Reducing the Standard Deviation in Multiple-Assay Experiments Where the Variation Matters but the Absolute Value Does Not

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 differen...

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Published inPloS one Vol. 8; no. 10; p. e78205
Main Authors Echenique-Robba, Pablo, Nelo-Bazán, María Alejandra, Carrodeguas, José A.
Format Journal Article
LanguageEnglish
Published United States Public Library of Science 30.10.2013
Public Library of Science (PLoS)
Subjects
Assaying
Biological Assay - methods
Deoxyribonucleic acid
DNA
Experiments
Fourier transforms
Gene Regulatory Networks - genetics
Humans
Laboratories
Mathematical analysis
Membrane Proteins - genetics
Mitochondrial Proteins - genetics
Parameter identification
Researchers
Standard deviation
Statistical analysis
Statistical significance
Studies
Vectors (mathematics)
Spain
Online AccessGet full text
ISSN1932-6203
1932-6203
DOI10.1371/journal.pone.0078205

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Summary: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.
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Competing Interests: The authors have declared that no competing interests exist.
Conceived and designed the experiments: MANB JAC. Performed the experiments: MANB. Analyzed the data: PER. Contributed reagents/materials/analysis tools: MANB JAC. Wrote the paper: PER JAC.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0078205