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→Notes: xds_nonisomorphism
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* The clustering of datasets in a low-dimensional space uses the method of Rodriguez and Laio (2014) ''Science'' '''344''', 1492-1496. | * The clustering of datasets in a 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. | * 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. | ||
* A different but related program is [ftp://turn5.biologie.uni-konstanz.de/pub/xds_nonisomorphism.rhel6.bz2 xds_nonisomorphism]. This determines the lengths of the vectors from the CC<sub>1/2</sub> of the data sets, and the angles between vectors from the correlation coefficients between data sets. It requires data sets with internal multiplicity, and mutual overlap. Angles are expressed in degrees. Less than 10° should be considered good isomorphism, 90° means completely unrelated (i.e. non-isomorphous) datasets (theoretically, higher angles are also possible if data sets are anti-correlated). xds_nonisomorphism prints a short help text if the -h option is used. | |||