# Statistics on Object Differences User Tutorial Authors: Kedar Madi, Beatriz Paniagua, Jared Vicory Collaborators: ![](img/SlicerSALT-DifferenceStatistics-Tutorial_0.jpg) ![](img/SlicerSALT-SPHARM-PDM-Tutorial_2.png) This module allows for the computation of correlation between how shapes change between two time points and various covariates. It does this by first computing point-to-point changes between the two given timepoints, then automatically running the [Covariate Significance Testing](CovarianceTesting.md) module on the results. Computed p-values at each point on the mesh are mapped onto a given template for visualization. ## Installation on SlicerSALT Download the SlicerSALT packages for your respective operating system from the _[SlicerSALT website ](http://salt.slicer.org)_ and install it\. The module will be ready to use then\. ![](img/SlicerSALT-DifferenceStatistics-Tutorial_2.png) ![](img/SlicerSALT-DifferenceStatistics-Tutorial_3.png) ## Difference Statistics Module The module can be found in the SlicerSALT menu under `Shape Analysis` -> `Statistics on Object Differences` ![](img/SlicerSALT-DifferenceStatistics-Tutorial_4.png) ### Inputs There are three required inputs: ![](img/SlicerSALT-DifferenceStatistics-Tutorial_5.png) - Input CSV file - A CSV file where: - The first two columns, `Timepoint 1` and `Timepoint 2`, are paths to the pairs of meshes between which differences will be computed. All meshes must have the same number of points and be in correspondence (i.e., point 1 in each mesh should correspond to roughly the same anatomical location) - All following columns describe covariates which will be compared with the computed differences to determine if there is correlation. These covariates can be continuous or categorical. - An example is shown below - Template Mesh - This is the mesh onto which p-values will be mapped for visualization purposes - This is typically a sample object from the population and should have the same number of points/correspondence as the input files (and can be one of the input files) - The choice of template will not effect computation - Output Directory - The directory where output files will be stored. Outputs will include - Computed pairwise differences between timepoints - The various outputs from the [Covariate Significance Testing](CovarianceTesting.md) module Example Input CSV file: ![](img/SlicerSALT-DifferenceStatistics-Tutorial_6.png) ### Running the module and Outputs Once the inputs are set, click the `Apply` button to begin computation. First, the meshes will be loaded and differences will be computed. Depending on the number of input files, this could take several minutes. ![](img/SlicerSALT-DifferenceStatistics-Tutorial_7.png) Then, SlicerSALT will switch to the [Covariate Significance Testing](CovarianceTesting.md) module, with the inputs set automatically, to begin computing correlations. ![](img/SlicerSALT-DifferenceStatistics-Tutorial_8.png) When this is finished, SlicerSALT will change to the Shape Population Viewer module which can be used to view the p-value maps for each input covariate. The data used to color the mesh can be set under `ColorMap and Vectors` -> `Attribute` on the bottom tab. ![](img/SlicerSALT-DifferenceStatistics-Tutorial_9.png) The file with the mapped p-values is saved to out.vtk in the specified output folder. ## Acknowledgements \- Resources \- Questions