Calculate for each track, using its starting frame, what is the relative density of tracks. We use a search radius to find how many tracks in the starting frame are neighbours of the track. The number of neighbours is normalised to the search circle, so that a track in the corner of the image with 2 neighbours has a density of 8. Code for calculating search area (intersection between search circle and the image border) is taken from https://petrelharp.github.io/circle_rectangle_intersection/circle_rectangle_intersection.html
calculateTrackDensity(dataList, radius = 1)list of a data frame (must include at a minimum - trace (track ID), x, y and frame (in real coords)) and a calibration data frame
numeric variable for search radius (in spatial units of the data)
data frame
xmlPath <- system.file("extdata", "ExampleTrackMateData.xml", package="TrackMateR")
tmObj <- readTrackMateXML(XMLpath = xmlPath)
#> Units are: 1 pixel and 0.07002736 s
#> Spatial units are in pixels - consider transforming to real units
#> Collecting spot data. Using 4 cores
#> Matching track data...
#> Calculating distances...
tmObj <- correctTrackMateData(dataList = tmObj, xyscalar = 0.04)
#> Correcting XY scale.
tdDF <- calculateTrackDensity(dataList = tmObj, radius = 2)