Tips for reporting computational results
Reporting image analysis results
When reporting image analysis results, we suggest performing three specialized analyses:
Parameter sensitivity analysis: repeat image processing analysis with a wide range of all parameters. Ideally your conclusions should not significantly change with small changes in parameters (your results are robust). You may want to try a negative control (parameter values where you would expect to see no detected events/cells, like a maximum diameter of 0 pixels) and a positive control (like a minimum threshold of 0 a.u.).
Reproducibility analysis: repeat image processing analysis on several subsets of data that you should be able to draw the same conclusion from (e.g., in a video where you’re quantifying suspension velocity, 3 10-second clips of the same suspension in the same experiment).
Comparison to gold standard analysis (typically a manual analysis): this may not be possible for all analyses, but you may want to manually characterize metrics like number and area of cells from a small subset of your data to make sure iCLOTS is providing you with reasonable results. ImageJ is a good basic-use image visualization and measuring tool.
Reporting machine learning results
This is not an exhaustive resource for interpreting and reporting machine learning results. Your journal likely has more specific guidelines. For more information, please also see:
An excellent, accessibly written guide to machine learning for biologists and life scientists: Greener, J.G., Kandathil, S.M., Moffat, L. et al. A guide to machine learning for biologists. Nat Rev Mol Cell Biol 23, 40–55 (2022). https://doi.org/10.1038/s41580-021-00407-0