Review: Don’t Be Fooled by Randomness: Valid p-Values for Single Molecule Microscopy
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