stored in a single unmerged reflection file as written by [[XSCALE]]. | [ftp://turn5.biologie.uni-konstanz.de/pub/xscale_isocluster_linux.bz2 xscale_isocluster] is a program that clusters datasets stored in a single unmerged reflection file as written by [[XSCALE]]. It implements the method of [https://doi.org/10.1107/S1399004713025431 Brehm and Diederichs (2014)] and theory of [https://doi.org/10.1107/S2059798317000699 Diederichs (2017)]. | ||
The help output (obtained by using the <code>-h</code> option) is | The help output (obtained by using the <code>-h</code> option) is | ||
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xscale_isocluster KD 2016-12-20. -h option shows options | xscale_isocluster KD 2016-12-20. -h option shows options | ||
Academic use only; no redistribution. Expires 2017-12-31 | Academic use only; no redistribution. Expires 2017-12-31 | ||
usage: xscale_isocluster -dmin <lowres> -dmax <highres> -nbin <nbin> -mode <1 or 2> -dim <dim> -clu <#> -cen <#,#,#,...> -<aiw> | usage: xscale_isocluster -dmin <lowres> -dmax <highres> -nbin <nbin> -mode <1 or 2> -dim <dim> -clu <#> -cen <#,#,#,...> -<aiw> XSCALE_FILE | ||
dmax, dmin (default from file) and nbin (default 10) have the usual meanings. | dmax, dmin (default from file) and nbin (default 10) have the usual meanings. | ||
mode can be 1 (equal volumes of resolution shells) or 2 (increasing volumes; default). | mode can be 1 (equal volumes of resolution shells) or 2 (increasing volumes; default). | ||
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-w: no weighting of intensities with their sigmas | -w: no weighting of intensities with their sigmas | ||
</pre> | </pre> | ||
== Usage == | |||
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 (see below). 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 the a 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 a, b and c axes). 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, and several values need to be tried, and the results inspected. | 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 (see below). 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 the a 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 a, b and c axes). 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, and several values need to be tried, and the results inspected. | ||
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 | 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 1 2 ..., and then establish symlinks (called XSCALE.INP) in these to the XSCALE.#.INP files. This enables separate scaling of each cluster. | ||
Each file XSCALE.x.INP enumerates the contributing INPUT_FILEs in the order of increasing angular distance. Each INPUT_FILE line is followed by a comment line. In this, the first two numbers (''new'' and ''old'') refer to the numbering of datasets in the resulting XSCALE.x.INP, versus that in the original XSCALE.INP (which was used to obtain XSCALE_FILE). Then, ''dist'' refers to arccosine of the angle (e.g. a value of 1.57 would mean 90 degrees) to the center of the cluster (the lower the better/closer), ''strength'' refers to vector length which is inversely proportional to the random noise in a data set, and ''cluster'', if negative, identifies a dataset that is outside the core of the cluster. To select good datasets and reject bad ones, the user may comment out INPUT_FILE lines which refer to datasets that are far away in angle or outside the core of the cluster. | |||
The clustering of datasets in the low-dimensional space uses the method of Rodriguez and Laio (2014) ''Science'' '''344''', 1492-1496. | == Notes == | ||
* For meaningful results, the number of known values (N*(N-1)/2 is the number of pairwise correlation coefficients) should be (preferrably much) higher than the number of unknowns (1+n*(N-1)). This means that one needs at least 5 datasets if dim=2, and at least 7 if dim=3. | |||
* The clustering of datasets in the low-dimensional space uses the method of Rodriguez and Laio (2014) ''Science'' '''344''', 1492-1496. | |||
* Limitation: the program does not work if the XSCALE.INP that produced the XSCALE_FILE has more than one OUTPUT_FILE. This is because the dataset numbers in XSCALE_FILE then do not start from 1. Workaround: do several XSCALE runs, one for each OUTPUT_FILE. | |||