Wolfram Research

Sample Data: Beta Cells

Source Notebook

Locations of retinal ganglia cells annotated with on/off and area (in square microns) marks

Details

Locations of retinal ganglia cells in the observation region Rectangle[{28.08, 778.08}, {16.2, 1007.02}] microns, annotated with on/off and area (in square microns) marks.

Examples

Basic Examples (1) 

In[1]:=
ResourceData[\!\(\*
TagBox["\"\<Sample Data: Beta Cells\>\"",
#& ,
BoxID -> "ResourceTag-Sample Data: Beta Cells-Input",
AutoDelete->True]\), "Data"]
Out[1]=

Summary of the spatial point data:

In[2]:=
ResourceData[\!\(\*
TagBox["\"\<Sample Data: Beta Cells\>\"",
#& ,
BoxID -> "ResourceTag-Sample Data: Beta Cells-Input",
AutoDelete->True]\), "Data"]["Summary"]
Out[2]=

Visualizations (3) 

Plot the spatial point data:

In[3]:=
ListPlot[ResourceData[\!\(\*
TagBox["\"\<Sample Data: Beta Cells\>\"",
#& ,
BoxID -> "ResourceTag-Sample Data: Beta Cells-Input",
AutoDelete->True]\), "Data"]]
Out[3]=

Visualize data with categorical annotations:

In[4]:=
PointValuePlot[ResourceData[\!\(\*
TagBox["\"\<Sample Data: Beta Cells\>\"",
#& ,
BoxID -> "ResourceTag-Sample Data: Beta Cells-Input",
AutoDelete->True]\), "Data"], PlotLegends -> Automatic]
Out[4]=

Visualize data with both annotations:

In[5]:=
PointValuePlot[ResourceData[\!\(\*
TagBox["\"\<Sample Data: Beta Cells\>\"",
#& ,
BoxID -> "ResourceTag-Sample Data: Beta Cells-Input",
AutoDelete->True]\), "Data"], {1 -> "Shape", 2 -> "Size"}, PlotLegends -> Automatic]
Out[5]=

Analysis (4) 

Compute probability of finding a point within given radius of an existing point - NearestNeighborG is the CDF of the nearest neighbor distribution:

In[6]:=
nnG = NearestNeighborG[ResourceData[\!\(\*
TagBox["\"\<Sample Data: Beta Cells\>\"",
#& ,
BoxID -> "ResourceTag-Sample Data: Beta Cells-Input",
AutoDelete->True]\), "Data"]]
Out[6]=
In[7]:=
maxR = nnG["MaxRadius"]
Out[7]=
In[8]:=
DiscretePlot[nnG[r], {r, maxR/100, maxR, maxR/100}, AxesLabel -> {"radius", "probability"}]
Out[8]=

Mean distance between a typical point and its nearest neighbor (for positive support distribution can be approximated via a Riemann sum of 1-CDF):

In[9]:=
step = maxR/100;
partition = Table[{k, k + step}, {k, 0, maxR, step}];
values = nnG[Mean /@ partition];
In[10]:=
Total[(1 - values)*step]*ResourceData[\!\(\*
TagBox["\"\<Sample Data: Beta Cells\>\"",
#& ,
BoxID -> "ResourceTag-Sample Data: Beta Cells-Input",
AutoDelete->True]\), "RegionScale"]
Out[10]=

Test for complete spacial randomness:

In[11]:=
SpatialRandomnessTest[ResourceData[\!\(\*
TagBox["\"\<Sample Data: Beta Cells\>\"",
#& ,
BoxID -> "ResourceTag-Sample Data: Beta Cells-Input",
AutoDelete->True]\), "Data"], {"PValue", "TestConclusion"}]
Out[11]=

Fit a Poisson point process to data:

In[12]:=
EstimatedPointProcess[ResourceData[\!\(\*
TagBox["\"\<Sample Data: Beta Cells\>\"",
#& ,
BoxID -> "ResourceTag-Sample Data: Beta Cells-Input",
AutoDelete->True]\), "Data"], PoissonPointProcess[\[Mu], 2]]
Out[12]=

Gosia Konwerska, "Sample Data: Beta Cells" from the Wolfram Data Repository (2021)  

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