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

TL;DR

This paper introduces two semi-parametric variants of the concordance index, the rCI and kCI, designed to improve association testing in noisy drug screening data, with better power demonstrated in simulations and real datasets.

Contribution

The paper presents novel semi-parametric modifications of the concordance index that incorporate noise distribution information for improved association testing.

Findings

rCI and kCI outperform the concordance index in simulation power.

Pearson correlation is most robust to measurement noise.

Proposed methods show some improvement on real pharmacogenomic data.

Abstract

dentifying 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. 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 compute p-values using adaptive permutation testing. We then evaluate the statistical power of these coefficients under simulation and compare with Pearson and Spearman correlation coefficients. Finally, we evaluate the various statistics in matching drugs across pharmacogenomic datasets. We observe that the rCI and kCI are better powered than the concordance index in simulation and show some improvement on real data. Surprisingly, we observe that the Pearson correlation was the most robust to measurement noise among the different metrics.