Evaluation of statistical approaches for association testing in noisy drug screening data.

ABSTRACT:

Background

Identifying associations among biological variables is a major challenge in modern quantitative biological research, particularly given the systemic and statistical noise endemic to biological systems. Drug sensitivity data has proven to be a particularly challenging field for identifying associations to inform patient treatment.

Results

To address this, we introduce two semi-parametric variations on the commonly used concordance index: the robust concordance index and the kernelized concordance index (rCI, kCI), which incorporate measurements about the noise distribution from the data. We demonstrate that common statistical tests applied to the concordance index and its variations fail to control for false positives, and introduce efficient implementations to com

SUBMITTER: Smirnov P 

PROVIDER: S-EPMC9118710 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature