Magdalena C Schneider and Gerhard J Schütz  describe how significance testing provides an important and valid addendum to the toolbox of quantitative (single molecule) biology. For two selected applications of single molecule microscopy –  Nanoclustering in single molecule localization microscopy (SMLM) and single molecule tracking – they introduce how a single overall p-value for data pooled from multiple cells or experiments can be obtained.

In this manuscript, the authors provide a guideline how to use p-values for the analysis of single molecule microscopy data. In particular, we address the following questions:

• What is the probabilistic basis of the significance level α and the p-value?

• How can one handle situations in which the distribution of the test statistic under the null hypothesis is not known analytically?

• How can multiple experimental outcomes be combined into one global p-value?

• How can one account for correlated data in significance testing?

Reference:  Front. Bioinform., 04 March 2022
Sec. Computational BioImaging
Volume 2 – 2022 | https://doi.org/10.3389/fbinf.2022.811053