Partition Quantitative Assessment (PQA): A Quantitative Methodology to Assess the Embedded Noise in Clustered Omics and Systems Biology Data

Identifying groups that share common features among datasets through clustering analysis is a typical problem in many fields of science, particularly post-omics and systems biology research. In respect this, quantifying how measure can cluster or organize intrinsic important since currently there no statistical evaluation ordered is, much noise embedded the resulting clustered vector. Much literature focuses on well algorithm orders data, with several measures regarding external internal validation; but score has been developed to quantify statistically an arranged vector posterior algorithm, i.e., due randomness. Here, we present quantitative methodology, based autocorrelation, order assess this problem.