  In this work, we present an approach for evaluating segmentation strategies
and solving the biological problem of creating robust interpretable maps of
biological data by employing wards agglomerative hierarchical clustering
applied to coexpression coordinates to deduce a faithful representation of the
input.
  We adopt and quantify two analyte-centric metrics named happiness and
immersiveness, one for describing the suitability of a single analyte
concerning the segmentation as well as a second metric for describing how well
the segmentation catches the underlying data variation. We show that these two
functions drive aggregation and segregation of segmentation respectively and
can produce trustworthy segmentation solutions. We discover that the
immersiveness metric exhibits higher-order phase transition properties in its
derivative to cluster numbers.
  Finally, we find that the cluster representations and label annotations, in
the case with clusters of high immersiveness, correspond to compositionally
inferred labels with the highest specificity. The interconnectedness mirrors
the potential relationships between cluster representations, label annotations,
and inferred labels, emphasizing the intricate nature of biology and the
representation of the specific expressions of gene products.
