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Identifying groups that share common features among datasets throughclustering analysis is a typical problem in many fields of science,particularly in post-omics and systems biology research. In respect of this,quantifying how a measure can cluster or organize intrinsic groups is importantsince currently there is no statistical evaluation of how ordered is, or howmuch noise is embedded in the resulting clustered vector. Many of theliterature focuses on how well the clustering algorithm orders the data, withseveral measures regarding external and internal statistical measures; but nonemeasure has been developed to statistically quantify the noise in an arrangedvector posterior a clustering algorithm, i.e., how much of the clustering isdue to randomness. Here, we present a quantitative methodology, based onautocorrelation, to assess this problem.

Partition Quantitative Assessment (PQA): A quantitative methodology to assess the embedded noise in clustered omics and systems biology data