Metabolomics - CERM

Metabolomics - CERM Metabolomics - CERM

cerm.unifi.it
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19.06.2013 Views

PCA Details/Advice In some cases PCA will not succeed in identifying any clear clusters or obvious groupings no matter how many components are used. If this is the case, it is wise to accept the result and assume that the presumptive classes or groups cannot be distinguished with PCA As a general rule, if a PCA analysis fails to achieve even a modest separation of classes, then it is probably better to use other statistical techniques to try to separate them

SIMCA Supervised learning method based on PCA Construct a seperate PCA model for each known class of observations PCA models used to assign the class belonging to observations of unknown class origin Recommended for use in one class case or for classification if no interpretation is needed CLASS SPECIFIC STUDIES One-class problem: Only disease observations define a class; control samples are too heterogeneous, for example, due to other variations caused by diseases, gender, age, diet, lifestyle, etc. Two-class problem: Disease and control observations define two seperate classes

PCA Details/Advice<br />

In some cases PCA will not succeed in<br />

identifying any clear clusters or obvious<br />

groupings no matter how many components<br />

are used. If this is the case, it is wise to<br />

accept the result and assume that the<br />

presumptive classes or groups cannot be<br />

distinguished with PCA<br />

As a general rule, if a PCA analysis fails to<br />

achieve even a modest separation of classes,<br />

then it is probably better to use other<br />

statistical techniques to try to separate them

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