Wolfram Data Repository

Immediate Computable Access to Curated Contributed Data

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"]
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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"]
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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"]]
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Plot annotated locations:

In[4]:=
PointValuePlot[ResourceData[\!\(\* TagBox["\"\<Sample Data: Amacrine Cells\>\"", #& , BoxID -> "ResourceTag-Sample Data: Amacrine Cells-Input", AutoDelete->True]\), "Data"], PlotLegends -> Automatic]
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Analysis (6) 

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"]]
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In[6]:=
maxR = nnG["MaxRadius"]
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In[7]:=
DiscretePlot[nnG[r], {r, maxR/100, maxR, maxR/100}, AxesLabel -> {"radius", "probability"}]
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NearestNeighborG as the CDF of nearest neighbor distribution can be used to compute the mean distance between a typical point and its nearest neighbor - the mean of a positive support distribution can be approximated via a Riemann sum of 1- CDF. To use Riemann approximation create the partition of the support interval from 0 to maxR into 100 parts and compute the value of the NearestNeighborG at the middle of each subinterval:

In[8]:=
step = maxR/100; middles = Subdivide[step/2, maxR - step/2, 99]; values = nnG[middles];

Now compute the Riemann sum to find the mean distance between a typical point and its nearest neighbor:

In[9]:=
Total[(1 - values)*step]
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Account for scale and units:

In[10]:=
%*ResourceData[\!\(\* TagBox["\"\<Sample Data: Amacrine Cells\>\"", #& , BoxID -> "ResourceTag-Sample Data: Amacrine Cells-Input", AutoDelete->True]\), "RegionScale"]
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Test for complete spatial randomness:

In[11]:=
SpatialRandomnessTest[ResourceData[\!\(\* TagBox["\"\<Sample Data: Amacrine Cells\>\"", #& , BoxID -> "ResourceTag-Sample Data: Amacrine Cells-Input", AutoDelete->True]\), "Data"], {"PValue", "TestConclusion"}] // Column
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Fit a Poisson point process to data:

In[12]:=
Clear[\[Mu]]; EstimatedPointProcess[ResourceData[\!\(\* TagBox["\"\<Sample Data: Amacrine Cells\>\"", #& , BoxID -> "ResourceTag-Sample Data: Amacrine Cells-Input", AutoDelete->True]\), "Data"], PoissonPointProcess[\[Mu], 2]]
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Gosia Konwerska, "Sample Data: Amacrine Cells" from the Wolfram Data Repository (2022)  

Data Resource History

  • Date Created:

Source Metadata

  • Citation:
    • Diggle, P. J., Eglen, S. J., & Troy, J. B. (2006). Modelling the Bivariate Spatial Distribution of Amacrine Cells. In A. Baddeley, P. Gregori, J. Mateu, R. Stoica, & D. Stoyan (Eds.), Case Studies in Spatial Point Process Modeling Berlin-Heidelberg-New York: Springer-Verlag.

Publisher Information

  • Prepared for the Wolfram Data Repository By: Gosia Konwerska (Wolfram Research, Inc.)
  • Publisher of Record: Gosia Konwerska