Adaptive Intersection Maximization (AIM) is a high-speed drift correction lgorithm for single molecule localization microscopy.
Citation: Hongqiang Ma, Maomao Chen, Phuong Nygyen, Yang Liu. Toward drift-free high-throughput nanoscopy through adaptive intersection maximization , Sci. Adv.10, eadm7765(2024). DOI:10.1126/sciadv.adm7765.
All the codes under \DME_RCC are from https://github.com/qnano/drift-estimation published in Jelmer Cnossen, Tao Ju Cui, Chirlmin Joo, and Carlas Smith, "Drift correction in localization microscopy using entropy minimization," Opt. Express 29, 27961-27974 (2021).
AIM requires only a standard computer. RCC and DME require a minimal of 32 GB RAM for the big datasets from large field of view system (e.g., 2048 x 2048).
The provided codes have been tested on MATLAB version 2020b to 2023a on Windows 10 Operating System.
Users can direacly download the codes and run the demo code on MATLAB. Users need to replace the file name when processing users' own datasets.
We provided four experimental datasets (Origami_PAINT, Microtublue_3d, Tissue_colon and CTCF_MCF10A_DRB_6h) and one simulated dataset (simulationSMLM) in MATLAB mat format available at Dryad. Please download these dataset and put them in the Data folder.
We provide four MATLAB codes as examples to demonstrate how to use AIM.
example_ExperimentalData.m: This code performs drift correction with AIM on 2D or 3D localization coordinates of experimental data. Sample experimental data are avaialble at Dryad.
example_code_2D.m : This code compares the performance of drift correction for AIM, RCC and DME using 2D localization coordinates for experimental dataset of DNA origami (Origami_PAINT.mat) or simulated data (simulationSMLM.mat) available at Dryad.
example_code_3D.m : This code compares the performance of drift correction for AIM, RCC and DME using 3D localization coordinates of experimental data of simulated data or or experimental data of microtubules (Microtublue_3d.mat) available at Dryad.
example_code_FigureS1.m: This code is used to reproduce Supplementary Figure S1, which shows drift tracking precision under a wide range of image sizes from 128×128 pixels to 2048×2048 pixels.
simulationSMLM.m: This code is used to generate the simulated SMLM dataset from DNA origami structures used in Figure 2 in the main text.
save_imSR.m: This MATLAB function is used to save the SMLM dataset into a tif image.
Load_ThunderSTORM.m: This code is used to provide a MATLAB function to read the localization dataset (csv files) from the commonly used ThunderSTORM software.