  The basis of several recent methods for drug repurposing is the key principle
that an efficacious drug will reverse the disease molecular 'signature' with
minimal side-effects. This principle was defined and popularized by the
influential 'connectivity map' study in 2006 regarding reversal relationships
between disease- and drug-induced gene expression profiles, quantified by a
disease-drug 'connectivity score.' Over the past 15 years, several studies have
proposed variations in calculating connectivity scores towards improving
accuracy and robustness in light of massive growth in reference drug profiles.
However, these variations have been formulated inconsistently using various
notations and terminologies even though they are based on a common set of
conceptual and statistical ideas. Therefore, we present a systematic
reconciliation of multiple disease-drug similarity metrics (ES, css, Sum,
Cosine, XSum, XCor, XSpe, XCos, EWCos) and connectivity scores (CS, RGES, NCS,
WCS, Tau, CSS, EMUDRA) by defining them using consistent notation and
terminology. In addition to providing clarity and deeper insights, this
coherent definition of connectivity scores and their relationships provides a
unified scheme that newer methods can adopt, enabling the computational
drug-development community to compare and investigate different approaches
easily. To facilitate the continuous and transparent integration of newer
methods, this article will be available as a live document
(https://jravilab.github.io/connectivity_scores) coupled with a GitHub
repository (https://github.com/jravilab/connectivity_scores) that any
researcher can build on and push changes to.
