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PLoS One. 2014 Jan 27;9(1):e87293. doi: 10.1371/journal.pone.0087293. eCollection 2014.

Evaluating strategies to normalise biological replicates of Western blot data.

Author information

1
Systems Biology Ireland, University College Dublin, Dublin, Republic of Ireland.
2
Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America.
3
Systems Biology Ireland, University College Dublin, Dublin, Republic of Ireland ; Conway Institute of Biomolecular & Biomedical Research, University College Dublin, Dublin, Republic of Ireland ; School of Medicine and Medical Science, University College Dublin, Dublin, Republic of Ireland.

Abstract

Western blot data are widely used in quantitative applications such as statistical testing and mathematical modelling. To ensure accurate quantitation and comparability between experiments, Western blot replicates must be normalised, but it is unclear how the available methods affect statistical properties of the data. Here we evaluate three commonly used normalisation strategies: (i) by fixed normalisation point or control; (ii) by sum of all data points in a replicate; and (iii) by optimal alignment of the replicates. We consider how these different strategies affect the coefficient of variation (CV) and the results of hypothesis testing with the normalised data. Normalisation by fixed point tends to increase the mean CV of normalised data in a manner that naturally depends on the choice of the normalisation point. Thus, in the context of hypothesis testing, normalisation by fixed point reduces false positives and increases false negatives. Analysis of published experimental data shows that choosing normalisation points with low quantified intensities results in a high normalised data CV and should thus be avoided. Normalisation by sum or by optimal alignment redistributes the raw data uncertainty in a mean-dependent manner, reducing the CV of high intensity points and increasing the CV of low intensity points. This causes the effect of normalisations by sum or optimal alignment on hypothesis testing to depend on the mean of the data tested; for high intensity points, false positives are increased and false negatives are decreased, while for low intensity points, false positives are decreased and false negatives are increased. These results will aid users of Western blotting to choose a suitable normalisation strategy and also understand the implications of this normalisation for subsequent hypothesis testing.

PMID:
24475266
PMCID:
PMC3903630
DOI:
10.1371/journal.pone.0087293
[Indexed for MEDLINE]
Free PMC Article

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