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Data Mining
Data Mining tries to find patterns in the data which may be "hidden" or
not really obvious.
Events (rearrangements) may occur more often in special combinations
than expected, or less often (dependence
between events). Some rearrangements may occur preferentially alone
(early events), while others are encountered with complex karyotypes only
("Aberration Count Distribution").
Also, complex karyotypes arise along pathways
of karyotype evolution. Finally, similar karyotypes can be clustered
into groups (not yet implemented).
With data mining, every karyotype is regarded a case of its own. I.e.
a patient with two investigations and five karyotypes is regarded as five
cases. This point of view is especially useful when calculating the evolution
pathway.
Mining Parameters
The Mining Parameters can be edited from the Main Window (menu Edit - Mining
Parameters) for all subsequent mining tasks. If the parameters are to be
edited for a single mining window, they can be edited there from the local
Edit menu; "Edit - General Parameters" will open the edit form in the same
way as from the main window, the "Edit - XY Parameters" opens the form
at a specific tab, but the other tabs can be accessed too.
General Parameters
Input
Mining can use either ISCN (banding) data as an input or CGH data. If cases
contain both types of data, only the selected type will be used. Cases
which do not have the selected type of input data will be ignored.
Method
Standard elements mean textual descriptions of rearrangements as denoted
in the ISCN or CGH notation. The data can be transformed into the SCCN
or Cytoband notation prior to actual mining.
Resolution
If data are transformed, a banding resolution has to be selected. Since
a resolution of 400 bands per haploid set (bphs) is quite rare in tumour
cytogenetics, a lower resolution is advisable; the resolution of chromosomal
arms is recommended.
Standard Elements cannot be standardized to a resolution.
Other Parameters
The meaning of other parameters is explained in the "edit" sections of
the respective analysis, e.g. evolution
tree, dependence network,
event distribution.