PT  - JOURNAL ARTICLE
AU  - Andrea Repele
AU  - Manu
TI  - Robust Normalization of Luciferase Reporter Data
AID  - 10.1101/673087
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 673087
4099  - http://biorxiv.org/content/early/2019/06/18/673087.short
4100  - http://biorxiv.org/content/early/2019/06/18/673087.full
AB  - Transient Luciferase reporter assays are widely used in the study of gene regulation and intracellular cell signaling. In order to control for sample-to-sample variation in luminescence arising from variability in transfection efficiency and other sources, an internal control reporter is co-transfected with the experimental reporter. The luminescence of the experimental reporter is normalized against the control by taking the ratio of the two. Here we show that this method of normalization, “ratiometric”, performs poorly when the transfection efficiency is low and leads to biased estimates of relative activity. We propose an alternative methodology based on linear regression that is much better suited for the normalization of reporter data, especially when transfection efficiency is low. We compare the ratiometric method against three regression methods on both simulated and empirical data. Our results suggest that robust errors-in-variables (REIV) regression performs the best in normalizing Luciferase reporter data. We have made the R code for Luciferase data normalization using REIV available on GitHub.PUERPU.1 Estrogen ReceptorOLSOrdinary least-squaresEIVErrors-in-variablesREIVRobust errors-in-variables