Information-Content-Informed Kendall-tau Correlation Methodology: Interpreting Missing Values as Useful Information.

ABSTRACT:

Background

Almost all correlation measures currently available are unable to directly handle missing values. Typically, missing values are either ignored completely by removing them or are imputed and used in the calculation of the correlation coefficient. In either case, the correlation value will be impacted based on a perspective that the missing data represents no useful information. However, missing values occur in real data sets for a variety of reasons. In omics data sets that are derived from analytical measurements, a major reason for missing values is that a specific measurable phenomenon falls below the detection limits of the analytical instrumentation (left-censored values). These missing data are not missing at random, but represent useful information by virtue of the

SUBMITTER: Flight RM 

PROVIDER: S-EPMC12330630 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature