The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/19786735

Kalisvaart, Dylan; Cnossen, Jelmer; Hung, Shih-Te; Stallinga, S. (Sjoerd); Verhaegen, Michel et. al. (2022): Data underlying the publication: Precision in iterative modulation enhanced single-molecule localization microscopy. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/19786735.v1

Dataset

• Optical Physics

Cramer-Rao Lower Bound, Iterative Localization Microscopy, Modulation Enhanced Localization Microscopy, Precision, Single Molecule Localization, Single Molecule Localization Microscopy, Super-resolution microscopy, Van Trees Inequality

CC BY 4.0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Dylan Kalisvaart , Jelmer Cnossen , Shih-Te Hung , S. (Sjoerd) Stallinga , Michel Verhaegen , Carlas Smith

General information

This simulation data belongs to the article:

Precision in iterative modulation enhanced single-molecule localization microscopy

DOI: _{https://doi.org/10.1016/j.bpj.2022.05.027}

This information is also available in README.txt, included in this repository.

Data availability

The simulation code underlying the article can be found at:

_{https://github.com/qnano/iterative-localization}

Data description

The simulation data in this repository, consisting of .npy files, is contained in several .zip files. The title of the .zip files describes which quantity is contained in it. 

Specifically:

• CRLBxI.zip: Cramér-Rao lower bound, computed over all iterations in the fixed photon budget scenario.
• CRLBxM.zip: Approximation of the Cramér-Rao lower bound in the fixed photon budget scenario, from Balzarotti et al. (2017). Nanometer resolution imaging and tracking of fluorescent molecules with minimal photon fluxes. Science 355:606–612. 
• FT-I.zip: Fisher information matrix in the fixed imaging time and fixed illumination intensity scenario.
• FT-J.zip: Bayesian information matrix in the fixed imaging time and fixed illumination intensity scenario.
• FT-JT.zip: Analytical approximation of the Bayesian information matrix in the fixed imaging time and fixed illumination intensity scenario.
• FT-mod.zip: Fraction of the photon budget used in each iteration in the fixed imaging time and fixed illumination intensity scenario.
• FT-modT.zip: Fraction of the photon budget used in each iteration assuming the analytical approximation of the VTI, in the fixed imaging time and fixed illumination intensity scenario.
• FT-smp.zip: Regions of interest in the fixed imaging time and fixed illumination intensity scenario.
• FT-theta.zip: Ground truth estimand values after subpixel randomization in the fixed imaging time and fixed illumination intensity scenario.
• FT-thetaMAP.zip: Maximum a posteriori (MAP) estimated parameter values in the fixed imaging time and fixed illumination intensity scenario.
• FT-VTIx.zip: Van Trees inequality (VTI) in the fixed imaging time and fixed illumination intensity scenario.
• FT-VTIxT.zip: Analytical approximation of the VTI in the fixed imaging time and fixed illumination intensity scenario.
• I.zip: Fisher information matrix in the fixed photon budget scenario.
• J.zip: Bayesian information matrix in the fixed photon budget scenario.
• JT.zip: Analytical approximation of the Bayesian information matrix in the fixed photon budget scenario.
• smp.zip: Regions of interest in the fixed photon budget scenario.
• theta.zip: Ground truth estimand values after subpixel randomization in the fixed photon budget scenario.
• thetaMAP.zip: Maximum a posteriori (MAP) estimated parameter values in the fixed photon budget scenario.
• VTIx.zip: Van Trees inequality (VTI) in the fixed photon budget scenario.
• VTIxT.zip: Analytical approximation of the VTI in the fixed photon budget scenario.

If you want to access all the data, including (raw) quantities that are not directly used to compute the results shown in the article, download SimData.zip. SimData.zip contains all data from individual .zip-files, combined for your convenience. Alternatively, you can download all individual .zip-files.

Within the .zip folders, the title of the data contains the simulation parameters it was obtained from. The data titles follow the following format (where the brackets [...] indicate variables):

[prefix]-iter-[parameter itermax]-thetaI-[parameter theta_I]-m-[parameter 100*m]-alpha-[parameter 100*alpha]-thetab-[parameter theta_b].npy

The variables are defined as follows:

• [prefix]: Quantity name, as in the .zip title (e.g. CRLBxI, CRLBxM).
• [parameter itermax]: Parameter itermax, i.e. the amount of imeSMLM iterations, that was used in simulation.
• [parameter theta_I]: Parameter theta_I, i.e. the expected signal photon intensity over all iterations under maximum illumination (photons), that was used in simulation.
• [parameter 100*m]: Parameter m, i.e. the modulation contrast of the illumination patterns, that was used in simulation. Values were multiplied by 100 in the title only, to avoid commas in the title.
• [parameter 100*alpha]: Parameter alpha, i.e. the aggressiveness parameter/the amount of standard deviations between the emitter estimate and a pattern minimum, that was used in simulation. Values were multiplied by 100 in the title only, to avoid commas in the title.
• [parameter theta_b]: Parameter theta_b, i.e. the expected background photon count per pixel, assumed to be uniform over the region of interest (photons/pixel), that was used in simulation.

For example, the file VTIx-iter-3-thetaI-2000-m-95-alpha-300-thetab-8.npy contains the simulated VTI values of an imeSMLM experiment in the fixed photon budget scenario with 3 iterations, an expected photon budget of 2000 photons, a modulation contrast of 0.95, an aggressiveness parameter of 3 and an expected background of 8 photons/pixel.

The reference table Datasets.xlsx describes which simulation parameters are used in each simulation. It also contains an overview of which datasets are used in each of the figures in the article.

Using existing data

This section describes how to use the simulation data in this repository to reproduce results.

1. Download Datasets.xlsx and place it in the directory "/iterative_localization/". This file describes which simulation parameters are used in each simulation. It also contains an overview of which datasets are used in each of the figures in the article.

2. Download the appropriate (raw and processed) simulation data. Specifically:

• If you intend to only reproduce the results and want to use the minimum amount of data to do so, download the following data and extract it to the directory "/iterative_localization/SimData/":

1. CRLBxI.zip
2. CRLBxM.zip
3. FT-J.zip
4. FT-mod.zip
5. FT-modT.zip
6. FT-smp.zip
7. FT-theta.zip
8. FT-thetaMAP.zip
9. FT-VTIx.zip
10. FT-VTIxT.zip
11. J.zip
12. smp.zip
13. theta.zip
14. thetaMAP.zip
15. VTIx.zip
16. VTIxT.zip

• If you want to access all the data, including (raw) quantities that are not directly used to compute the results shown in the article, download SimData.zip and extract it to the directory "/iterative_localization/SimData/". SimData.zip contains all data from individual .zip-files, combined for your convenience. Alternatively, you can download all individual .zip-files and extract them to the directory "/iterative_localization/SimData/".

• 2022-06-16 first online, published, posted

4TU.ResearchData

Simulated data in zipped .npy files; Reference table for datasets in .xlsx

Precision in iterative modulation enhanced single-molecule localization microscopy

• NWO START-UP project no. 740.018.015
• NWO Veni project no. 16761

TU Delft, Faculty of Mechanical, Maritime and Materials Engineering, Delft Center for Systems and Control;

TU Delft, Faculty of Applied Sciences, Department of Imaging Physics