Alejandro1400
commented
3 days ago The merging algorithm in ThunderSTORM is of no use to us because it outputs just the number of molecules with how many times it was detected. Instead, what we want is to keep the same dataset but adding a column molecule_id according to the merging. Also, by the definition of ThunderSTORM, the intensity is calculated as the sum, which doesn't allow for a temporal profiling.
Therefore, the imlpementation of our own merging algorithm results important
STORM Processing
For the STORM processing we will be using ThunderSTORM from ImageJ. The documentation for it can be found in the next links:
When using ThunderSTORM the option for Multi-emitter needs to be selected, this helps us analyze as temporal resolution is of greater importance than spatial resolution in blinking statistics analysis. In the section detailing Duty Cycle of the next document it is detailed: Nikon STORM
Therefore, after using STORM, its output will retreive a resolution with overlapping cases resolved, which will help in identifying in higher precision a profiling of molecules over frames.
These results may go through multiple filters in ThunderSTORM options but it is suggested to only use Merging and Drift Correction. Density filter is not of interest because we want temporal resolution, Z-stage offset may not have utility as images analyzed for this project are in 2D-resolution.
Local Density Filtering Local Density filtering removes noise by discarding a localization event if the number of neighbors is below a user-specified treshold. (This doesn't interest us because that exact case of a molecule appearing just once is something we want to understand.
Removing Duplicates
Repeated localizations of single molecules can occur due to overlapping fitting using the multi -emitter analysis approach. Here, molecules with a mutual distance smaller than their localization uncertainty are grouped together, and in each group, only the molecule with the smallest localization uncertainty is kept.
Lateral drift correction
Since we are analyzing temporal resolution, drift correction is important for merging molecules and get their profile. In this scenario it would use Cross-correlation or Fiducial Markers solution, where through analyzing batches of frames, the repetition of similar events correlates to estimated fiducial markers for a set of frames