ZetaSuite, A Computational Method for Analyzing Multi-dimensional High-throughput Data, Reveals Genes with Opposite Roles in Cancer Dependency

Abstract

The rapid advance of high-throughput technologies has enabled the generation of two-dimensional or even multi-dimensional high-throughput data, e.g., genome-wide siRNA screen (1st dimension) for multiple changes in gene expression (2nd dimension) in many different cell types or tissues or under different experimental conditions (3rd dimension). We show that the simple Z-based statistic and derivatives are no longer suitable for analyzing such data because of the accumulation of experimental noise and/or off-target effects. Here, we introduce ZetaSuite, a statistical package designed to score and rank hits from two-dimensional screens, construct regulatory networks based on response similarities, and eliminate off-targets. Applying this method to two large cancer dependency screen datasets, we identify not only genes critical for cell fitness, but also those required for constraining cell proliferation. Strikingly, most of those cancer constraining genes function in DNA replication/repair checkpoint, suggesting that cancer cells also need to protect their genomes for long-term survival.