Enable user to filter out points based on their properties and save the output for further analysis in workflow.
The points in dSTORM acquisitions have the properties shown in the right-hand panel. Sliders can be used to filter out points by choosing ranges for these properties.
The checkbox in the top left corner of each slider is used to determine whether that property will be used to filter the points. The number of properties displayed depends on those present in the data.
The localisation algorithm runs a statistical fitting procedure on each localisation, producing a number of properties. Those available through the filtering tool are summarised here:
frame index: The frame in which the localisation was detected.
background [photons/μm^2]: density of photons from the background at the spot.
photon count: photons at the spot.
sigma [nm]: the standard deviation of the fitted point spread function at the spot. This filter acts at the same time on both the nimOS parameters Sigma X and Sigma Y.
p-value: smaller values correspond to a more reliable localisations. Normally used to filter out points with high p-value.
localisation precision [nm]: Estimation of the spatial uncertainty of the localisations. This filter acts at the same time on both the nimOS parameters Localisation precision X and Localisation precision Y, which are the Cramér–Rao lower bounds for the X and Y coordinates of the localisation.
z [nm] : The z-location of each spot. Only available for 3D datasets.
If a dataset has multiple channels, then the set of channels to be filtered can be selected in the visualisation panel as shown below where two channels have been selected (highlighted borders). Individual channels can also be selected separately to apply different filters to each.
Once the filters have been selected, the current parameter settings can then be saved by clicking on the save button on the top right corner of the right hand side panel. These settings can be saved to the cloud (on CODI) or on the local machine. If saved on the local machine, a JSON file is downloaded showing the parameters that have been changed and the corresponding ranges. An example of a downloaded file is shown below. This same file can be loaded into the CODI filtering tool using the load button and that will automatically apply the filters to the image.