Wolfram Research

Sample Data: Amacrine Cells

Source Notebook

Locations of amacrine cells (inhibitory interneurons in the retina) annotated with on/off marks

Details

Locations of amacrine cells in the observation region Rectangle[{0, 0}, {1.6012085, 1}]*662 microns, annotated with on/off marks.

Examples

Basic Examples (1) 

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

Summary of the spatial point data:

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

Visualizations (2) 

Plot the spatial point data:

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

Plot annotated locations:

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

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[5]:=
nnG = NearestNeighborG[ResourceData[\!\(\*
TagBox["\"\<Sample Data: Amacrine Cells\>\"",
#& ,
BoxID -> "ResourceTag-Sample Data: Amacrine Cells-Input",
AutoDelete->True]\), "Data"]]
Out[5]=
In[6]:=
maxR = nnG["MaxRadius"]
Out[6]=
In[7]:=
DiscretePlot[nnG[r], {r, maxR/100, maxR, maxR/100}, AxesLabel -> {"radius", "probability"}]
Out[7]=

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[8]:=
step = maxR/100;
partition = Table[{k, k + step}, {k, 0, maxR, step}];
values = nnG[Mean /@ partition];
In[9]:=
Total[(1 - values)*step]*ResourceData[\!\(\*
TagBox["\"\<Sample Data: Amacrine Cells\>\"",
#& ,
BoxID -> "ResourceTag-Sample Data: Amacrine Cells-Input",
AutoDelete->True]\), "RegionScale"]
Out[9]=

Test for complete spacial randomness:

In[10]:=
SpatialRandomnessTest[ResourceData[\!\(\*
TagBox["\"\<Sample Data: Amacrine Cells\>\"",
#& ,
BoxID -> "ResourceTag-Sample Data: Amacrine Cells-Input",
AutoDelete->True]\), "Data"], {"PValue", "TestConclusion"}]
Out[10]=

Fit a Poisson point process to data:

In[11]:=
EstimatedPointProcess[ResourceData[\!\(\*
TagBox["\"\<Sample Data: Amacrine Cells\>\"",
#& ,
BoxID -> "ResourceTag-Sample Data: Amacrine Cells-Input",
AutoDelete->True]\), "Data"], PoissonPointProcess[\[Mu], 2]]
Out[11]=

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

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