# AlertGS: determining alerts for gene sets

## TL;DR

AlertGS is a new method that determines when gene sets become significantly enriched in gene expression experiments, helping researchers identify biologically meaningful responses.

## Contribution

The novelty lies in extending gene-wise alert estimation to gene sets using a Kolmogorov–Smirnoff type test statistic.

## Key findings

- Simulations show the method can identify true gene sets with signals, especially when their number is low.
- False positives are controlled through decorrelation approaches, with alerts typically underestimated rather than overestimated.
- The AlertGS methodology is reproducible via provided GitHub code.

## Abstract

A typical goal in gene expression studies is identifying certain gene sets enriched with significant genes. The measurement of many gene expression experiments for several concentrations or time points allows the modeling of the concentration/time–response relationship for each gene, and the subsequent estimation of a gene-wise alert. In this work, an approach is proposed to transfer the concept of alerts from single genes to gene sets, yielding a global significance statement and the respective concentration or time where the first enrichment of the gene set can be observed. The methodology is based on a Kolmogorov–Smirnoff type test statistic for each gene set.

Simulations show that a majority of these sets can be identified especially for lower numbers of true gene sets with a signal. The false positive rate can be controlled by subsequent decorrelation approaches. Overall, the true gene set-wise alerts are rarely overestimated and rather tend to be underestimated.

The code needed to reproduce the simulations and apply the AlertGS methodology is available at the GitHub repository: https://github.com/FKappenberg/AlertGS.