Quantitative analysis and visualization of spatial points

1. A computer implemented method for analyzing spatial point patterns in a region comprising:

generating a real data point set with a microscope;

wherein the real point set comprises a distribution of cell events;

receiving the real data point set;

simulating at least one point set within the region using a point process;

dividing the region into a plurality of elements;

determining a first real data score for each element of the plurality of elements by weighting the real data point set within a domain of a predetermined kernel; and

determining a second score for each element by weighting the at least one simulated point set within the domain of the predetermined kernel;

comparing, for each element of the plurality of elements, the first real data score to the second score;

displaying the results of the comparison on a display screen.

2. The computer implemented method of claim 1 , wherein the predetermined kernel is one of:

a continuous kernel; and

a discrete kernel.

3. The computer implemented method of claim 2 , wherein the domain of the kernel is isotropic having a radius greater than the radius of each of the plurality of elements.

4. The computer implemented method of claim 3 , wherein weighting the point set comprises counting the number of points within the domain of the predetermined kernel centered on the element.

5. The computer implemented method of claim 1 , wherein comparing the first real data score to the second score comprises one of:

computing confidence intervals for at least one confidence level having a predetermined statistical significance; and

computing p-values and comparing to a predetermined statistical significance.

6. The computer implemented method of claim 5 further comprising visualizing at least one of the plurality of elements having a higher score relative to the corresponding confidence intervals as belonging to a cluster and at least one of the plurality of elements having a lower score relative to the corresponding confidence intervals as belonging to an exclusion zone.

7. The computer implemented method of claim 6 , wherein the plurality of elements correspond to a plurality of pixels to be visualized.

8. The computer implemented method of claim 6 further comprising:

varying at least one parameter related to one of:

the region division;

the simulation;

the first real data score determination;

the second score determination; and

repeating determining a first score, determining a second score, and comparing the first score to the second score to optimize the statistical significance in a visualization of one of:

a cluster; and

an exclusion zone.

9. The computer implemented method of claim 1 , wherein the predetermined kernel has a domain comprising a Gaussian shape around a point within the element.

10. The computer implemented method of claim 1 , wherein determining a first score for each element of the plurality of elements by weighting the point set within a domain of a predetermined kernel comprises using one of:

a top hat weighting function;

a bell-shaped Gaussian weighting function;

a conical weighting function; and

a parabolic weighting function.

11. The computer implemented method of claim 1 , wherein the point process is a Poisson point process.

12. The computer implemented method of claim 1 , wherein the point process simulating at least one point set within the region includes a distribution based on one of:

a covariate dataset;

a dataset having a non-uniform distribution; and

a marked dataset.

13. The computer implemented method of claim 1 , wherein each of the plurality of elements includes multiple dimensions.

14. The computer implemented method of claim 1 , wherein simulating at least one point set comprises using random samples drawn from a predetermined distribution.

15. The computer implemented method of claim 1 , wherein the point set comprises a distribution of exocytosis events in cells.

16. The computer implemented method of claim 1 further comprising testing the point set for a goodness-of-fit of a non-random distribution.

17. The computer implemented method of claim 1 , wherein the microscope comprises a total internal reflection fluorescence (TIRF) microscope.

18. A computer program product including a non-transitory computer-storage medium having instructions stored thereon for processing data information, such that the instructions, when carried out by a processing device, cause the processing device to perform operations of:

generating a real data point set with a microscope;

wherein the real point set comprises a distribution of cell events;

receiving the real data point set;

simulating at least one point set within the region using a point process;

dividing the region into a plurality of elements;

determining a first score for each element of the plurality of elements by weighting the point set within a domain of a predetermined kernel;

determining a second score for each element by weighting the at least one simulated point set within the domain of the predetermined kernel;

comparing, for each element, the first score to the second score;

computing confidence intervals for at least one confidence level having a predetermined statistical significance; and

visualizing at least one of the plurality of elements having a higher score relative to one of the confidence intervals as belonging to a cluster and at least one of the plurality of elements having a lower score relative to a different confidence interval as belonging to an exclusion zone;

wherein visualizing at least one of the plurality of elements having a higher score includes displaying the results of the comparison on a display screen.

19. A system for analyzing spatial point patterns in a region comprising:

a host processor coupled to memory, the memory storing instructions that when executed on the host processor cause the system to perform:

generating a real data point set with a microscope system;

wherein the real point set comprises a distribution of cell events;

receiving the real data point set;

simulating at least one point set within the region using a point process;

dividing the region into a plurality of elements;

determining a first score for each element of the plurality of elements by weighting the point set within a domain of a predetermined kernel;

determining a second score for each element by weighting the at least one simulated point set within the domain of the predetermined kernel;

comparing, for each element, the first score to the second score;

computing confidence intervals for at least one confidence level having a predetermined statistical significance; and

visualizing at least one of the plurality of elements having a higher score relative to one of the confidence intervals as belonging to a cluster and at least one of the plurality of elements having a lower score relative to a different confidence interval as belonging to an exclusion zone;

wherein visualizing at least one of the plurality of elements having a higher score includes displaying the results of the comparison on a display screen.