GitHub

References

The primary literature for the methods SMLMAnalysis orchestrates, grouped by step. Each step page repeats its own reference and links to the backing package's documentation for the algorithm details.

Detection & fitting

  • Detection (SMLMBoxer). F. Huang, T. M. P. Hartwich, F. E. Rivera-Molina, et al. "Video-rate nanoscopy using sCMOS camera-specific single-molecule localization algorithms." Nature Methods 10, 653–658 (2013). doi:10.1038/nmeth.2488
  • MLE fitting (GaussMLE). C. S. Smith, N. Joseph, B. Rieger, K. A. Lidke. "Fast, single-molecule localization that achieves theoretically minimum uncertainty." Nature Methods 7, 373–375 (2010). doi:10.1038/nmeth.1449
  • Localization precision / CRLB. The reported σ are the exact CRLB from the MLE Fisher information — Smith et al. 2010 (above), extended to per-pixel sCMOS noise by Huang et al. 2013 (above) — not an analytical approximation.
  • sCMOS noise model. Huang et al. 2013 (above).
  • Multi-emitter fitting & goodness-of-fit (pvalue). F. Huang, S. L. Schwartz, J. M. Byars, K. A. Lidke. "Simultaneous multiple-emitter fitting for single molecule super-resolution imaging." Biomedical Optics Express 2, 1377–1393 (2011). doi:10.1364/BOE.2.001377

Frame connection

  • D. J. Schodt, K. A. Lidke. "Spatiotemporal Clustering of Repeated Super-Resolution Localizations via Linear Assignment Problem." Frontiers in Bioinformatics 1, 724325 (2021). doi:10.3389/fbinf.2021.724325

Drift correction

  • J. Cnossen, T. J. Cui, C. Joo, C. Smith. "Drift correction in localization microscopy using entropy minimization." Optics Express 29, 27961–27974 (2021). doi:10.1364/OE.426620
  • M. J. Wester, D. J. Schodt, H. Mazloom-Farsibaf, M. Fazel, S. Pallikkuth, K. A. Lidke. "Robust, fiducial-free drift correction for super-resolution imaging." Scientific Reports 11, 23672 (2021). doi:10.1038/s41598-021-02850-7

Bayesian grouping

  • M. Fazel, et al. "High-Precision Estimation of Emitter Positions using Bayesian Grouping of Localizations." Nature Communications 13, 7152 (2022). doi:10.1038/s41467-022-34894-2

Clustering

  • DBSCAN. M. Ester, H.-P. Kriegel, J. Sander, X. Xu. "A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise." Proc. 2nd Int. Conf. on Knowledge Discovery and Data Mining (KDD-96), 226–231 (1996).
  • Voronoi / SR-Tesseler. F. Levet, et al. "SR-Tesseler: a method to segment and quantify localization-based super-resolution microscopy data." Nature Methods 12, 1065–1071 (2015). doi:10.1038/nmeth.3579

See the SMLMClustering documentation for the full set of backends and their individual references.

Cross-correlation (multi-channel)

  • Pair correlation, theory. B. D. Ripley. "Modelling spatial patterns." Journal of the Royal Statistical Society B 39, 172–212 (1977).
  • Pair correlation in SMLM. P. Sengupta, T. Jovanovic-Talisman, D. Skoko, et al. "Probing protein heterogeneity in the plasma membrane using PALM and pair correlation analysis." Nature Methods 8, 969–975 (2011). doi:10.1038/nmeth.1704

Methods native to SMLMAnalysis

The Quality Filter, Intensity Filter, and Density Filter steps are implemented in SMLMAnalysis. Their methods (Poisson upper-tail multi-emitter rejection against an estimated excitation field; neighbor-count density filtering with automatic threshold selection) are described in full on their step pages.

How to cite

If you use SMLMAnalysis in your research, please cite the package together with the primary references for the specific methods your pipeline used (the steps above). A package citation entry (CITATION.bib) will accompany the registered release.