Western blotting or protein immunoblotting, was introduced at the end of the 1970s to enable the detection of specific proteins. 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. 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. 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. 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. Thus, in the context of hypothesis testing, normalisation by fixed point reduces false positives and increases false negatives. 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. We consider how these different strategies affect the coefficient of variation (CV) and the results of hypothesis testing with the normalised 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. 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. Western blot data are widely used in quantitative applications such as statistical testing and mathematical modelling.
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