3D sample viewer

The 3D visualization in this tab is implemented with ThreeJS, a javascript library that uses webGL. If you experience any problem during the visualization update your browser and/or check that your browser supports webGL.

To visualize a synapsis sample, select it using the selector on the left side. If no data file or simulation has been created yet, please proceed to data load or simulation tab.

For each synapsis type you can set its color. After all parameters have been set you can click on the render button.


Sample selector

Plot options


Load CSV and/or XSL files

To load a synapsis data sample in CSV or XSL format, please use the file explorer on the sidebar to select the files. If no sample id or layer is provided in the file, you can set it in the general options box. For CSV files field delimiter and decimal dot options are available in the csv options box

Any error detected during the upload or the parsing phase will be displayed in this same page. Files with errors will not be uploaded into the application.


File selector

Note: CSV file must contain, at least, (x,y,z) coordinates and Feret's diameter of each synapse in nanometers, a sample ID number and its layer (in roman numbers).

Click here to see an example

General options

Process report

View data tables

The table displays all variables in the data files (one row per sample). On the left side you can select which data files to show in the table.

To export the table in CSV format, click the export button under the selector. A ZIP file containing selected data will be created.


Sample selector

Simulate synapses sample

In this tab, you can create RSA based samples based on existing models or by manually setting the simulation parameters.


Simulation options

Parameters

Process report

Model building process
Step 1:
Select model data
Step 2:
Feret's diameter
Step 3:
Spatial distribution
Step 4:
Summary functions

Create new model


The creation of a model for the spatial distribution of the synapses is a three-step process, with an additional step for computing a set of functions that describe that distribution. The four steps are:

  1. Sample selection : Samples that will be used for building the model are selected using a hierarchical checkbox selector. You can select all samples that belong to one layer, or choose them individually
  2. Feret's diameter distribution : Assuming that the Feret's diameter follows a log-normal distribution, the distribution parameters are computed based on the diameters in the selected samples
  3. Spatial distribution : Once the synapses diameters distribution has been modeled, this step tests if the spatial distribution of the selected samples follow a Random sequential adsorption process (RSA) where synapses are distributed in space almost randomly, with the only constraint that they cannot overlap, or if they are distributed completely at random (CSR)
  4. Summary functions : Finally, a set of well-known summary functions in spatital processes are computed for the model and the selected samples. There are four functions
    • F Function : The empty space function
    • G Function : The nearest-neighbor distance cumulative distribution function
    • K Function : The Ripley's function, the expected number of points within a distance of a typical point of the process
    • L Function : A common transformation of the K function that makes the plots much easier to assess visually

To begin the process, click on the Start process button on the right.

Sample selection

Samples that will be used for building the model are selected using a hierarchical checkbox selector. You can select all samples that belong to one layer, or choose them individually


Sample selector

Fit Feret's diameter distribution

Assuming that the Feret's diameter follows a log-normal distribution, the distribution parameters are computed based on the diameters in the selected samples


Controls

Fit output

Analize synapses spatial distribution

Once the synapses diameters distribution has been modeled, this step tests if the spatial distribution of the selected samples follow a Random sequential adsorption process (RSA) where synapses are distributed in space almost randomly, with the only constraint that they cannot overlap, or if they are distributed completely at random (CSR)


RSA Advanced options

RSA envelope output

Compute FGKL summary functions

Finally, a set of well-known summary functions in spatital processes are computed for the model and the selected samples. There are four functions


Process completed


To save the model, give it an identificative name and click on Save model.

Saved models can be reviewed and downloaded in the view model page, accesible on the sidebar under Modelling.

Model visualization

In this tab you can explore every model created in the application. After selecting a model, the plots and graphics correspondig to that model are displayed below.



Download model

Model summary


Plots

Feret's diameter

RSA envelope

FGKL functions

Synaptic density comparison

This analysis compares, from a statistical point of view, the synaptic density between samples from two or more layers. The results are summarized in a stacked bar plot.

To perform the test, at least 3 samples per layer are needed. As the number of samples increase, the accuracy of the statistical results increases.


Layer selector

Comparison output

Nearest synapses distance

This analysis compares the distance to the nearest synapse distribution within samples of two or more layers. A statistical test is used to perform a pairwise comparison to determine whether the distances have the same distribution for each pair of layers or not.


Layer selector

Comparison output

3D Synapses spatial analysis - Graphical User Interface

Welcome to 3DSynapsesSA, an R package for spatial analysis of synapses. The application is divided in five main sections:

  • 3D Visualization : This section provides a 3D sample visualizator of the spatial distribution of loaded and simulated synapses.
  • Data loading : In this tab the user can upload new files and export loaded and simulated data in CSV format.
  • Modelling : This tab contains a four step process to build a new RSA spatial model based on selected samples as well as a model viewer.
  • Simulation : In the simulation tab the user can generate new distributions of synapses from the models built in the previous section.
  • Layer comparison : Finally, in the layer comparison tab, the user can compare the synaptic density and the distance to the nearest synapse between layers.
In the boxes below, you can find detailed information about these four sections. If you have any question about 3DsynapsesSA, this interface or you want to report a bug, the contact information is at the end of this page.

3D visualization

3D Visualization tab

Dashboard tab The 3D visualization tab contains an interactive 3D plot of any sample in the application (real or simulated). In the controls on the left side you can find a soma selector and the render options, while the 3D plot is on the right side.
The first step is to select a sample in the selector. Once a sample has been selected you can select which synapses to plot (symmetric and/or asymmetric) and its color. After setting all the parameters, click on draw button to create or update the 3D plot.
You can move the camera in the 3D plot using your keyboard, specific controls are detailed in the link on the top-left side of the plot.

Data loading

Data load tab

Data load tab In the data load tab the user can upload file in CSV or XSL format . The controls are located on the left side. The user can select one or more files to upload using the file explorer. Sample name, layer and number can be selected in the general options tab. Each sample is automatically tested and validated. Validation results are shown in the process report tab.

Data view

Char table tab In the data view tab a table displays all variables in the data files (one row per sample). On the left side you can select which data files to show in the table. You can also download the table in CSV format. Just click the export button under the selector to download selected data in a ZIP file.

Modelling

New model tab

New model tab In this tab the user can create a new model and:
  • Select the data to build the model
  • Fit the synapses Feret's diameter
  • Compute the synaptic intensity and compare it to a RSA model
  • Plot the characteristic FGKL functions for a spatial process
  • Name and save the model

Model visualization tab

Model view tab Given a model, this tab shows detailed information about it. In the general info section some general number such as the number of instances used in the learning process, model parameters and the plots associated to the model. You can also download the model in XML format along with its plot by clicking in the download model button.

Simulation

New simulation tab

New simulation tab Simulation creates new synapses samples from a spatial RSA model. Model parameters can be setted either manually or taken from a previously created model. To do so, users selects one of the previously computed models in Model selector. Additional simulation parameters, such as the volume or the number of simulations can also be specified in the boxes on the left.

Layer comparison

Synaptic density comparison

New simulation tab The analysis performed in the tab compares the synaptic density between samples from two or more layers. The results are summarized in a stacked bar plot.

Nearest synapse distance comparison

New simulation tab The analysis performed in the tab compares the synaptic density between samples from two or more layers. The results are summarized in a stacked bar plot.

Computational Intelligence Group Universidad Polit├ęcnica de Madrid
3DSynapsesSA library and this GUI have been developed by the Computational Intelligence Group (CIG) at the Universidad Politecnica de Madrid (Spain).
Contact info:

Human Brain Project European Commision
This work has been supported by European Union's Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 604102 (Human Brain Project).