title: Reducing the standard deviation in multiple-assay experiments where the variation matters but the absolute value does not.
abstract: When the value of a quantity x for a number of systems (cells, molecules, people, chunks of metal, DNA vectors, so on) is measured and the aim is to replicate the whole set again for different trials or assays, despite the efforts for a near-equal design, scientists might often obtain quite different measurements. As a consequence, some systems' averages present standard deviations that are too large to render statistically significant results. This work presents a novel correction method of a very low mathematical and numerical complexity that can reduce the standard deviation of such results and increase their statistical significance. Two conditions are to be met: the inter-system variations of x matter while its absolute value does not, and a similar tendency in the values of x must be present in the different assays (or in other words, the results corresponding to different assays must present a high linear correlation). We demonstrate the improvements this method offers with a cell biology experiment, but it can definitely be applied to any problem that conforms to the described structure and requirements and in any quantitative scientific field that deals with data subject to uncertainty.
authors.affiliation: Instituto de Química Física Rocasolano, Consejo Superior de Investigaciones Científicas, Madrid, Spain ; Instituto de Biocomputación y Física de Sistemas Complejos, Universidad de Zaragoza, Zaragoza, Spain ; Zaragoza Scientific Center for Advanced Modeling, Universidad de Zaragoza, Zaragoza, Spain ; Departamento de Fsica Teórica, Universidad de Zaragoza, Zaragoza, Spain ; Unidad Asociada IQFR-BIFI, Madrid-Zaragoza, Spain.
authors.forename: Pablo
authors.initials: P
authors.lastname: Echenique-Robba
authors.forename: María Alejandra
authors.initials: MA
authors.lastname: Nelo-Bazán
authors.forename: José A
authors.initials: JA
authors.lastname: Carrodeguas
mesh_terms: D001681:Biological Assay / Q000379:methods*; D053263:Gene Regulatory Networks / Q000235:genetics; D006801:Humans; D008565:Membrane Proteins / Q000235:genetics; D024101:Mitochondrial Proteins / Q000235:genetics
chemical_list: C402222:MTCH1 protein, human; D008565:Membrane Proteins; D024101:Mitochondrial Proteins