Rscreenorm: normalization of CRISPR and siRNA screen data for more reproducible hit selection

Bachas, Costa; Hodzic, Jasmina; van der Mijn, Johannes C; Stoepker, Chantal; Verheul, Henk M W; Wolthuis, Rob M F; Felley-Bosco, Emanuela; van Wieringen, Wessel N; van Beusechem, Victor W; Brakenhoff, Ruud H; de Menezes, Renée X

doi:10.5167/uzh-165108

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Bachas, C., Hodzic, J., van der Mijn, J. C., Stoepker, C., Verheul, H. M. W., Wolthuis, R. M. F., Felley-Bosco, E., van Wieringen, W. N., van Beusechem, V. W., Brakenhoff, R. H., & de Menezes, R. X. (2018). Rscreenorm: normalization of CRISPR and siRNA screen data for more reproducible hit selection. BMC Bioinformatics, 19, 301. https://doi.org/10.1186/s12859-018-2306-z

Abstract

BACKGROUND Reproducibility of hits from independent CRISPR or siRNA screens is poor. This is partly due to data normalization primarily addressing technical variability within independent screens, and not the technical differences between them.

RESULTS We present "rscreenorm", a method that standardizes the functional data ranges between screens using assay controls, and subsequently performs a piecewise-linear normalization to make data distributions across all screens comparable. In simulation studies, rscreenorm reduces false posi